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Bhandari PL, Drolet BC, James AJ, Lineaweaver WC. Artificial Intelligence and Submissions to Annals of Plastic Surgery. Ann Plast Surg 2024; 92:487-488. [PMID: 38685489 DOI: 10.1097/sap.0000000000003997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Affiliation(s)
- Panambur L Bhandari
- From the Department of Plastic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
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202
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Malerbi FK, Mezzomo Ventura B, Fischer M, Penha FM. Patients Perceptions of Artificial Intelligence in a Deep Learning-Assisted Diabetic Retinopathy Screening Event: A Real-World Assessment. J Diabetes Sci Technol 2024; 18:750-751. [PMID: 38404014 DOI: 10.1177/19322968241234378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
During an artificial intelligence (AI)-assisted diabetic retinopathy screening event, we performed a survey on patients´ perceptions on AI. Respondents were individuals with diabetes, mostly followed in primary healthcare with a low education level. While 49.6% of participants said they knew what AI was, only 14% reported good or expert knowledge of AI. The vast majority reported positive feelings towards AI in healthcare. We highlight the importance of understanding patients´ views regarding AI in health in a real-life situation and emphasize the importance of digital education.
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Affiliation(s)
| | | | | | - Fernando Marcondes Penha
- Federal University of São Paulo, São Paulo, Brazil
- Universidade Regional de Blumenau, Blumenau, Brazil
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203
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Holyfield C, MacNeil S, Caldwell N, O'Neill Zimmerman T, Lorah E, Dragut E, Vucetic S. Leveraging Communication Partner Speech to Automate Augmented Input for Children on the Autism Spectrum Who Are Minimally Verbal: Prototype Development and Preliminary Efficacy Investigation. Am J Speech Lang Pathol 2024; 33:1174-1192. [PMID: 38290536 DOI: 10.1044/2023_ajslp-23-00224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
PURPOSE Augmentative and alternative communication (AAC) technology innovation is urgently needed to improve outcomes for children on the autism spectrum who are minimally verbal. One potential technology innovation is applying artificial intelligence (AI) to automate strategies such as augmented input to increase language learning opportunities while mitigating communication partner time and learning barriers. Innovation in AAC research and design methodology is also needed to empirically explore this and other applications of AI to AAC. The purpose of this report was to describe (a) the development of an AAC prototype using a design methodology new to AAC research and (b) a preliminary investigation of the efficacy of this potential new AAC capability. METHOD The prototype was developed using a Wizard-of-Oz prototyping approach that allows for initial exploration of a new technology capability without the time and effort required for full-scale development. The preliminary investigation with three children on the autism spectrum who were minimally verbal used an adapted alternating treatment design to compare the effects of a Wizard-of-Oz prototype that provided automated augmented input (i.e., pairing color photos with speech) to a standard topic display (i.e., a grid display with line drawings) on visual attention, linguistic participation, and (for one participant) word learning during a circle activity. RESULTS Preliminary investigation results were variable, but overall participants increased visual attention and linguistic participation when using the prototype. CONCLUSIONS Wizard-of-Oz prototyping could be a valuable approach to spur much needed innovation in AAC. Further research into efficacy, reliability, validity, and attitudes is required to more comprehensively evaluate the use of AI to automate augmented input in AAC.
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Affiliation(s)
- Christine Holyfield
- Department of Communication Disorders and Occupational Therapy, University of Arkansas, Fayetteville
| | - Stephen MacNeil
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA
| | - Nicolette Caldwell
- Department of Communication Disorders and Occupational Therapy, University of Arkansas, Fayetteville
| | - Tara O'Neill Zimmerman
- Department of Communication Disorders and Occupational Therapy, University of Arkansas, Fayetteville
| | - Elizabeth Lorah
- Department of Curriculum and Instruction, University of Arkansas, Fayetteville
| | - Eduard Dragut
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA
| | - Slobodan Vucetic
- Department of Computer and Information Sciences, Temple University, Philadelphia, PA
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204
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Kiraly AP, Cunningham CA, Najafi R, Nabulsi Z, Yang J, Lau C, Ledsam JR, Ye W, Ardila D, McKinney SM, Pilgrim R, Liu Y, Saito H, Shimamura Y, Etemadi M, Melnick D, Jansen S, Corrado GS, Peng L, Tse D, Shetty S, Prabhakara S, Nadich DP, Beladia N, Eswaran K. Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the United States and Japan. Radiol Artif Intell 2024; 6:e230079. [PMID: 38477661 DOI: 10.1148/ryai.230079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
Purpose To evaluate the impact of an artificial intelligence (AI) assistant for lung cancer screening on multinational clinical workflows. Materials and Methods An AI assistant for lung cancer screening was evaluated on two retrospective randomized multireader multicase studies where 627 (141 cancer-positive cases) low-dose chest CT cases were each read twice (with and without AI assistance) by experienced thoracic radiologists (six U.S.-based or six Japan-based radiologists), resulting in a total of 7524 interpretations. Positive cases were defined as those within 2 years before a pathology-confirmed lung cancer diagnosis. Negative cases were defined as those without any subsequent cancer diagnosis for at least 2 years and were enriched for a spectrum of diverse nodules. The studies measured the readers' level of suspicion (on a 0-100 scale), country-specific screening system scoring categories, and management recommendations. Evaluation metrics included the area under the receiver operating characteristic curve (AUC) for level of suspicion and sensitivity and specificity of recall recommendations. Results With AI assistance, the radiologists' AUC increased by 0.023 (0.70 to 0.72; P = .02) for the U.S. study and by 0.023 (0.93 to 0.96; P = .18) for the Japan study. Scoring system specificity for actionable findings increased 5.5% (57% to 63%; P < .001) for the U.S. study and 6.7% (23% to 30%; P < .001) for the Japan study. There was no evidence of a difference in corresponding sensitivity between unassisted and AI-assisted reads for the U.S. (67.3% to 67.5%; P = .88) and Japan (98% to 100%; P > .99) studies. Corresponding stand-alone AI AUC system performance was 0.75 (95% CI: 0.70, 0.81) and 0.88 (95% CI: 0.78, 0.97) for the U.S.- and Japan-based datasets, respectively. Conclusion The concurrent AI interface improved lung cancer screening specificity in both U.S.- and Japan-based reader studies, meriting further study in additional international screening environments. Keywords: Assistive Artificial Intelligence, Lung Cancer Screening, CT Supplemental material is available for this article. Published under a CC BY 4.0 license.
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Affiliation(s)
- Atilla P Kiraly
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Corbin A Cunningham
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Ryan Najafi
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Zaid Nabulsi
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Jie Yang
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Charles Lau
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Joseph R Ledsam
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Wenxing Ye
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Diego Ardila
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Scott M McKinney
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Rory Pilgrim
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Yun Liu
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Hiroaki Saito
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Yasuteru Shimamura
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Mozziyar Etemadi
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - David Melnick
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Sunny Jansen
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Greg S Corrado
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Lily Peng
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Daniel Tse
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Shravya Shetty
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Shruthi Prabhakara
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - David P Nadich
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Neeral Beladia
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
| | - Krish Eswaran
- From Google Health Research, 1600 Amphitheatre Pkwy, Mountain View, CA 94043 (A.P.K., C.A.C., R.N., Z.N., C.L., J.R.L., D.A., S.M.M., R.P., Y.L., S.J., G.S.C., L.P., D.T., S.S., S.P., K.E.); Waymo, Mountain View, Calif (J.Y., N.B.), David Geffen School of Medicine at UCLA, Los Angeles, Calif (C.L.); Google, Mountain View, Calif (W.Y.); Department of Gastroenterology, Sendai Kousei Hospital, Sendai, Japan (H.S.); MNES Inc, Hiroshima, Japan (Y.S.); Department of Telemedicine, Northwestern University Feinberg School of Medicine, Chicago, Ill (M.E., D.M.); and Center for Biological Imaging, New York University-Langone Medical Center, New York, NY (D.P.N.)
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Magnusson MMM, Gerk U, Schüpbach G, Rieger J, Plendl J, Marin I, Drews B, Kaessmeyer S. Microvascular changes following exposure to iodinated contrast media in vitro. A qualitative comparison to serum creatinine concentrations in post-cardiac catheterization patients. Microvasc Res 2024; 153:104659. [PMID: 38286222 DOI: 10.1016/j.mvr.2024.104659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/23/2024] [Accepted: 01/23/2024] [Indexed: 01/31/2024]
Abstract
INTRODUCTION Contrast-associated acute kidney injury (CA-AKI) is characterized as a loss of renal function following radiological contrast media administration. While all contrast media induce variable changes in microvascular endothelial cells in vitro, only few studies report clinical significance of their findings. A comprehensive assessment of the effect of iodinated contrast media on the renal function in vitro and in vivo is essential. The aim of our study was to morphometrically quantify the effect of two different contrast media (Iobitridol and Iodixanol) on vascular endothelial capillaries in vitro and to analyze their effect on the renal function of patients who underwent cardiac catheterization including the intra-arterial administration of contrast media, by measuring serum creatinine concentration (SCr), a byproduct of muscle metabolism, primarily excreted by the kidneys. Our hypothesis suggests that conducting a qualitative comparison of both outcomes will enable identification of differences and similarities between in vitro and in vivo exposure. MATERIAL AND METHODS In vitro, co-cultures of human dermal fibroblasts and human dermal microvascular endothelial cells forming capillary beds were exposed to a mixture of phosphate buffered saline and either Iobitridol, Iodixanol, or one of their supplements EDTA or Trometamol for 1.5 or 5 min. Negative control co-cultures were exposed exclusively to phosphate buffered saline. Co-cultures were either directly fixed or underwent a regeneration time of 1, 3 or 7 days. An artificial intelligence software was trained for detection of labeled endothelial capillaries (CD31) on light microscope images and measurements of morphometric parameters. In vivo, we retrospectively analyzed data from patients who underwent intra-arterial administration of contrast media and for whom SCr values were available pre- and post-contrast exposition (1, 3, and 7 days following procedure). Temporal development of SCr and incidence of CA-AKI were assessed. Both exposure types were qualitatively compared. RESULTS In vitro, Iobitridol, Iodixanol and EDTA induced a strong decrease of two morphometric parameters after 3 days of regeneration. In vivo, a significant increase of SCr and incidence of CA-AKI was observed 3 days following procedure in the post-contrast media patients. No difference was observed between groups. DISCUSSION Two of the morphometric parameters were inversely proportional to the SCr of the patients. If the endothelial damages observed in vitro occur in vivo, it may result in renal hypoxia, inducing a loss of kidney function clinically translated into an increase of SCr. Further development of our in vitro model could allow closer replication of the internal structure of a kidney and bridge the gap between in vitro studies and their clinical findings.
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Affiliation(s)
- Marine M M Magnusson
- Division of Veterinary Anatomy, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Ulrich Gerk
- Städtisches Klinikum Dresden, Dresden, Germany
| | - Gertraud Schüpbach
- Veterinary Public Health Institute, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Juliane Rieger
- Institute of Translational Medicine for Health Care Systems, Department of Human Medicine, Faculty of Medicine, MSB Medical School Berlin, Berlin, Germany
| | - Johanna Plendl
- Institute of Veterinary Anatomy, Department of Veterinary Medicine, Freie Universität Berlin, Berlin, Germany
| | - Ilka Marin
- Institute of Veterinary Anatomy, Department of Veterinary Medicine, Freie Universität Berlin, Berlin, Germany
| | - Barbara Drews
- Division of Veterinary Anatomy, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Sabine Kaessmeyer
- Division of Veterinary Anatomy, Vetsuisse Faculty, University of Bern, Bern, Switzerland.
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206
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Ly A, Garcia V, Blenman KRM, Ehinger A, Elfer K, Hanna MG, Li X, Peeters DJE, Birmingham R, Dudgeon S, Gardecki E, Gupta R, Lennerz J, Pan T, Saltz J, Wharton KA, Ehinger D, Acs B, Dequeker EMC, Salgado R, Gallas BD. Training pathologists to assess stromal tumour-infiltrating lymphocytes in breast cancer synergises efforts in clinical care and scientific research. Histopathology 2024; 84:915-923. [PMID: 38433289 PMCID: PMC10990791 DOI: 10.1111/his.15140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/15/2023] [Accepted: 12/31/2023] [Indexed: 03/05/2024]
Abstract
A growing body of research supports stromal tumour-infiltrating lymphocyte (TIL) density in breast cancer to be a robust prognostic and predicive biomarker. The gold standard for stromal TIL density quantitation in breast cancer is pathologist visual assessment using haematoxylin and eosin-stained slides. Artificial intelligence/machine-learning algorithms are in development to automate the stromal TIL scoring process, and must be validated against a reference standard such as pathologist visual assessment. Visual TIL assessment may suffer from significant interobserver variability. To improve interobserver agreement, regulatory science experts at the US Food and Drug Administration partnered with academic pathologists internationally to create a freely available online continuing medical education (CME) course to train pathologists in assessing breast cancer stromal TILs using an interactive format with expert commentary. Here we describe and provide a user guide to this CME course, whose content was designed to improve pathologist accuracy in scoring breast cancer TILs. We also suggest subsequent steps to translate knowledge into clinical practice with proficiency testing.
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Affiliation(s)
- Amy Ly
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA
| | - Victor Garcia
- Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Kim RM Blenman
- Department of Internal Medicine, Section of Medical Oncology and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
- Department of Computer Science, Yale School of Engineering and Applied Science, New Haven, CT, USA
| | - Anna Ehinger
- Department of Genetics, Pathology and Molecular Diagnostics, Laboratory Medicine, Region Skane, Lund University, Lund, Sweden
| | - Katherine Elfer
- Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Xiaoxian Li
- Department of Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA
| | - Dieter JE Peeters
- Department of Pathology, University Hospital Antwerp, Edegem, Belgium
- Department of Pathology, Algemeen Ziekenhuis (AZ) Sint-Maarten, Mechelen, Belgium
| | - Ryan Birmingham
- Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, U.S. Food and Drug Administration, Silver Spring, MD, USA
- Department of Biomedical Informatics, Emory University, Atlanta, GA, USA
| | - Sarah Dudgeon
- Center for Computational Health, Yale School of Medicine, New Haven, CT, USA
| | - Emma Gardecki
- Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Rajarsi Gupta
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Jochen Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Massachusetts General Hospital, Boston, MA, USA; currently at BostonGene, Boston, MA
| | - Tony Pan
- Department of Biomedical Informatics, Emory University, Atlanta, GA, USA
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | | | - Daniel Ehinger
- Department of Clinical Sciences, Division of Oncology, Lund University, Lund, Sweden
- Department of Genetics, Pathology, and Molecular Diagnostics, Skane University Hospital, Lund, Sweden
| | - Balazs Acs
- Department of Oncology and Pathology, Cancer Centre Karolinska, Karolinksa Institutet, Stockholm, Sweden
- Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
| | - Elisabeth MC Dequeker
- Department of Public Health and Primary Care, Biomedical Quality Assurance Research Unit, University of Leuven, Leuven, Belgium
| | - Roberto Salgado
- Department of Pathology, Gasthuiszusters Antwerpen-Ziekenhuis Netwerk Antwerpen (GZA-ZNA) Hospitals, Antwerp, Belgium
- Division of Research, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Brandon D Gallas
- Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Imaging, Diagnostics, and Software Reliability, U.S. Food and Drug Administration, Silver Spring, MD, USA
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207
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Santagata D, Donadini MP, Ageno W. Use of artificial intelligence and radiomics for risk stratification in patients with pulmonary embolism: New tools for an old problem. Eur J Clin Invest 2024; 54:e14171. [PMID: 38265096 DOI: 10.1111/eci.14171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/13/2024] [Accepted: 01/13/2024] [Indexed: 01/25/2024]
Affiliation(s)
- Davide Santagata
- Department of Medicine and Surgery, Research center on Thrombosis and Antithrombotic Therapies, University of Insubria, Varese, Italy
| | - Marco Paolo Donadini
- Department of Medicine and Surgery, Research center on Thrombosis and Antithrombotic Therapies, University of Insubria, Varese, Italy
| | - Walter Ageno
- Department of Medicine and Surgery, Research center on Thrombosis and Antithrombotic Therapies, University of Insubria, Varese, Italy
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Jackson S, Freeman R, Noronha A, Jamil H, Chavez E, Carmichael J, Ruiz KM, Miller C, Benke S, Perrot R, Hockley M, Murphy K, Casillan A, Radanovich L, Deforest R, Nunes ME, Galarreta-Aima C, Sidlow R, Einhorn Y, Woods J. Applying data science methodologies with artificial intelligence variant reinterpretation to map and estimate genetic disorder prevalence utilizing clinical data. Am J Med Genet A 2024; 194:e63505. [PMID: 38168469 DOI: 10.1002/ajmg.a.63505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/28/2023] [Accepted: 12/02/2023] [Indexed: 01/05/2024]
Abstract
Data science methodologies can be utilized to ascertain and analyze clinical genetic data that is often unstructured and rarely used outside of patient encounters. Genetic variants from all genetic testing resulting to a large pediatric healthcare system for a 5-year period were obtained and reinterpreted utilizing the previously validated Franklin© Artificial Intelligence (AI). Using PowerBI©, the data were further matched to patients in the electronic healthcare record to associate with demographic data to generate a variant data table and mapped by ZIP codes. Three thousand and sixty-five variants were identified and 98% were matched to patients with geographic data. Franklin© changed the interpretation for 24% of variants. One hundred and fifty-six clinically actionable variant reinterpretations were made. A total of 739 Mendelian genetic disorders were identified with disorder prevalence estimation. Mapping of variants demonstrated hot-spots for pathogenic genetic variation such as PEX6-associated Zellweger Spectrum Disorder. Seven patients were identified with Bardet-Biedl syndrome and seven patients with Rett syndrome amenable to newly FDA-approved therapeutics. Utilizing readily available software we developed a database and Exploratory Data Analysis (EDA) methodology enabling us to systematically reinterpret variants, estimate variant prevalence, identify conditions amenable to new treatments, and localize geographies enriched for pathogenic variants.
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Affiliation(s)
| | - Rebecca Freeman
- Valley Children's Hospital, Madera, California, USA
- UCSF Benioff Children's Hospital Oakland, Oakland, California, USA
| | | | - Hafsah Jamil
- Valley Children's Hospital, Madera, California, USA
| | - Eric Chavez
- Valley Children's Hospital, Madera, California, USA
| | | | | | | | - Sarah Benke
- Valley Children's Hospital, Madera, California, USA
| | | | | | - Kady Murphy
- Valley Children's Hospital, Madera, California, USA
| | | | | | | | - Mark E Nunes
- Valley Children's Hospital, Madera, California, USA
| | | | | | | | - Jeremy Woods
- Valley Children's Hospital, Madera, California, USA
- Stanford University, Palo Alto, California, USA
- Eureka Institute for Translational Medicine, Siracusa, Italy
- Translation Science Foundation, Fresno, California, USA
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209
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Ardoin T, Sueur C. Automatic identification of stone-handling behaviour in Japanese macaques using LabGym artificial intelligence. Primates 2024; 65:159-172. [PMID: 38520479 DOI: 10.1007/s10329-024-01123-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/22/2024] [Indexed: 03/25/2024]
Abstract
The latest advances in artificial intelligence technology have opened doors to the video analysis of complex behaviours. In light of this, ethologists are actively exploring the potential of these innovations to streamline the time-intensive behavioural analysis process using video data. Several tools have been developed for this purpose in primatology in the past decade. Nonetheless, each tool grapples with technical constraints. To address these limitations, we have established a comprehensive protocol designed to harness the capabilities of a cutting-edge artificial intelligence-assisted software, LabGym. The primary objective of this study was to evaluate the suitability of LabGym for the analysis of primate behaviour, focusing on Japanese macaques as our model subjects. First, we developed a model that accurately detects Japanese macaques, allowing us to analyse their actions using LabGym. Our behavioural analysis model succeeded in recognising stone-handling-like behaviours on video. However, the absence of quantitative data within the specified time frame limits the ability of our study to draw definitive conclusions regarding the quality of the behavioural analysis. Nevertheless, to the best of our knowledge, this study represents the first instance of applying the LabGym tool specifically for the analysis of primate behaviours, with our model focusing on the automated recognition and categorisation of specific behaviours in Japanese macaques. It lays the groundwork for future research in this promising field to complexify our model using the latest version of LabGym and associated tools, such as multi-class detection and interactive behaviour analysis.
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Affiliation(s)
- Théo Ardoin
- Master Biodiversité Ecologie Et Evolution, Université Paris-Saclay, Orsay, France
- Magistère de Biologie, Université Paris-Saclay, Orsay, France
| | - Cédric Sueur
- Université de Strasbourg, IPHC UMR7178, CNRS, Strasbourg, France.
- ANTHROPO-LAB, ETHICS EA 7446, Université Catholique de Lille, Lille, France.
- Institut Universitaire de France, Paris, France.
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210
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Wang X, Zhang R, Zhao B, Yao Y, Zhao H, Zhu X. Medical knowledge graph completion via fusion of entity description and type information. Artif Intell Med 2024; 151:102848. [PMID: 38658132 DOI: 10.1016/j.artmed.2024.102848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 02/05/2024] [Accepted: 03/19/2024] [Indexed: 04/26/2024]
Abstract
Medical Knowledge Graphs (MKGs) are vital in propelling big data technologies in healthcare and facilitating the realization of medical intelligence. However, large-scale MKGs often exhibit characteristics of data sparsity and missing facts. Following the latest advances, knowledge embedding addresses these problems by performing knowledge graph completion. Most knowledge embedding algorithms rely solely on triplet structural information, overlooking the rich information hidden within entity property sets, leading to bottlenecks in performance enhancement when dealing with the intricate relations of MKGs. Inspired by the semantic sensitivity and explicit type constraints unique to the medical domain, we propose BioBERT-based graph embedding model. This model represents an evolvable framework that integrates graph embedding, language embedding, and type information, thereby optimizing the utility of MKGs. Our study utilizes not only WordNet as a benchmark dataset but also incorporates MedicalKG to compare and corroborate the specificity of medical knowledge. Experimental results on these datasets indicate that the proposed fusion framework achieves state-of-art (SOTA) performance compared to other baselines. We believe that this incremental improvement provides promising insights for future medical knowledge graph completion endeavors.
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Affiliation(s)
- Xiaochen Wang
- Department of Information Management, Beijing Jiaotong University, Beijing, 100044, China
| | - Runtong Zhang
- Department of Information Management, Beijing Jiaotong University, Beijing, 100044, China.
| | - Butian Zhao
- Department of Information Management, Beijing Jiaotong University, Beijing, 100044, China
| | - Yuhan Yao
- Department of Finance, Imperial College London, SW7 2AZ, United Kingdom
| | - Hongmei Zhao
- Peking University People's Hospital, Beijing, 100044, China
| | - Xiaomin Zhu
- Department of Industrial Engineering, Beijing Jiaotong University, Beijing, 100044, China
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211
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G S, Gopalakrishnan U, Parthinarupothi RK, Madathil T. Deep learning supported echocardiogram analysis: A comprehensive review. Artif Intell Med 2024; 151:102866. [PMID: 38593684 DOI: 10.1016/j.artmed.2024.102866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 03/20/2024] [Accepted: 03/30/2024] [Indexed: 04/11/2024]
Abstract
An echocardiogram is a sophisticated ultrasound imaging technique employed to diagnose heart conditions. The transthoracic echocardiogram, one of the most prevalent types, is instrumental in evaluating significant cardiac diseases. However, interpreting its results heavily relies on the clinician's expertise. In this context, artificial intelligence has emerged as a vital tool for helping clinicians. This study critically analyzes key state-of-the-art research that uses deep learning techniques to automate transthoracic echocardiogram analysis and support clinical judgments. We have systematically organized and categorized articles that proffer solutions for view classification, enhancement of image quality and dataset, segmentation and identification of cardiac structures, detection of cardiac function abnormalities, and quantification of cardiac functions. We compared the performance of various deep learning approaches within each category, identifying the most promising methods. Additionally, we highlight limitations in current research and explore promising avenues for future exploration. These include addressing generalizability issues, incorporating novel AI approaches, and tackling the analysis of rare cardiac diseases.
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Affiliation(s)
- Sanjeevi G
- Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri, India
| | - Uma Gopalakrishnan
- Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri, India.
| | | | - Thushara Madathil
- Department of Cardiac Anesthesiology, Amrita Institute of Medical Sciences and Research Center, Kochi, India
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212
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Serrano RA, Smeltz AM. The Promise of Artificial Intelligence-Assisted Point-of-Care Ultrasonography in Perioperative Care. J Cardiothorac Vasc Anesth 2024; 38:1244-1250. [PMID: 38402063 DOI: 10.1053/j.jvca.2024.01.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 01/29/2024] [Indexed: 02/26/2024]
Abstract
The role of point-of-care ultrasonography in the perioperative setting has expanded rapidly over recent years. Revolutionizing this technology further is integrating artificial intelligence to assist clinicians in optimizing images, identifying anomalies, performing automated measurements and calculations, and facilitating diagnoses. Artificial intelligence can increase point-of-care ultrasonography efficiency and accuracy, making it an even more valuable point-of-care tool. Given this topic's importance and ever-changing landscape, this review discusses the latest trends to serve as an introduction and update in this area.
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Affiliation(s)
| | - Alan M Smeltz
- University of North Carolina School of Medicine, Chapel Hill, NC
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213
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Lee JH, Song G, Lee J, Kang S, Moon KM, Choi Y, Shen J, Noh M, Yang D. Prediction of immunochemotherapy response for diffuse large B-cell lymphoma using artificial intelligence digital pathology. J Pathol Clin Res 2024; 10:e12370. [PMID: 38584594 PMCID: PMC10999948 DOI: 10.1002/2056-4538.12370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/13/2024] [Accepted: 03/04/2024] [Indexed: 04/09/2024]
Abstract
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous and prevalent subtype of aggressive non-Hodgkin lymphoma that poses diagnostic and prognostic challenges, particularly in predicting drug responsiveness. In this study, we used digital pathology and deep learning to predict responses to immunochemotherapy in patients with DLBCL. We retrospectively collected 251 slide images from 216 DLBCL patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP), with their immunochemotherapy response labels. The digital pathology images were processed using contrastive learning for feature extraction. A multi-modal prediction model was developed by integrating clinical data and pathology image features. Knowledge distillation was employed to mitigate overfitting on gigapixel histopathology images to create a model that predicts responses based solely on pathology images. Based on the importance derived from the attention mechanism of the model, we extracted histological features that were considered key textures associated with drug responsiveness. The multi-modal prediction model achieved an impressive area under the ROC curve of 0.856, demonstrating significant associations with clinical variables such as Ann Arbor stage, International Prognostic Index, and bulky disease. Survival analyses indicated their effectiveness in predicting relapse-free survival. External validation using TCGA datasets supported the model's ability to predict survival differences. Additionally, pathology-based predictions show promise as independent prognostic indicators. Histopathological analysis identified centroblastic and immunoblastic features to be associated with treatment response, aligning with previous morphological classifications and highlighting the objectivity and reproducibility of artificial intelligence-based diagnosis. This study introduces a novel approach that combines digital pathology and clinical data to predict the response to immunochemotherapy in patients with DLBCL. This model shows great promise as a diagnostic and prognostic tool for clinical management of DLBCL. Further research and genomic data integration hold the potential to enhance its impact on clinical practice, ultimately improving patient outcomes.
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Affiliation(s)
- Jeong Hoon Lee
- Department of RadiologyStanford University School of MedicineStanfordCAUSA
| | - Ga‐Young Song
- Department of Hematology‐OncologyChonnam National University Hwasun HospitalHwasunRepublic of Korea
| | - Jonghyun Lee
- Department of Medical and Digital EngineeringHanyang University College of EngineeringSeoulRepublic of Korea
| | - Sae‐Ryung Kang
- Department of Nuclear MedicineChonnam National University Hwasun Hospital and Medical SchoolHwasun‐gunRepublic of Korea
| | - Kyoung Min Moon
- Division of Pulmonary and Allergy Medicine, Department of Internal MedicineChung‐Ang University Hospital, Chung‐Ang University College of MedicineSeoulRepublic of Korea
- Artificial Intelligence, Ziovision Co., Ltd.ChuncheonRepublic of Korea
| | - Yoo‐Duk Choi
- Department of PathologyChonnam National University Medical SchoolGwangjuRepublic of Korea
| | - Jeanne Shen
- Department of Pathology and Center for Artificial Intelligence in Medicine & ImagingStanford University School of MedicineStanfordCAUSA
| | - Myung‐Giun Noh
- Department of PathologyChonnam National University Medical SchoolGwangjuRepublic of Korea
- Department of PathologySchool of Medicine, Ajou UniversitySuwonRepublic of Korea
| | - Deok‐Hwan Yang
- Department of Hematology‐OncologyChonnam National University Hwasun HospitalHwasunRepublic of Korea
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Hoppe BF, Rueckel J, Dikhtyar Y, Heimer M, Fink N, Sabel BO, Ricke J, Rudolph J, Cyran CC. Implementing Artificial Intelligence for Emergency Radiology Impacts Physicians' Knowledge and Perception: A Prospective Pre- and Post-Analysis. Invest Radiol 2024; 59:404-412. [PMID: 37843828 DOI: 10.1097/rli.0000000000001034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
PURPOSE The aim of this study was to evaluate the impact of implementing an artificial intelligence (AI) solution for emergency radiology into clinical routine on physicians' perception and knowledge. MATERIALS AND METHODS A prospective interventional survey was performed pre-implementation and 3 months post-implementation of an AI algorithm for fracture detection on radiographs in late 2022. Radiologists and traumatologists were asked about their knowledge and perception of AI on a 7-point Likert scale (-3, "strongly disagree"; +3, "strongly agree"). Self-generated identification codes allowed matching the same individuals pre-intervention and post-intervention, and using Wilcoxon signed rank test for paired data. RESULTS A total of 47/71 matched participants completed both surveys (66% follow-up rate) and were eligible for analysis (34 radiologists [72%], 13 traumatologists [28%], 15 women [32%]; mean age, 34.8 ± 7.8 years). Postintervention, there was an increase that AI "reduced missed findings" (1.28 [pre] vs 1.94 [post], P = 0.003) and made readers "safer" (1.21 vs 1.64, P = 0.048), but not "faster" (0.98 vs 1.21, P = 0.261). There was a rising disagreement that AI could "replace the radiological report" (-2.04 vs -2.34, P = 0.038), as well as an increase in self-reported knowledge about "clinical AI," its "chances," and its "risks" (0.40 vs 1.00, 1.21 vs 1.70, and 0.96 vs 1.34; all P 's ≤ 0.028). Radiologists used AI results more frequently than traumatologists ( P < 0.001) and rated benefits higher (all P 's ≤ 0.038), whereas senior physicians were less likely to use AI or endorse its benefits (negative correlation with age, -0.35 to 0.30; all P 's ≤ 0.046). CONCLUSIONS Implementing AI for emergency radiology into clinical routine has an educative aspect and underlines the concept of AI as a "second reader," to support and not replace physicians.
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Affiliation(s)
- Boj Friedrich Hoppe
- From the Department of Radiology, University Hospital, LMU Munich, Munich, Germany (B.F.J., J.Rueckel, Y.D., M.H., N.F., B.O.S., J.Ricke, J.Rudolph, C.C.C.); and Institute of Neuroradiology, University Hospital, LMU Munich, Munich, Germany (J.R.)
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Kim MJ, Admane S, Chang YK, Shih KSK, Reddy A, Tang M, Cruz MDL, Taylor TP, Bruera E, Hui D. Chatbot Performance in Defining and Differentiating Palliative Care, Supportive Care, Hospice Care. J Pain Symptom Manage 2024; 67:e381-e391. [PMID: 38219964 DOI: 10.1016/j.jpainsymman.2024.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/22/2023] [Accepted: 01/03/2024] [Indexed: 01/16/2024]
Abstract
CONTEXT Artificial intelligence (AI) chatbot platforms are increasingly used by patients as sources of information. However, there is limited data on the performance of these platforms, especially regarding palliative care terms. OBJECTIVES We evaluated the accuracy, comprehensiveness, reliability, and readability of three AI platforms in defining and differentiating "palliative care," "supportive care," and "hospice care." METHODS We asked ChatGPT, Microsoft Bing Chat, Google Bard to define and differentiate "palliative care," "supportive care," and "hospice care" and provide three references. Outputs were randomized and assessed by six blinded palliative care physicians using 0-10 scales (10 = best) for accuracy, comprehensiveness, and reliability. Readability was assessed using Flesch Kincaid Grade Level and Flesch Reading Ease scores. RESULTS The mean (SD) accuracy scores for ChatGPT, Bard, and Bing Chat were 9.1 (1.3), 8.7 (1.5), and 8.2 (1.7), respectively; for comprehensiveness, the scores for the three platforms were 8.7 (1.5), 8.1 (1.9), and 5.6 (2.0), respectively; for reliability, the scores were 6.3 (2.5), 3.2 (3.1), and 7.1 (2.4), respectively. Despite generally high accuracy, we identified some major errors (e.g., Bard stated that supportive care had "the goal of prolonging life or even achieving a cure"). We found several major omissions, particularly with Bing Chat (e.g., no mention of interdisciplinary teams in palliative care or hospice care). References were often unreliable. Readability scores did not meet recommended levels for patient educational materials. CONCLUSION We identified important concerns regarding the accuracy, comprehensiveness, reliability, and readability of outputs from AI platforms. Further research is needed to improve their performance.
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Affiliation(s)
- Min Ji Kim
- Department of Palliative Care (M.J.K., S.A., Y.K.C., A.R., M.T., E.B., D.H.), Rehabilitation, and Integrative Medicine, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
| | - Sonal Admane
- Department of Palliative Care (M.J.K., S.A., Y.K.C., A.R., M.T., E.B., D.H.), Rehabilitation, and Integrative Medicine, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Yuchieh Kathryn Chang
- Department of Palliative Care (M.J.K., S.A., Y.K.C., A.R., M.T., E.B., D.H.), Rehabilitation, and Integrative Medicine, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | | | - Akhila Reddy
- Department of Palliative Care (M.J.K., S.A., Y.K.C., A.R., M.T., E.B., D.H.), Rehabilitation, and Integrative Medicine, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Michael Tang
- Department of Palliative Care (M.J.K., S.A., Y.K.C., A.R., M.T., E.B., D.H.), Rehabilitation, and Integrative Medicine, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Maxine De La Cruz
- Beth Israel Deaconess Medical Center, Harvard Medical School (M.C.), Boston, Massachusetts, USA
| | - Terry Pham Taylor
- Department of Hospital Medicine, University of Texas MD Anderson Cancer Center (T.P.T.), Houston, Texas, USA
| | - Eduardo Bruera
- Department of Palliative Care (M.J.K., S.A., Y.K.C., A.R., M.T., E.B., D.H.), Rehabilitation, and Integrative Medicine, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - David Hui
- Department of Palliative Care (M.J.K., S.A., Y.K.C., A.R., M.T., E.B., D.H.), Rehabilitation, and Integrative Medicine, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Mishra S, Corro-Flores M, Krum D, Forouzesh N. Molecular Docking Improved with Human Spatial Perception Using Virtual Reality. IEEE Trans Vis Comput Graph 2024; 30:2269-2275. [PMID: 38451773 DOI: 10.1109/tvcg.2024.3372128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
Adaptive steered molecular dynamics (ASMD) is a computational biophysics method in which an external force is applied to a selected set of atoms or a specific reaction coordinate to induce a particular molecular motion. Virtual reality (VR) based methods for protein-ligand docking are beneficial for visualizing on-the-fly interactive molecular dynamics and performing promising docking trajectories. In this paper, we propose a novel method to guide ASMD with optimal trajectories collected from human experiences using interactive molecular dynamics in virtual reality (iMD-VR). We also explain the benefits of using VR as a tool for expediting the process of ligand binding, outlining an experimental protocol that enables iMD-VR users to guide Amprenavir into and out of the binding pockets of HIV-1 protease and recreate their respective crystallographic binding poses within 5 minutes. Later, we discuss our analysis of the results from iMD-VR-assisted ASMD simulation and assess its performance compared to a standard ASMD simulation. From the accuracy point of view, our proposed method calculates higher Potential Mean Force (PMF) values consistently relative to a standard ASMD simulation with an almost twofold increase in all the experiments. Finally, we describe the novelty of the research and discuss results showcasing a faster and more effective convergence of the ligand to the protein's binding site as compared to a standard molecular dynamics simulation, proving the effectiveness of VR in the field of drug discovery. Future work includes the development of an artificial intelligence algorithm capable of predicting optimal binding trajectories for many protein-ligand pairs, as well as the required force needed to steer the ligand to follow the said trajectory.
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Gimeno-García AZ, Alayón-Miranda S, Benítez-Zafra F, Hernández-Negrín D, Nicolás-Pérez D, Pérez Cabañas C, Delgado R, Del-Castillo R, Romero A, Adrián Z, Cubas A, González-Méndez Y, Jiménez A, Navarro-Dávila MA, Hernández-Guerra M. Design and validation of an artificial intelligence system to detect the quality of colon cleansing before colonoscopy. Gastroenterol Hepatol 2024; 47:481-490. [PMID: 38154552 DOI: 10.1016/j.gastrohep.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 12/17/2023] [Accepted: 12/18/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND AND AIMS Patients' perception of their bowel cleansing quality may guide rescue cleansing strategies before colonoscopy. The main aim of this study was to train and validate a convolutional neural network (CNN) for classifying rectal effluent during bowel preparation intake as "adequate" or "inadequate" cleansing before colonoscopy. PATIENTS AND METHODS Patients referred for outpatient colonoscopy were asked to provide images of their rectal effluent during the bowel preparation process. The images were categorized as adequate or inadequate cleansing based on a predefined 4-picture quality scale. A total of 1203 images were collected from 660 patients. The initial dataset (799 images), was split into a training set (80%) and a validation set (20%). The second dataset (404 images) was used to develop a second test of the CNN accuracy. Afterward, CNN prediction was prospectively compared with the Boston Bowel Preparation Scale (BBPS) in 200 additional patients who provided a picture of their last rectal effluent. RESULTS On the initial dataset, a global accuracy of 97.49%, a sensitivity of 98.17% and a specificity of 96.66% were obtained using the CNN model. On the second dataset, an accuracy of 95%, a sensitivity of 99.60% and a specificity of 87.41% were obtained. The results from the CNN model were significantly associated with those from the BBPS (P<0.001), and 77.78% of the patients with poor bowel preparation were correctly classified. CONCLUSION The designed CNN is capable of classifying "adequate cleansing" and "inadequate cleansing" images with high accuracy.
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Affiliation(s)
- Antonio Z Gimeno-García
- Gastroenterology Department, Hospital Universitario de Canarias, Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, Tenerife, Spain.
| | - Silvia Alayón-Miranda
- Department of Computer Science and Systems Engineering, Universidad de La Laguna, Tenerife, Spain
| | - Federica Benítez-Zafra
- Gastroenterology Department, Hospital Universitario de Canarias, Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, Tenerife, Spain
| | - Domingo Hernández-Negrín
- Gastroenterology Department, Hospital Universitario de Canarias, Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, Tenerife, Spain
| | - David Nicolás-Pérez
- Gastroenterology Department, Hospital Universitario de Canarias, Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, Tenerife, Spain
| | - Claudia Pérez Cabañas
- Gastroenterology Department, Hospital Universitario de Canarias, Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, Tenerife, Spain
| | - Rosa Delgado
- Gastroenterology Department, Hospital Universitario de Canarias, Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, Tenerife, Spain
| | - Rocío Del-Castillo
- Gastroenterology Department, Hospital Universitario de Canarias, Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, Tenerife, Spain
| | - Ana Romero
- Gastroenterology Department, Hospital Universitario de Canarias, Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, Tenerife, Spain
| | - Zaida Adrián
- Gastroenterology Department, Hospital Universitario de Canarias, Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, Tenerife, Spain
| | - Ana Cubas
- Gastroenterology Department, Hospital Universitario de Canarias, Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, Tenerife, Spain
| | - Yanira González-Méndez
- Gastroenterology Department, Hospital Universitario de Canarias, Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, Tenerife, Spain
| | | | | | - Manuel Hernández-Guerra
- Gastroenterology Department, Hospital Universitario de Canarias, Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, Tenerife, Spain
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Roberts LW. Addressing the Novel Implications of Generative AI for Academic Publishing, Education, and Research. Acad Med 2024; 99:471-473. [PMID: 38451086 DOI: 10.1097/acm.0000000000005667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
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Lu A, Chen CC, Lin C, Wu TJ, Lin SH. Artificial Intelligence Electrocardiography Detecting Thyrotoxic Periodic Paralysis Following a SARS-CoV-2 Infection. Am J Med 2024; 137:e91-e93. [PMID: 38280555 DOI: 10.1016/j.amjmed.2024.01.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/09/2024] [Accepted: 01/22/2024] [Indexed: 01/29/2024]
Affiliation(s)
- Ang Lu
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Chien-Chou Chen
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan; Department of Internal Medicine, Tri-Service General Hospital Songshan Branch, National Defense Medical Center, Taipei, Taiwan
| | - Chin Lin
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan; Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan; School of Public Health, National Defense Medical Center, Taipei, Taiwan
| | - Tsung-Jui Wu
- Department of Internal Medicine, Hualien Armed Forces General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Shih-Hua Lin
- Division of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
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220
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Jokar M, Abdous A, Rahmanian V. AI chatbots in pet health care: Opportunities and challenges for owners. Vet Med Sci 2024; 10:e1464. [PMID: 38678576 PMCID: PMC11056198 DOI: 10.1002/vms3.1464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 04/04/2024] [Indexed: 05/01/2024] Open
Abstract
The integration of artificial intelligence (AI) into health care has seen remarkable advancements, with applications extending to animal health. This article explores the potential benefits and challenges associated with employing AI chatbots as tools for pet health care. Focusing on ChatGPT, a prominent language model, the authors elucidate its capabilities and its potential impact on pet owners' decision-making processes. AI chatbots offer pet owners access to extensive information on animal health, research studies and diagnostic options, providing a cost-effective and convenient alternative to traditional veterinary consultations. The fate of a case involving a Border Collie named Sassy demonstrates the potential benefits of AI in veterinary medicine. In this instance, ChatGPT played a pivotal role in suggesting a diagnosis that led to successful treatment, showcasing the potential of AI chatbots as valuable tools in complex cases. However, concerns arise regarding pet owners relying solely on AI chatbots for medical advice, potentially resulting in misdiagnosis, inappropriate treatment and delayed professional intervention. We emphasize the need for a balanced approach, positioning AI chatbots as supplementary tools rather than substitutes for licensed veterinarians. To mitigate risks, the article proposes strategies such as educating pet owners on AI chatbots' limitations, implementing regulations to guide AI chatbot companies and fostering collaboration between AI chatbots and veterinarians. The intricate web of responsibilities in this dynamic landscape underscores the importance of government regulations, the educational role of AI chatbots and the symbiotic relationship between AI technology and veterinary expertise. In conclusion, while AI chatbots hold immense promise in transforming pet health care, cautious and informed usage is crucial. By promoting awareness, establishing regulations and fostering collaboration, the article advocates for a responsible integration of AI chatbots to ensure optimal care for pets.
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Affiliation(s)
- Mohammad Jokar
- Faculty of Veterinary MedicineKaraj BranchIslamic Azad UniversityKarajIran
| | - Arman Abdous
- Faculty of Veterinary MedicineKaraj BranchIslamic Azad UniversityKarajIran
| | - Vahid Rahmanian
- Department of Public HealthTorbat Jam Faculty of Medical SciencesTorbat JamIran
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221
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Qiu S, Chen J, Wu T, Li L, Wang G, Wu H, Song X, Liu X, Wang H. CAR-Toner: an AI-driven approach for CAR tonic signaling prediction and optimization. Cell Res 2024; 34:386-388. [PMID: 38351128 PMCID: PMC11061301 DOI: 10.1038/s41422-024-00936-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/24/2024] [Indexed: 05/02/2024] Open
Affiliation(s)
- Shizhen Qiu
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- Department of Hematology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jian Chen
- ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Tao Wu
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Li Li
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Gang Wang
- Jiangsu Center for the Collaboration and Innovation of Cancer Biotherapy, Cancer Institute, Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Haitao Wu
- ENT Institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Xianmin Song
- Department of Hematology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xuesong Liu
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Haopeng Wang
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
- State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.
- Shanghai Clinical Research and Trial Center, Shanghai, China.
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Goglia M, Pace M, Yusef M, Gallo G, Pavone M, Petrucciani N, Aurello P. Artificial Intelligence and ChatGPT in Abdominopelvic Surgery: A Systematic Review of Applications and Impact. In Vivo 2024; 38:1009-1015. [PMID: 38688653 PMCID: PMC11059919 DOI: 10.21873/invivo.13534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/27/2024] [Accepted: 03/11/2024] [Indexed: 05/02/2024]
Abstract
BACKGROUND/AIM The integration of AI and natural language processing technologies, such as ChatGPT, into surgical practice has shown promising potential in enhancing various aspects of abdominopelvic surgical procedures. This systematic review aims to comprehensively evaluate the current state of research on the applications and impact of artificial intelligence (AI) and ChatGPT in abdominopelvic surgery summarizing existing literature towards providing a comprehensive overview of the diverse applications, effectiveness, challenges, and future directions of these innovative technologies. MATERIALS AND METHODS A systematic search of major electronic databases, including PubMed, Google Scholar, Cochrane Library, Web of Science, was conducted from October to November 2023, to identify relevant studies. Inclusion criteria encompassed studies that investigated the utilization of AI and ChatGPT in abdominopelvic surgical settings, including, but not limited to preoperative planning, intraoperative decision-making, postoperative care, and patient communication. RESULTS Fourteen studies met the inclusion criteria and were included in this review. The majority of the studies were analysing ChatGPT's data output and decision making while two studies reported patient and general surgery resident perception of the tool applied to clinical practice. Most studies reported a high accuracy of ChatGPT in data output and decision-making process, however with an unforgettable number of errors. CONCLUSION This systematic review contributes to the current understanding of the role of AI and ChatGPT in abdominopelvic surgery, providing insight into their applications and impact on clinical practice. The synthesis of available evidence will inform future research directions, clinical guidelines, and development of these technologies to optimize their potential benefits in enhancing surgical care within the abdominopelvic domain.
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Affiliation(s)
- Marta Goglia
- Department of Medical and Surgical Sciences and Translational Medicine, School in Translational Medicine and Oncology, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy
- IHU Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France
- IRCAD, Research Institute Against Digestive Cancer, Strasbourg, France
| | - Marco Pace
- Department of Medical and Surgical Sciences and Translational Medicine, School in Translational Medicine and Oncology, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy;
| | - Marco Yusef
- Department of Medical and Surgical Sciences and Translational Medicine, School in Translational Medicine and Oncology, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy
| | - Gaetano Gallo
- Department of Surgery, Sapienza University of Rome, Rome, Italy
| | - Matteo Pavone
- Dipartimento di Scienze per la Salute della Donna e del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli, IRCCS, UOC Ginecologia Oncologica, Rome, Italy
| | - Niccolò Petrucciani
- Department of Medical and Surgical Sciences and Translational Medicine, School in Translational Medicine and Oncology, Faculty of Medicine and Psychology, Sapienza University of Rome, Rome, Italy
| | - Paolo Aurello
- Department of Surgery, Sapienza University of Rome, Rome, Italy
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Kim M, Huh S, Park HJ, Cho SH, Lee MY, Jo S, Jung YS. Surface-functionalized SERS platform for deep learning-assisted diagnosis of Alzheimer's disease. Biosens Bioelectron 2024; 251:116128. [PMID: 38367567 DOI: 10.1016/j.bios.2024.116128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/16/2023] [Accepted: 02/11/2024] [Indexed: 02/19/2024]
Abstract
Early diagnosis of Alzheimer's disease is crucial to stall the deterioration of brain function, but conventional diagnostic methods require complicated analytical procedures or inflict acute pain on the patient. Then, label-free Surface-enhanced Raman spectroscopy (SERS) analysis of blood-based biomarkers is a convenient alternative to rapidly obtain spectral information from biofluids. However, despite the rapid acquisition of spectral information from biofluids, it is challenging to distinguish spectral features of biomarkers due to interference from biofluidic components. Here, we introduce a deep learning-assisted, SERS-based platform for separate analysis of blood-based amyloid β (1-42) and metabolites, enabling the diagnosis of Alzheimer's disease. SERS substrates consisting of Au nanowire arrays are fabricated and functionalized in two characteristic ways to compare the validity of different Alzheimer's disease biomarkers measured on our SERS system. The 6E10 antibody is immobilized for the capture of amyloid β (1-42) and analysis of its oligomerization process, while various self-assembled monolayers are attached for different dipole interactions with blood-based metabolites. Ultimately, SERS spectra of blood plasma of Alzheimer's disease patients and human controls are measured on the substrates and classified via advanced deep learning techniques that automatically extract informative features to learn generalizable representations. Accuracies up to 99.5% are achieved for metabolite-based analyses, which are verified with an explainable artificial intelligence technique that identifies key spectral features used for classification and for deducing significant biomarkers.
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Affiliation(s)
- Minjoon Kim
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Sejoon Huh
- School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hyung Joon Park
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Seunghee H Cho
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Min-Young Lee
- Department of Nano-Bio Convergence, Surface Materials Division, Korea Institute of Materials Science (KIMS), Changwon-si, Gyeongsangnam-do, 51508, Republic of Korea.
| | - Sungho Jo
- School of Computing, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
| | - Yeon Sik Jung
- Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
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Sadik M, Barrington SF, Trägårdh E, Saboury B, Nielsen AL, Jakobsen AL, Gongora JLL, Urdaneta JL, Kumar R, Edenbrandt L. Metabolic tumour volume in Hodgkin lymphoma-A comparison between manual and AI-based analysis. Clin Physiol Funct Imaging 2024; 44:220-227. [PMID: 38011940 DOI: 10.1111/cpf.12868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 11/10/2023] [Accepted: 11/24/2023] [Indexed: 11/29/2023]
Abstract
AIM To compare total metabolic tumour volume (tMTV), calculated using two artificial intelligence (AI)-based tools, with manual segmentation by specialists as the reference. METHODS Forty-eight consecutive Hodgkin lymphoma (HL) patients staged with [18F] fluorodeoxyglucose positron emission tomography/computed tomography were included. The median age was 35 years (range: 7-75), 46% female. The tMTV was automatically measured using the AI-based tools positron emission tomography assisted reporting system (PARS) (from Siemens) and RECOMIA (recomia.org) without any manual adjustments. A group of eight nuclear medicine specialists manually segmented lesions for tMTV calculations; each patient was independently segmented by two specialists. RESULTS The median of the manual tMTV was 146 cm3 (interquartile range [IQR]: 79-568 cm3) and the median difference between two tMTV values segmented by different specialists for the same patient was 26 cm3 (IQR: 10-86 cm3). In 22 of the 48 patients, the manual tMTV value was closer to the RECOMIA tMTV value than to the manual tMTV value segmented by the second specialist. In 11 of the remaining 26 patients, the difference between the RECOMIA tMTV and the manual tMTV was small (<26 cm3, which was the median difference between two manual tMTV values from the same patient). The corresponding numbers for PARS were 18 and 10 patients, respectively. CONCLUSION The results of this study indicate that RECOMIA and Siemens PARS AI tools could be used without any major manual adjustments in 69% (33/48) and 58% (28/48) of HL patients, respectively. This demonstrates the feasibility of using AI tools to support physicians measuring tMTV for assessment of prognosis in clinical practice.
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Affiliation(s)
- May Sadik
- Department of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Sally F Barrington
- School of Biomedical Engineering and Imaging Sciences Kings College, King's College London and Guy's and St Thomas' PET Centre, London, UK
| | - Elin Trägårdh
- Departmet of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Lund University, Malmö, Sweden
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA
| | - Anne L Nielsen
- Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Annika L Jakobsen
- Department of Clinical Physiology, Nuclear Medicine and PET, Centre of Diagnostic Investigations, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Jose L L Gongora
- Department of Diagnostic Imaging, Akershus University Hospital, Oslo, Norway
| | - Jesus L Urdaneta
- Department of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Rajender Kumar
- Department of Nuclear Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Lars Edenbrandt
- Department of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
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Estrada Alamo CE, Diatta F, Monsell SE, Lane-Fall MB. Artificial Intelligence in Anesthetic Care: A Survey of Physician Anesthesiologists. Anesth Analg 2024; 138:938-950. [PMID: 38055624 DOI: 10.1213/ane.0000000000006752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
BACKGROUND This study explored physician anesthesiologists' knowledge, exposure, and perceptions of artificial intelligence (AI) and their associations with attitudes and expectations regarding its use in clinical practice. The findings highlight the importance of understanding anesthesiologists' perspectives for the successful integration of AI into anesthesiology, as AI has the potential to revolutionize the field. METHODS A cross-sectional survey of 27,056 US physician anesthesiologists was conducted to assess their knowledge, perceptions, and expectations regarding the use of AI in clinical practice. The primary outcome measured was attitude toward the use of AI in clinical practice, with scores of 4 or 5 on a 5-point Likert scale indicating positive attitudes. The anticipated impact of AI on various aspects of professional work was measured using a 3-point Likert scale. Logistic regression was used to explore the relationship between participant responses and attitudes toward the use of AI in clinical practice. RESULTS A 2021 survey of 27,056 US physician anesthesiologists received 1086 responses (4% response rate). Most respondents were male (71%), active clinicians (93%) under 45 (34%). A majority of anesthesiologists (61%) had some knowledge of AI and 48% had a positive attitude toward using AI in clinical practice. While most respondents believed that AI can improve health care efficiency (79%), timeliness (75%), and effectiveness (69%), they are concerned that its integration in anesthesiology could lead to a decreased demand for anesthesiologists (45%) and decreased earnings (45%). Within a decade, respondents expected AI would outperform them in predicting adverse perioperative events (83%), formulating pain management plans (67%), and conducting airway exams (45%). The absence of algorithmic transparency (60%), an ambiguous environment regarding malpractice (47%), and the possibility of medical errors (47%) were cited as significant barriers to the use of AI in clinical practice. Respondents indicated that their motivation to use AI in clinical practice stemmed from its potential to enhance patient outcomes (81%), lower health care expenditures (54%), reduce bias (55%), and boost productivity (53%). Variables associated with positive attitudes toward AI use in clinical practice included male gender (odds ratio [OR], 1.7; P < .001), 20+ years of experience (OR, 1.8; P < .01), higher AI knowledge (OR, 2.3; P = .01), and greater AI openness (OR, 10.6; P < .01). Anxiety about future earnings was associated with negative attitudes toward AI use in clinical practice (OR, 0.54; P < .01). CONCLUSIONS Understanding anesthesiologists' perspectives on AI is essential for the effective integration of AI into anesthesiology, as AI has the potential to revolutionize the field.
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Affiliation(s)
- Carlos E Estrada Alamo
- From the Department of Anesthesiology, Virginia Mason Medical Center, Seattle, Washington
| | - Fortunay Diatta
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Yale School of Medicine, New Haven, Connecticut
| | - Sarah E Monsell
- Department of Biostatistics, University of Washington, Hans Rosling Center for Population Health, Seattle, Washington
| | - Meghan B Lane-Fall
- Department of Anesthesiology and Critical Care, University of Pennsylvania, Philadelphia, Pennsylvania
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Wang K, Ghafurian M, Chumachenko D, Cao S, Butt ZA, Salim S, Abhari S, Morita PP. Application of artificial intelligence in active assisted living for aging population in real-world setting with commercial devices - A scoping review. Comput Biol Med 2024; 173:108340. [PMID: 38555702 DOI: 10.1016/j.compbiomed.2024.108340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 02/23/2024] [Accepted: 03/17/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND The aging population is steadily increasing, posing new challenges and opportunities for healthcare systems worldwide. Technological advancements, particularly in commercially available Active Assisted Living devices, offer a promising alternative. These readily accessible products, ranging from smartwatches to home automation systems, are often equipped with Artificial Intelligence capabilities that can monitor health metrics, predict adverse events, and facilitate a safer living environment. However, there is no review exploring how Artificial Intelligence has been integrated into commercially available Active Assisted Living technologies, and how these devices monitor health metrics and provide healthcare solutions in a real-world environment for healthy aging. This review is essential because it fills a knowledge gap in understanding AI's integration in Active Assisted Living technologies in promoting healthy aging in real-world settings, identifying key issues that require to be addressed in future studies. OBJECTIVE The aim of this overview is to outline current understanding, identify potential research opportunities, and highlight research gaps from published studies regarding the use of Artificial Intelligence in commercially available Active Assisted Living technologies that assists older individuals aging at home. METHODS A comprehensive search was conducted in six databases-PubMed, CINAHL, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science-to identify relevant studies published over the past decade from 2013 to 2024. Our methodology adhered to the PRISMA extension for scoping reviews to ensure rigor and transparency throughout the review process. After applying predefined inclusion and exclusion criteria on 825 retrieved articles, a total of 64 papers were included for analysis and synthesis. RESULTS Several trends emerged from our analysis of the 64 selected papers. A majority of the work (39/64, 61%) was published after the year 2020. Geographically, most of the studies originated from East Asia and North America (36/64, 56%). The primary application goal of Artificial Intelligence in the reviewed literature was focused on activity recognition (34/64, 53%), followed by daily monitoring (10/64, 16%). Methodologically, tree-based and neural network-based approaches were the most prevalent Artificial Intelligence algorithms used in studies (32/64, 50% and 31/64, 48% respectively). A notable proportion of the studies (32/64, 50%) carried out their research using specially designed smart home testbeds that simulate the conditions in real-world. Moreover, ambient technology was a common thread (49/64, 77%), with occupancy-related data (such as motion and electrical appliance usage logs) and environmental sensors (indicators like temperature and humidity) being the most frequently used. CONCLUSION Our results suggest that Artificial Intelligence has been increasingly deployed in the real-world Active Assisted Living context over the past decade, offering a variety of applications aimed at healthy aging and facilitating independent living for the older adults. A wide range of smart home indicators were leveraged for comprehensive data analysis, exploring and enhancing the potentials and effectiveness of solutions. However, our review has identified multiple research gaps that need further investigation. First, most research has been conducted in controlled testbed environments, leaving a lack of real-world applications that could validate the technologies' efficacy and scalability. Second, there is a noticeable absence of research leveraging cloud technology, an essential tool for large-scale deployment and standardized data collection and management. Future work should prioritize these areas to maximize the potential benefits of Artificial Intelligence in Active Assisted Living settings.
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Affiliation(s)
- Kang Wang
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Moojan Ghafurian
- Department of Systems Design Engineering, University of Waterloo, ON, Canada
| | - Dmytro Chumachenko
- National Aerospace University "Kharkiv Aviation Institute", Kharkiv, Ukraine
| | - Shi Cao
- Department of Systems Design Engineering, University of Waterloo, ON, Canada
| | - Zahid A Butt
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Shahan Salim
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Shahabeddin Abhari
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Plinio P Morita
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada; Department of Systems Design Engineering, University of Waterloo, ON, Canada; Centre for Digital Therapeutics, Techna Institute, University Health Network, Toronto, ON, Canada; Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada.
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227
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Spada C, Piccirelli S, Hassan C, Ferrari C, Toth E, González-Suárez B, Keuchel M, McAlindon M, Finta Á, Rosztóczy A, Dray X, Salvi D, Riccioni ME, Benamouzig R, Chattree A, Humphries A, Saurin JC, Despott EJ, Murino A, Johansson GW, Giordano A, Baltes P, Sidhu R, Szalai M, Helle K, Nemeth A, Nowak T, Lin R, Costamagna G. AI-assisted capsule endoscopy reading in suspected small bowel bleeding: a multicentre prospective study. Lancet Digit Health 2024; 6:e345-e353. [PMID: 38670743 DOI: 10.1016/s2589-7500(24)00048-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 02/20/2024] [Accepted: 03/04/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND Capsule endoscopy reading is time consuming, and readers are required to maintain attention so as not to miss significant findings. Deep convolutional neural networks can recognise relevant findings, possibly exceeding human performances and reducing the reading time of capsule endoscopy. Our primary aim was to assess the non-inferiority of artificial intelligence (AI)-assisted reading versus standard reading for potentially small bowel bleeding lesions (high P2, moderate P1; Saurin classification) at per-patient analysis. The mean reading time in both reading modalities was evaluated among the secondary endpoints. METHODS Patients aged 18 years or older with suspected small bowel bleeding (with anaemia with or without melena or haematochezia, and negative bidirectional endoscopy) were prospectively enrolled at 14 European centres. Patients underwent small bowel capsule endoscopy with the Navicam SB system (Ankon, China), which is provided with a deep neural network-based AI system (ProScan) for automatic detection of lesions. Initial reading was performed in standard reading mode. Second blinded reading was performed with AI assistance (the AI operated a first-automated reading, and only AI-selected images were assessed by human readers). The primary endpoint was to assess the non-inferiority of AI-assisted reading versus standard reading in the detection (diagnostic yield) of potentially small bowel bleeding P1 and P2 lesions in a per-patient analysis. This study is registered with ClinicalTrials.gov, NCT04821349. FINDINGS From Feb 17, 2021 to Dec 29, 2021, 137 patients were prospectively enrolled. 133 patients were included in the final analysis (73 [55%] female, mean age 66·5 years [SD 14·4]; 112 [84%] completed capsule endoscopy). At per-patient analysis, the diagnostic yield of P1 and P2 lesions in AI-assisted reading (98 [73·7%] of 133 lesions) was non-inferior (p<0·0001) and superior (p=0·0213) to standard reading (82 [62·4%] of 133; 95% CI 3·6-19·0). Mean small bowel reading time was 33·7 min (SD 22·9) in standard reading and 3·8 min (3·3) in AI-assisted reading (p<0·0001). INTERPRETATION AI-assisted reading might provide more accurate and faster detection of clinically relevant small bowel bleeding lesions than standard reading. FUNDING ANKON Technologies, China and AnX Robotica, USA provided the NaviCam SB system.
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Affiliation(s)
- Cristiano Spada
- Department of Medicine, Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy; Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Stefania Piccirelli
- Department of Medicine, Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy; Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
| | - Cesare Hassan
- IRCCS Humanitas Research Hospital, Department of Biomedical Sciences, Rozzano, Milan, Italy
| | - Clarissa Ferrari
- Unit of Research and Clinical Trials, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy
| | - Ervin Toth
- Skåne University Hospital, Lund University, Department of Gastroenterology, Malmö, Sweden
| | - Begoña González-Suárez
- Hospital Clínic of Barcelona, Endoscopy Unit, Gastroenterology Department, Barcelona, Spain
| | - Martin Keuchel
- Agaplesion Bethesda Krankenhaus Bergedorf, Academic Teaching Hospital of the University of Hamburg, Clinic for Internal Medicine, Hamburg, Germany
| | - Marc McAlindon
- Sheffield Teaching Hospitals NHS Trust, Academic Department of Gastroenterology and Hepatology, Sheffield, UK; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Ádám Finta
- Endo-Kapszula Health Centre and Endoscopy Unit, Department of Gastroenterology, Székesfehérvár, Hungary
| | - András Rosztóczy
- University of Szeged, Department of Internal Medicine, Szeged, Hungary
| | - Xavier Dray
- Sorbonne University, Saint Antoine Hospital, APHP, Centre for Digestive Endoscopy, Paris, France
| | - Daniele Salvi
- Department of Medicine, Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy; Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Maria Elena Riccioni
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Digestive Endoscopy Unit, Rome, Italy
| | - Robert Benamouzig
- Hôpital Avicenne, Université Paris 13, Service de Gastroenterologie, Bobigny, France
| | - Amit Chattree
- South Tyneside and Sunderland NHS Foundation Trust, Gastroenterology, Stockton-on-Tees, UK
| | - Adam Humphries
- St Mark's Hospital and Academic Institute, Department of Gastroenterology, Middlesex, UK
| | - Jean-Christophe Saurin
- Hospices Civils de Lyon-Centre Hospitalier Universitaire, Gastroenterology Department, Lyon, France
| | - Edward J Despott
- The Royal Free Hospital and University College London (UCL) Institute for Liver and Digestive Health, Royal Free Unit for Endoscopy, London, UK
| | - Alberto Murino
- The Royal Free Hospital and University College London (UCL) Institute for Liver and Digestive Health, Royal Free Unit for Endoscopy, London, UK
| | | | - Antonio Giordano
- Hospital Clínic of Barcelona, Endoscopy Unit, Gastroenterology Department, Barcelona, Spain
| | - Peter Baltes
- Agaplesion Bethesda Krankenhaus Bergedorf, Academic Teaching Hospital of the University of Hamburg, Clinic for Internal Medicine, Hamburg, Germany
| | - Reena Sidhu
- Sheffield Teaching Hospitals NHS Trust, Academic Department of Gastroenterology and Hepatology, Sheffield, UK; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Milan Szalai
- Endo-Kapszula Health Centre and Endoscopy Unit, Department of Gastroenterology, Székesfehérvár, Hungary
| | - Krisztina Helle
- University of Szeged, Department of Internal Medicine, Szeged, Hungary
| | - Artur Nemeth
- Skåne University Hospital, Lund University, Department of Gastroenterology, Malmö, Sweden
| | | | - Rong Lin
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Department of Gastroenterology, Wuhan, China
| | - Guido Costamagna
- Department of Medicine, Gastroenterology and Endoscopy, Fondazione Poliambulanza Istituto Ospedaliero, Brescia, Italy; Università Cattolica del Sacro Cuore, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
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228
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Lebold KM, Preiksaitis C. Is Artificial Intelligence Ready to Take Over Triage? Ann Emerg Med 2024; 83:500-502. [PMID: 38642978 DOI: 10.1016/j.annemergmed.2024.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2024]
Affiliation(s)
- Katie M Lebold
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA
| | - Carl Preiksaitis
- Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA
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229
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Khokhar S, Holden J, Toomer C, Del Parigi A. Weight Loss with an AI-Powered Digital Platform for Lifestyle Intervention. Obes Surg 2024; 34:1810-1818. [PMID: 38573389 DOI: 10.1007/s11695-024-07209-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 03/28/2024] [Accepted: 03/28/2024] [Indexed: 04/05/2024]
Abstract
BACKGROUND Lifestyle intervention remains the cornerstone of weight loss programs in addition to pharmacological or surgical therapies. Artificial intelligence (AI) and other digital technologies can offer individualized approaches to lifestyle intervention to enable people with obesity to reach successful weight loss. METHODS SureMediks, a digital lifestyle intervention platform using AI, was tested by 391 participants (58% women) with a broad range of BMI (20-78 kg/m2), with the aim of losing weight over 24 weeks in a multinational field trial. SureMediks consists of a mobile app, an Internet-connected scale, and a discipline of artificial intelligence called Expert system to provide individualized guidance and weight-loss management. RESULTS All participants lost body weight (average 14%, range 4-22%). Almost all (98.7%) participants lost at least 5% of body weight, 75% lost at least 10%, 43% at least 15%, and 9% at least 20%, suggesting that this AI-powered lifestyle intervention was also effective in reducing the burden of obesity co-morbidities. Weight loss was partially positively correlated with female sex, accountability circle size, and participation in challenges, while it was negatively correlated with sub-goal reassignment. The latter three variables are specific features of the SureMediks weight loss program. CONCLUSION An AI-assisted lifestyle intervention allowed people with different body sizes to lose 14% body weight on average, with 99% of them losing more than 5%, over 24 weeks. These results show that digital technologies and AI might provide a successful means to lose weight, before, during, and after pharmacological or surgical therapies.
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Affiliation(s)
| | - John Holden
- Rockford-College of Medicine, University of Illinois, Rockford, IL, 6110, USA
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230
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He XJ, Wang XL, Su TK, Yao LJ, Zheng J, Wen XD, Xu QW, Huang QR, Chen LB, Chen CX, Lin HF, Chen YQ, Hu YX, Zhang KH, Jiang CS, Liu G, Li DZ, Li DL, Wen W. Artificial intelligence-assisted system for the assessment of Forrest classification of peptic ulcer bleeding: a multicenter diagnostic study. Endoscopy 2024; 56:334-342. [PMID: 38412993 DOI: 10.1055/a-2252-4874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
BACKGROUND Inaccurate Forrest classification may significantly affect clinical outcomes, especially in high risk patients. Therefore, this study aimed to develop a real-time deep convolutional neural network (DCNN) system to assess the Forrest classification of peptic ulcer bleeding (PUB). METHODS A training dataset (3868 endoscopic images) and an internal validation dataset (834 images) were retrospectively collected from the 900th Hospital, Fuzhou, China. In addition, 521 images collected from four other hospitals were used for external validation. Finally, 46 endoscopic videos were prospectively collected to assess the real-time diagnostic performance of the DCNN system, whose diagnostic performance was also prospectively compared with that of three senior and three junior endoscopists. RESULTS The DCNN system had a satisfactory diagnostic performance in the assessment of Forrest classification, with an accuracy of 91.2% (95%CI 89.5%-92.6%) and a macro-average area under the receiver operating characteristic curve of 0.80 in the validation dataset. Moreover, the DCNN system could judge suspicious regions automatically using Forrest classification in real-time videos, with an accuracy of 92.0% (95%CI 80.8%-97.8%). The DCNN system showed more accurate and stable diagnostic performance than endoscopists in the prospective clinical comparison test. This system helped to slightly improve the diagnostic performance of senior endoscopists and considerably enhance that of junior endoscopists. CONCLUSION The DCNN system for the assessment of the Forrest classification of PUB showed satisfactory diagnostic performance, which was slightly superior to that of senior endoscopists. It could therefore effectively assist junior endoscopists in making such diagnoses during gastroscopy.
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Affiliation(s)
- Xiao-Jian He
- Fuzong Clinical Medical College, Fujian Medical University, Fuzhou, China
- Department of Digestive Diseases, 900th Hospital of PLA, Fuzhou, China
- Department of Digestive Diseases, Oriental Hospital affiliated to Xiamen University, Fuzhou, China
| | - Xiao-Ling Wang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Department of Gastroenterology, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Tian-Kang Su
- School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Li-Jia Yao
- Fuzong Clinical Medical College, Fujian Medical University, Fuzhou, China
- Department of Digestive Diseases, 900th Hospital of PLA, Fuzhou, China
| | - Jing Zheng
- Fuzong Clinical Medical College, Fujian Medical University, Fuzhou, China
- Department of Digestive Diseases, 900th Hospital of PLA, Fuzhou, China
| | - Xiao-Dong Wen
- Fuzong Clinical Medical College, Fujian Medical University, Fuzhou, China
- Department of Digestive Diseases, 900th Hospital of PLA, Fuzhou, China
| | - Qin-Wei Xu
- Department of Gastroenterology, Shanghai East Hospital, Shanghai, China
- School of Medicine, Tongji University, Shanghai, China
| | - Qian-Rong Huang
- Department of Digestive Diseases, The Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Li-Bin Chen
- Department of Digestive Diseases, Cangshan District of 900th Hospital of PLA (Fuzhou Air Force Hospital), Fuzhou, China
| | - Chang-Xin Chen
- Department of Digestive Diseases, Fujian Medical University Affiliated Quanzhou First Hospital, Quanzhou, China
| | - Hai-Fan Lin
- Department of Digestive Diseases, Xiamen Medical College Affiliated Haicang Hospital, Xiamen, China
| | - Yi-Qun Chen
- Department of Digestive Diseases, Xiamen Medical College Affiliated Haicang Hospital, Xiamen, China
| | - Yan-Xing Hu
- Xiamen Innovision Medical Technology Co., Ltd, Xiamen, China
| | - Kai-Hua Zhang
- School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Chuan-Shen Jiang
- Fuzong Clinical Medical College, Fujian Medical University, Fuzhou, China
- Department of Digestive Diseases, 900th Hospital of PLA, Fuzhou, China
| | - Gang Liu
- Fuzong Clinical Medical College, Fujian Medical University, Fuzhou, China
- Department of Digestive Diseases, 900th Hospital of PLA, Fuzhou, China
| | - Da-Zhou Li
- Fuzong Clinical Medical College, Fujian Medical University, Fuzhou, China
- Department of Digestive Diseases, 900th Hospital of PLA, Fuzhou, China
- Department of Digestive Diseases, Oriental Hospital affiliated to Xiamen University, Fuzhou, China
| | - Dong-Liang Li
- Fuzong Clinical Medical College, Fujian Medical University, Fuzhou, China
- Department of Hepatobiliary Diseases, 900th Hospital of PLA, Fuzhou, China
| | - Wang Wen
- Fuzong Clinical Medical College, Fujian Medical University, Fuzhou, China
- Department of Digestive Diseases, 900th Hospital of PLA, Fuzhou, China
- Department of Digestive Diseases, Oriental Hospital affiliated to Xiamen University, Fuzhou, China
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231
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Giorgini F, Di Dalmazi G, Diciotti S. Artificial intelligence in endocrinology: a comprehensive review. J Endocrinol Invest 2024; 47:1067-1082. [PMID: 37971630 PMCID: PMC11035463 DOI: 10.1007/s40618-023-02235-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 10/24/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND AND AIM Artificial intelligence (AI) has emerged as a promising technology in the field of endocrinology, offering significant potential to revolutionize the diagnosis, treatment, and management of endocrine disorders. This comprehensive review aims to provide a concise overview of the current landscape of AI applications in endocrinology and metabolism, focusing on the fundamental concepts of AI, including machine learning algorithms and deep learning models. METHODS The review explores various areas of endocrinology where AI has demonstrated its value, encompassing screening and diagnosis, risk prediction, translational research, and "pre-emptive medicine". Within each domain, relevant studies are discussed, offering insights into the methodology and main findings of AI in the treatment of different pathologies, such as diabetes mellitus and related disorders, thyroid disorders, adrenal tumors, and bone and mineral disorders. RESULTS Collectively, these studies show the valuable contributions of AI in optimizing healthcare outcomes and unveiling new understandings of the intricate mechanisms underlying endocrine disorders. Furthermore, AI-driven approaches facilitate the development of precision medicine strategies, enabling tailored interventions for patients based on their individual characteristics and needs. CONCLUSIONS By embracing AI in endocrinology, a future can be envisioned where medical professionals and AI systems synergistically collaborate, ultimately enhancing the lives of individuals affected by endocrine disorders.
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Affiliation(s)
- F Giorgini
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy
- Division of Endocrinology and Diabetes Prevention and Care, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - G Di Dalmazi
- Division of Endocrinology and Diabetes Prevention and Care, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Department of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - S Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy.
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy.
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232
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Kessler DA, Graves MJ. Editorial for "The Effect of Image Resampling on the Performance of Radiomics-Based Artificial Intelligence in Multicenter Prostate MRI". J Magn Reson Imaging 2024; 59:1807-1808. [PMID: 37534877 DOI: 10.1002/jmri.28939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 07/24/2023] [Indexed: 08/04/2023] Open
Affiliation(s)
| | - Martin J Graves
- Department of Radiology, University of Cambridge, Cambridge, UK
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233
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Hassoun S, Bruckmann C, Ciardullo S, Perseghin G, Marra F, Curto A, Arena U, Broccolo F, Di Gaudio F. NAIF: A novel artificial intelligence-based tool for accurate diagnosis of stage F3/F4 liver fibrosis in the general adult population, validated with three external datasets. Int J Med Inform 2024; 185:105373. [PMID: 38395017 DOI: 10.1016/j.ijmedinf.2024.105373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 02/05/2024] [Accepted: 02/07/2024] [Indexed: 02/25/2024]
Abstract
OBJECTIVE The purpose of this study was to determine the effectiveness of a new AI-based tool called NAIF (NAFLD-AI-Fibrosis) in identifying individuals from the general population with advanced liver fibrosis (stage F3/F4). We compared NAIF's performance to two existing risk score calculators, aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (Fib4). METHODS To set up the algorithm for diagnosing severe liver fibrosis (defined as Fibroscan® values E ≥ 9.7 KPa), we used 19 blood biochemistry parameters and two demographic parameters in a group of 5,962 individuals from the NHANES population (2017-2020 pre-pandemic, public database). We then assessed the algorithm's performance by comparing its accuracy, precision, sensitivity, specificity, and F1 score values to those of APRI and Fib4 scoring systems. RESULTS In a kept-out sub dataset of the NHANES population, NAIF achieved a predictive precision of 72 %, a sensitivity of 61 %, and a specificity of 77 % in correctly identifying adults (aged 18-79 years) with severe liver fibrosis. Additionally, NAIF performed well when tested with two external datasets of Italian patients with a Fibroscan® score E ≥ 9.7 kPa, and with an external dataset of patients with diagnosis of severe liver fibrosis through biopsy. CONCLUSIONS The results of our study suggest that NAIF, using routinely available parameters, outperforms in sensitivity existing scoring methods (Fib4 and APRI) in diagnosing severe liver fibrosis, even when tested with external validation datasets. NAIF uses routinely available parameters, making it a promising tool for identifying individuals with advanced liver fibrosis from the general population. Word count abstract: 236.
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Affiliation(s)
- Samir Hassoun
- Unità Operativa Centro Controllo Qualità e Rischio Chimico (CQRC), Azienda Ospedaliera Villa Sofia Cervello, viale Strasburgo 233, 90146 Palermo, Italy.
| | - Chiara Bruckmann
- Unità Operativa Centro Controllo Qualità e Rischio Chimico (CQRC), Azienda Ospedaliera Villa Sofia Cervello, viale Strasburgo 233, 90146 Palermo, Italy.
| | - Stefano Ciardullo
- Department of Medicine and Surgery, University of Milano-Bicocca, via Modigliani 10, 20900 Monza, Italy; Department of Medicine and Rehabilitation, Policlinico di Monza, Monza, via Modigliani 10, 20900 Monza, Italy
| | - Gianluca Perseghin
- Department of Medicine and Surgery, University of Milano-Bicocca, via Modigliani 10, 20900 Monza, Italy; Department of Medicine and Rehabilitation, Policlinico di Monza, Monza, via Modigliani 10, 20900 Monza, Italy
| | - Fabio Marra
- Dipartimento di Medicina Sperimentale e Clinica, University of Florence, Largo Giovanni Alessandro Brambilla, 3, 50134 Firenze Italy
| | - Armando Curto
- Dipartimento di Medicina Sperimentale e Clinica, University of Florence, Largo Giovanni Alessandro Brambilla, 3, 50134 Firenze Italy
| | - Umberto Arena
- Dipartimento di Medicina Sperimentale e Clinica, University of Florence, Largo Giovanni Alessandro Brambilla, 3, 50134 Firenze Italy
| | - Francesco Broccolo
- Department of Experimental Medicine, University of Salento, 73100 Lecce, Italy.
| | - Francesca Di Gaudio
- Unità Operativa Centro Controllo Qualità e Rischio Chimico (CQRC), Azienda Ospedaliera Villa Sofia Cervello, viale Strasburgo 233, 90146 Palermo, Italy; PROMISE-Promotion of Health, Maternal-Childhood, Internal and Specialized Medicine of Excellence G. D'Alessandro, Piazza delle Cliniche, 2, 90127 Palermo, Italy
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Villaizán-Vallelado M, Salvatori M, Carro B, Sanchez-Esguevillas AJ. Graph Neural Network contextual embedding for Deep Learning on tabular data. Neural Netw 2024; 173:106180. [PMID: 38447303 DOI: 10.1016/j.neunet.2024.106180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 01/29/2024] [Accepted: 02/13/2024] [Indexed: 03/08/2024]
Abstract
All industries are trying to leverage Artificial Intelligence (AI) based on their existing big data which is available in so called tabular form, where each record is composed of a number of heterogeneous continuous and categorical columns also known as features. Deep Learning (DL) has constituted a major breakthrough for AI in fields related to human skills like natural language processing, but its applicability to tabular data has been more challenging. More classical Machine Learning (ML) models like tree-based ensemble ones usually perform better. This paper presents a novel DL model using Graph Neural Network (GNN) more specifically Interaction Network (IN), for contextual embedding and modeling interactions among tabular features. Its results outperform those of a recently published survey with DL benchmark based on seven public datasets, also achieving competitive results when compared to boosted-tree solutions.
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Affiliation(s)
- Mario Villaizán-Vallelado
- Artificial Intelligence Laboratory (AI-Lab), Telefonica I+D, Spain; Universidad de Valladolid, Valladolid, 47011, Spain.
| | - Matteo Salvatori
- Artificial Intelligence Laboratory (AI-Lab), Telefonica I+D, Spain.
| | - Belén Carro
- Universidad de Valladolid, Valladolid, 47011, Spain.
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235
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Nimmal Haribabu G, Basu B. Implementing Machine Learning approaches for accelerated prediction of bone strain in acetabulum of a hip joint. J Mech Behav Biomed Mater 2024; 153:106495. [PMID: 38460455 DOI: 10.1016/j.jmbbm.2024.106495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/10/2024] [Accepted: 03/01/2024] [Indexed: 03/11/2024]
Abstract
The Finite Element (FE) methods for biomechanical analysis involving implant design and subject parameters for musculoskeletal applications are extensively reported in literature. Such an approach is manually intensive and computationally expensive with longer simulations times. Although Artificial Intelligence (AI) based approaches are implemented to a limited extent in biomechanics, such approaches to predict bone strain in acetabulum of a hip joint, are hardly explored. In this context, the primary objective of this paper is to evaluate machine learning (ML) models in tandem with high-fidelity FEA data for the accelerated prediction of the biomechanical response in the acetabulum of the human hip joint, during the walking gait. The parameters used in the FEA study included the subject weight, number and distribution of fins on the periphery of the acetabular shell, bone condition and phases of the gait cycle. The biomechanical response has also been evaluated using three different acetabular liners, including pre-clinically validated HDPE-20% HA-20% Al2O3, highly-crosslinked ultrahigh molecular weight polyethylene (HC-UHMWPE) and ZrO2-toughened Al2O3 (ZTA). Such parametric variation in FEA analysis, involving 26 variables and a full factorial design resulted in 10,752 datasets for spatially varying bone strains. The bone condition, as opposed to subject weight, was found to play a statistically significant role in determining the strain response in the periprosthetic bone of the acetabulum. While utilising hyperparameter tuning, K-fold cross validation and statistical learning approaches, a number of ML models were trained on the FEA dataset, and the Random Forest model performed the best with a coefficient of determination (R2) value of 0.99/0.97 and Root Mean Square Error (RMSE) of 0.02/0.01 on the training/test dataset. Taken together, this study establishes the potential of ML approach as a fast surrogate of FEA for implant biomechanics analysis, in less than a minute.
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Affiliation(s)
- Gowtham Nimmal Haribabu
- Laboratory for Biomaterials Science and Translational Research, Materials Research Centre, Indian Institute of Science, Bangalore, 560012, India
| | - Bikramjit Basu
- Laboratory for Biomaterials Science and Translational Research, Materials Research Centre, Indian Institute of Science, Bangalore, 560012, India.
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236
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Jafar A, Pasqua MR. Postprandial glucose-management strategies in type 1 diabetes: Current approaches and prospects with precision medicine and artificial intelligence. Diabetes Obes Metab 2024; 26:1555-1566. [PMID: 38263540 DOI: 10.1111/dom.15463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/01/2024] [Accepted: 01/05/2024] [Indexed: 01/25/2024]
Abstract
Postprandial glucose control can be challenging for individuals with type 1 diabetes, and this can be attributed to many factors, including suboptimal therapy parameters (carbohydrate ratios, correction factors, basal doses) because of physiological changes, meal macronutrients and engagement in postprandial physical activity. This narrative review aims to examine the current postprandial glucose-management strategies tested in clinical trials, including adjusting therapy settings, bolusing for meal macronutrients, adjusting pre-exercise and postexercise meal boluses for postprandial physical activity, and other therapeutic options, for individuals on open-loop and closed-loop therapies. Then we discuss their challenges and future avenues. Despite advancements in insulin delivery devices such as closed-loop systems and decision-support systems, many individuals with type 1 diabetes still struggle to manage their glucose levels. The main challenge is the lack of personalized recommendations, causing suboptimal postprandial glucose control. We suggest that postprandial glucose control can be improved by (i) providing personalized recommendations for meal macronutrients and postprandial activity; (ii) including behavioural recommendations; (iii) using other personalized therapeutic approaches (e.g. glucagon-like peptide-1 receptor agonists, sodium-glucose co-transporter inhibitors, amylin analogues, inhaled insulin) in addition to insulin therapy; and (iv) integrating an interpretability report to explain to individuals about changes in treatment therapy and behavioural recommendations. In addition, we suggest a future avenue to implement precision recommendations for individuals with type 1 diabetes utilizing the potential of deep reinforcement learning and foundation models (such as GPT and BERT), employing different modalities of data including diabetes-related and external background factors (i.e. behavioural, environmental, biological and abnormal events).
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Affiliation(s)
- Adnan Jafar
- Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Melissa-Rosina Pasqua
- Division of Endocrinology, Department of Medicine, McGill University, Montreal, Quebec, Canada
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Expression of Concern: Assessment in carbon-based layered double hydroxides for water and wastewater: Application of artificial intelligence and recent progress. Chemosphere 2024; 356:141986. [PMID: 38685629 DOI: 10.1016/j.chemosphere.2024.141986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
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238
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Viraswami-Appanna K, Buenconsejo J, Baidoo C, Chan I, Li D, Micsinai-Balan M, Tiwari R, Yang L, Sethuraman V. Accelerating drug development at Bristol Myers Squibb through innovation. Drug Discov Today 2024; 29:103952. [PMID: 38508230 DOI: 10.1016/j.drudis.2024.103952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 03/07/2024] [Accepted: 03/14/2024] [Indexed: 03/22/2024]
Abstract
This paper focuses on the use of novel technologies and innovative trial designs to accelerate evidence generation and increase pharmaceutical Research and Development (R&D) productivity, at Bristol Myers Squibb. We summarize learnings with case examples, on how we prepared and continuously evolved to address the increasing cost, complexities, and external pressures in drug development, to bring innovative medicines to patients much faster. These learnings were based on review of internal efforts toward accelerating R&D focusing on four key areas: adopting innovative trial designs, optimizing trial designs, leveraging external control data, and implementing novel methods using artificial intelligence and machine learning.
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Affiliation(s)
| | - Joan Buenconsejo
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | - Charlotte Baidoo
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | - Ivan Chan
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | - Daniel Li
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | | | - Ram Tiwari
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | - Ling Yang
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
| | - Venkat Sethuraman
- Global Biometrics and Data Sciences, Bristol Myers Squibb, Princeton, NJ, USA
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Destere A, Marchello G, Merino D, Othman NB, Gérard AO, Lavrut T, Viard D, Rocher F, Corneli M, Bouveyron C, Drici MD. An artificial intelligence algorithm for co-clustering to help in pharmacovigilance before and during the COVID-19 pandemic. Br J Clin Pharmacol 2024; 90:1258-1267. [PMID: 38332645 DOI: 10.1111/bcp.16012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 01/16/2024] [Accepted: 01/19/2024] [Indexed: 02/10/2024] Open
Abstract
AIMS Monitoring drug safety in real-world settings is the primary aim of pharmacovigilance. Frequent adverse drug reactions (ADRs) are usually identified during drug development. Rare ones are mostly characterized through post-marketing scrutiny, increasingly with the use of data mining and disproportionality approaches, which lead to new drug safety signals. Nonetheless, waves of excessive numbers of reports, often stirred up by social media, may overwhelm and distort this process, as observed recently with levothyroxine or COVID-19 vaccines. As human resources become rarer in the field of pharmacovigilance, we aimed to evaluate the performance of an unsupervised co-clustering method to help the monitoring of drug safety. METHODS A dynamic latent block model (dLBM), based on a time-dependent co-clustering generative method, was used to summarize all regional ADR reports (n = 45 269) issued between 1 January 2012 and 28 February 2022. After analysis of their intra and extra interrelationships, all reports were grouped into different cluster types (time, drug, ADR). RESULTS Our model clustered all reports in 10 time, 10 ADR and 9 drug collections. Based on such clustering, three prominent societal problems were detected, subsequent to public health concerns about drug safety, including a prominent media hype about the perceived safety of COVID-19 vaccines. The dLBM also highlighted some specific drug-ADR relationships, such as the association between antiplatelets, anticoagulants and bleeding. CONCLUSIONS Co-clustering and dLBM appear as promising tools to explore large pharmacovigilance databases. They allow, 'unsupervisedly', the detection, exploration and strengthening of safety signals, facilitating the analysis of massive upsurges of reports.
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Affiliation(s)
- Alexandre Destere
- Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France
- Université Côte d'Azur, Inria, CNRS, Laboratoire J.A. Dieudonné, Maasai team, Nice, France
| | - Giulia Marchello
- Université Côte d'Azur, Inria, CNRS, Laboratoire J.A. Dieudonné, Maasai team, Nice, France
| | - Diane Merino
- Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France
| | - Nouha Ben Othman
- Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France
| | - Alexandre O Gérard
- Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France
| | - Thibaud Lavrut
- Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France
| | - Delphine Viard
- Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France
| | - Fanny Rocher
- Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France
| | - Marco Corneli
- Université Côte d'Azur, Inria, Maison de la Modélisation des Simulations et des Interactions (MSI), MAASAI team, Nice, France
| | - Charles Bouveyron
- Université Côte d'Azur, Inria, CNRS, Laboratoire J.A. Dieudonné, Maasai team, Nice, France
| | - Milou-Daniel Drici
- Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France
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Bhattarai P, Thakuri DS, Nie Y, Chand GB. Explainable AI-based Deep-SHAP for mapping the multivariate relationships between regional neuroimaging biomarkers and cognition. Eur J Radiol 2024; 174:111403. [PMID: 38452732 DOI: 10.1016/j.ejrad.2024.111403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/16/2024] [Accepted: 03/01/2024] [Indexed: 03/09/2024]
Abstract
BACKGROUND Mild cognitive impairment (MCI)/Alzheimer's disease (AD) is associated with cognitive decline beyond normal aging and linked to the alterations of brain volume quantified by magnetic resonance imaging (MRI) and amyloid-beta (Aβ) quantified by positron emission tomography (PET). Yet, the complex relationships between these regional imaging measures and cognition in MCI/AD remain unclear. Explainable artificial intelligence (AI) may uncover such relationships. METHOD We integrate the AI-based deep learning neural network and Shapley additive explanations (SHAP) approaches and introduce the Deep-SHAP method to investigate the multivariate relationships between regional imaging measures and cognition. After validating this approach on simulated data, we apply it to real experimental data from MCI/AD patients. RESULTS Deep-SHAP significantly predicted cognition using simulated regional features and identified the ground-truth simulated regions as the most significant multivariate predictors. When applied to experimental MRI data, Deep-SHAP revealed that the insula, lateral occipital, medial frontal, temporal pole, and occipital fusiform gyrus are the primary contributors to global cognitive decline in MCI/AD. Furthermore, when applied to experimental amyloid Pittsburgh compound B (PiB)-PET data, Deep-SHAP identified the key brain regions for global cognitive decline in MCI/AD as the inferior temporal, parahippocampal, inferior frontal, supratemporal, and lateral frontal gray matter. CONCLUSION Deep-SHAP method uncovered the multivariate relationships between regional brain features and cognition, offering insights into the most critical modality-specific brain regions involved in MCI/AD mechanisms.
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Affiliation(s)
- Puskar Bhattarai
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Deepa Singh Thakuri
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; University of Missouri, School of Medicine, Columbia, MO, USA
| | - Yuzheng Nie
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Ganesh B Chand
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA; Imaging Core, Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA; Institute of Clinical and Translational Sciences, Washington University School of Medicine, St. Louis, MO, USA; NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA.
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241
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Hussain F, Ali Y, Li Y, Haque MM. Revisiting the hybrid approach of anomaly detection and extreme value theory for estimating pedestrian crashes using traffic conflicts obtained from artificial intelligence-based video analytics. Accid Anal Prev 2024; 199:107517. [PMID: 38442633 DOI: 10.1016/j.aap.2024.107517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/17/2023] [Accepted: 02/21/2024] [Indexed: 03/07/2024]
Abstract
Pedestrians represent a group of vulnerable road users who are at a higher risk of sustaining severe injuries than other road users. As such, proactively assessing pedestrian crash risks is of paramount importance. Recently, extreme value theory models have been employed for proactively assessing crash risks from traffic conflicts, whereby the underpinning of these models are two sampling approaches, namely block maxima and peak over threshold. Earlier studies reported poor accuracy and large uncertainty of these models, which has been largely attributed to limited sample size. Another fundamental reason for such poor performance could be the improper selection of traffic conflict extremes due to the lack of an efficient sampling mechanism. To test this hypothesis and demonstrate the effect of sampling technique on extreme value theory models, this study aims to develop hybrid models whereby unconventional sampling techniques were used to select the extreme vehicle-pedestrian conflicts that were then modelled using extreme value distributions to estimate the crash risk. Unconventional sampling techniques refer to unsupervised machine learning-based anomaly detection techniques. In particular, Isolation forest and minimum covariance determinant techniques were used to identify extreme vehicle-pedestrian conflicts characterised by post encroachment time as the traffic conflict measure. Video data was collected for four weekdays (6 am-6 pm) from three four-legged intersections in Brisbane, Australia and processed using artificial intelligence-based video analytics. Results indicate that mean crash estimates of hybrid models were much closer to observed crashes with narrower confidence intervals as compared with traditional extreme value models. The findings of this study demonstrate the suitability of machine learning-based anomaly detection techniques to augment the performance of existing extreme value models for estimating pedestrian crashes from traffic conflicts. These findings are envisaged to further explore the possibility of utilising more advanced machine learning models for traffic conflict techniques.
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Affiliation(s)
- Fizza Hussain
- Queensland University of Technology, School of Civil & Environment Engineering, Faculty of Engineering, Brisbane 4001, Australia.
| | - Yasir Ali
- School of Architecture, Building, and Civil Engineering, Loughborough University, Leicestershire LE11 3TU, United Kingdom.
| | - Yuefeng Li
- Queensland University of Technology, School of Computer Science, Faculty of Science, Brisbane 4001, Australia.
| | - Md Mazharul Haque
- Queensland University of Technology, School of Civil & Environment Engineering, Faculty of Engineering, Brisbane 4001, Australia.
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242
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Contino S, Cruciata L, Gambino O, Pirrone R. IODeep: An IOD for the introduction of deep learning in the DICOM standard. Comput Methods Programs Biomed 2024; 248:108113. [PMID: 38479148 DOI: 10.1016/j.cmpb.2024.108113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/22/2024] [Accepted: 03/01/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND AND OBJECTIVE In recent years, Artificial Intelligence (AI) and in particular Deep Neural Networks (DNN) became a relevant research topic in biomedical image segmentation due to the availability of more and more data sets along with the establishment of well known competitions. Despite the popularity of DNN based segmentation on the research side, these techniques are almost unused in the daily clinical practice even if they could support effectively the physician during the diagnostic process. Apart from the issues related to the explainability of the predictions of a neural model, such systems are not integrated in the diagnostic workflow, and a standardization of their use is needed to achieve this goal. METHODS This paper presents IODeep a new DICOM Information Object Definition (IOD) aimed at storing both the weights and the architecture of a DNN already trained on a particular image dataset that is labeled as regards the acquisition modality, the anatomical region, and the disease under investigation. RESULTS The IOD architecture is presented along with a DNN selection algorithm from the PACS server based on the labels outlined above, and a simple PACS viewer purposely designed for demonstrating the effectiveness of the DICOM integration, while no modifications are required on the PACS server side. Also a service based architecture in support of the entire workflow has been implemented. CONCLUSION IODeep ensures full integration of a trained AI model in a DICOM infrastructure, and it is also enables a scenario where a trained model can be either fine-tuned with hospital data or trained in a federated learning scheme shared by different hospitals. In this way AI models can be tailored to the real data produced by a Radiology ward thus improving the physician decision making process. Source code is freely available at https://github.com/CHILab1/IODeep.git.
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Affiliation(s)
- Salvatore Contino
- Department of Engineering, University of Palermo, Palermo, 90128, Sicily, Italy
| | - Luca Cruciata
- Department of Engineering, University of Palermo, Palermo, 90128, Sicily, Italy
| | - Orazio Gambino
- Department of Engineering, University of Palermo, Palermo, 90128, Sicily, Italy.
| | - Roberto Pirrone
- Department of Engineering, University of Palermo, Palermo, 90128, Sicily, Italy
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Wang C, Ong J, Wang C, Ong H, Cheng R, Ong D. Potential for GPT Technology to Optimize Future Clinical Decision-Making Using Retrieval-Augmented Generation. Ann Biomed Eng 2024; 52:1115-1118. [PMID: 37530906 DOI: 10.1007/s10439-023-03327-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 07/17/2023] [Indexed: 08/03/2023]
Abstract
Advancements in artificial intelligence (AI) provide many helpful tools for healthcare, one of which includes AI chatbots that use natural language processing to create humanlike, conversational dialog. These chatbots have general cognitive skills and are able to engage with clinicians and patients to discuss patients' health conditions and what they may be at risk for. While chatbot engines have access to a wide range of medical texts and research papers, they currently provide high-level, generic responses and are limited in their ability to provide diagnostic guidance and clinical advice to patients on an individual level. The essay discusses the use of retrieval-augmented generation (RAG), which can be used to improve the specificity of user-entered prompts and thereby enhance the detail in AI chatbot responses. By embedding more recent clinical data and trusted medical sources, such as clinical guidelines, into the chatbot models, AI chatbots can provide more patient-specific guidance, faster diagnoses and treatment recommendations, and greater improvement of patient outcomes.
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Affiliation(s)
- Calvin Wang
- College of Medicine - Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, 08901, USA.
| | - Joshua Ong
- Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Chara Wang
- Biotechnology High School, Freehold, NJ, USA
| | - Hannah Ong
- College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Rebekah Cheng
- Department of Physical Therapy, Virginia Commonwealth University, Richmond, VA, USA
| | - Dennis Ong
- Amazon Web Services, Amazon, Seattle, WA, USA
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Jadav R, Kameriya R, Chatterjee S, Gour V, Purohit P, Bandyopadhyay A. Identification, synthesis, and characterization of an unprecedented N-(2-carboxyethyl) adduct impurity in an injectable ganirelix formulation. J Pept Sci 2024; 30:e3564. [PMID: 38131153 DOI: 10.1002/psc.3564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/23/2023]
Abstract
Ganirelix, a peptide-based drug used to treat female infertility, has been in high market demand, which attracted generic formulation. A hitherto unknown impurity of ganirelix was observed in our formulation process, which reached ~0.3% in 6 months and led to a detailed investigation of its structure. In-depth analysis of ESI-MS/MS data of this impurity coupled with an artificial intelligence prediction tool led to a highly unusual putative structure, that is, N-(2-carboxyethyl)-ganirelix (NCE-GA), which was authenticated by chemical synthesis from ganirelix and NMR analysis and via corroborated HPLC and MS/MS data with the formulation-derived impurity.
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Affiliation(s)
- Rohit Jadav
- Kashiv BioSciences Pvt Ltd., Ahmedabad, Gujarat, India
| | - Ramraj Kameriya
- Biomimetic Peptide Engineering Laboratory, Department of Chemistry, Indian Institute of Technology Ropar, Ropar, Punjab, India
| | - Saurav Chatterjee
- Biomimetic Peptide Engineering Laboratory, Department of Chemistry, Indian Institute of Technology Ropar, Ropar, Punjab, India
| | - Vinod Gour
- Biomimetic Peptide Engineering Laboratory, Department of Chemistry, Indian Institute of Technology Ropar, Ropar, Punjab, India
| | - Parva Purohit
- Kashiv BioSciences Pvt Ltd., Ahmedabad, Gujarat, India
| | - Anupam Bandyopadhyay
- Biomimetic Peptide Engineering Laboratory, Department of Chemistry, Indian Institute of Technology Ropar, Ropar, Punjab, India
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245
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Biswas S, Davies LN, Sheppard AL, Logan NS, Wolffsohn JS. Utility of artificial intelligence-based large language models in ophthalmic care. Ophthalmic Physiol Opt 2024; 44:641-671. [PMID: 38404172 DOI: 10.1111/opo.13284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 01/23/2024] [Accepted: 01/25/2024] [Indexed: 02/27/2024]
Abstract
PURPOSE With the introduction of ChatGPT, artificial intelligence (AI)-based large language models (LLMs) are rapidly becoming popular within the scientific community. They use natural language processing to generate human-like responses to queries. However, the application of LLMs and comparison of the abilities among different LLMs with their human counterparts in ophthalmic care remain under-reported. RECENT FINDINGS Hitherto, studies in eye care have demonstrated the utility of ChatGPT in generating patient information, clinical diagnosis and passing ophthalmology question-based examinations, among others. LLMs' performance (median accuracy, %) is influenced by factors such as the iteration, prompts utilised and the domain. Human expert (86%) demonstrated the highest proficiency in disease diagnosis, while ChatGPT-4 outperformed others in ophthalmology examinations (75.9%), symptom triaging (98%) and providing information and answering questions (84.6%). LLMs exhibited superior performance in general ophthalmology but reduced accuracy in ophthalmic subspecialties. Although AI-based LLMs like ChatGPT are deemed more efficient than their human counterparts, these AIs are constrained by their nonspecific and outdated training, no access to current knowledge, generation of plausible-sounding 'fake' responses or hallucinations, inability to process images, lack of critical literature analysis and ethical and copyright issues. A comprehensive evaluation of recently published studies is crucial to deepen understanding of LLMs and the potential of these AI-based LLMs. SUMMARY Ophthalmic care professionals should undertake a conservative approach when using AI, as human judgement remains essential for clinical decision-making and monitoring the accuracy of information. This review identified the ophthalmic applications and potential usages which need further exploration. With the advancement of LLMs, setting standards for benchmarking and promoting best practices is crucial. Potential clinical deployment requires the evaluation of these LLMs to move away from artificial settings, delve into clinical trials and determine their usefulness in the real world.
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Affiliation(s)
- Sayantan Biswas
- School of Optometry, College of Health and Life Sciences, Aston University, Birmingham, UK
| | - Leon N Davies
- School of Optometry, College of Health and Life Sciences, Aston University, Birmingham, UK
| | - Amy L Sheppard
- School of Optometry, College of Health and Life Sciences, Aston University, Birmingham, UK
| | - Nicola S Logan
- School of Optometry, College of Health and Life Sciences, Aston University, Birmingham, UK
| | - James S Wolffsohn
- School of Optometry, College of Health and Life Sciences, Aston University, Birmingham, UK
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246
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Role of artificial intelligence in neuromuscular and electrodiagnostic medicine. Muscle Nerve 2024; 69:523-526. [PMID: 38488281 DOI: 10.1002/mus.28074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 04/07/2024]
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247
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Kıyak YS, Coşkun Ö, Budakoğlu Iİ, Uluoğlu C. ChatGPT for generating multiple-choice questions: Evidence on the use of artificial intelligence in automatic item generation for a rational pharmacotherapy exam. Eur J Clin Pharmacol 2024; 80:729-735. [PMID: 38353690 DOI: 10.1007/s00228-024-03649-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/03/2024] [Indexed: 04/09/2024]
Abstract
PURPOSE Artificial intelligence, specifically large language models such as ChatGPT, offers valuable potential benefits in question (item) writing. This study aimed to determine the feasibility of generating case-based multiple-choice questions using ChatGPT in terms of item difficulty and discrimination levels. METHODS This study involved 99 fourth-year medical students who participated in a rational pharmacotherapy clerkship carried out based-on the WHO 6-Step Model. In response to a prompt that we provided, ChatGPT generated ten case-based multiple-choice questions on hypertension. Following an expert panel, two of these multiple-choice questions were incorporated into a medical school exam without making any changes in the questions. Based on the administration of the test, we evaluated their psychometric properties, including item difficulty, item discrimination (point-biserial correlation), and functionality of the options. RESULTS Both questions exhibited acceptable levels of point-biserial correlation, which is higher than the threshold of 0.30 (0.41 and 0.39). However, one question had three non-functional options (options chosen by fewer than 5% of the exam participants) while the other question had none. CONCLUSIONS The findings showed that the questions can effectively differentiate between students who perform at high and low levels, which also point out the potential of ChatGPT as an artificial intelligence tool in test development. Future studies may use the prompt to generate items in order for enhancing the external validity of the results by gathering data from diverse institutions and settings.
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Affiliation(s)
- Yavuz Selim Kıyak
- Department of Medical Education and Informatics, Faculty of Medicine, Gazi University, Ankara, Turkey.
- Gazi Üniversitesi Hastanesi E Blok 9, Kat 06500 Beşevler, Ankara, Turkey.
| | - Özlem Coşkun
- Department of Medical Education and Informatics, Faculty of Medicine, Gazi University, Ankara, Turkey
| | - Işıl İrem Budakoğlu
- Department of Medical Education and Informatics, Faculty of Medicine, Gazi University, Ankara, Turkey
| | - Canan Uluoğlu
- Department of Medical Pharmacology, Faculty of Medicine, Gazi University, Ankara, Turkey
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248
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Vargas-Cardona HD, Rodriguez-Lopez M, Arrivillaga M, Vergara-Sanchez C, García-Cifuentes JP, Bermúdez PC, Jaramillo-Botero A. Artificial intelligence for cervical cancer screening: Scoping review, 2009-2022. Int J Gynaecol Obstet 2024; 165:566-578. [PMID: 37811597 DOI: 10.1002/ijgo.15179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/04/2023] [Accepted: 09/20/2023] [Indexed: 10/10/2023]
Abstract
BACKGROUND The intersection of artificial intelligence (AI) with cancer research is increasing, and many of the advances have focused on the analysis of cancer images. OBJECTIVES To describe and synthesize the literature on the diagnostic accuracy of AI in early imaging diagnosis of cervical cancer following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). SEARCH STRATEGY Arksey and O'Malley methodology was used and PubMed, Scopus, and Google Scholar databases were searched using a combination of English and Spanish keywords. SELECTION CRITERIA Identified titles and abstracts were screened to select original reports and cross-checked for overlap of cases. DATA COLLECTION AND ANALYSIS A descriptive summary was organized by the AI algorithm used, total of images analyzed, data source, clinical comparison criteria, and diagnosis performance. MAIN RESULTS We identified 32 studies published between 2009 and 2022. The primary sources of images were digital colposcopy, cervicography, and mobile devices. The machine learning/deep learning (DL) algorithms applied in the articles included support vector machine (SVM), random forest classifier, k-nearest neighbors, multilayer perceptron, C4.5, Naïve Bayes, AdaBoost, XGboots, conditional random fields, Bayes classifier, convolutional neural network (CNN; and variations), ResNet (several versions), YOLO+EfficientNetB0, and visual geometry group (VGG; several versions). SVM and DL methods (CNN, ResNet, VGG) showed the best diagnostic performances, with an accuracy of over 97%. CONCLUSION We concluded that the use of AI for cervical cancer screening has increased over the years, and some results (mainly from DL) are very promising. However, further research is necessary to validate these findings.
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Affiliation(s)
| | - Mérida Rodriguez-Lopez
- Faculty of Health Sciences, Universidad Icesi, Cali, Colombia
- Fundación Valle del Lili, Centro de Investigaciones Clínicas, Cali, Colombia
| | | | | | | | | | - Andres Jaramillo-Botero
- OMICAS Research Institute (iOMICAS), Pontificia Universidad Javeriana Cali, Cali, Colombia
- Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, USA
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249
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Alter IL, Chan K, Lechien J, Rameau A. An introduction to machine learning and generative artificial intelligence for otolaryngologists-head and neck surgeons: a narrative review. Eur Arch Otorhinolaryngol 2024; 281:2723-2731. [PMID: 38393353 DOI: 10.1007/s00405-024-08512-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 01/25/2024] [Indexed: 02/25/2024]
Abstract
PURPOSE Despite the robust expansion of research surrounding artificial intelligence (AI) and machine learning (ML) and their applications to medicine, these methodologies often remain opaque and inaccessible to many otolaryngologists. Especially, with the increasing ubiquity of large-language models (LLMs), such as ChatGPT and their potential implementation in clinical practice, clinicians may benefit from a baseline understanding of some aspects of AI. In this narrative review, we seek to clarify underlying concepts, illustrate applications to otolaryngology, and highlight future directions and limitations of these tools. METHODS Recent literature regarding AI principles and otolaryngologic applications of ML and LLMs was reviewed via search in PubMed and Google Scholar. RESULTS Significant recent strides have been made in otolaryngology research utilizing AI and ML, across all subspecialties, including neurotology, head and neck oncology, laryngology, rhinology, and sleep surgery. Potential applications suggested by recent publications include screening and diagnosis, predictive tools, clinical decision support, and clinical workflow improvement via LLMs. Ongoing concerns regarding AI in medicine include ethical concerns around bias and data sharing, as well as the "black box" problem and limitations in explainability. CONCLUSIONS Potential implementations of AI in otolaryngology are rapidly expanding. While implementation in clinical practice remains theoretical for most of these tools, their potential power to influence the practice of otolaryngology is substantial. LEVEL OF EVIDENCE: 4
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Affiliation(s)
- Isaac L Alter
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, 240 E 59 St, New York, NY, 10022, USA
| | - Karly Chan
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, 240 E 59 St, New York, NY, 10022, USA
| | - Jérome Lechien
- Department of Otorhinolaryngology, Head and Neck Surgery, Hôpital Foch, School of Medicine, UFR Simone Veil, Université Versailles Saint-Quentin-en-Yvelines (Paris Saclay University), Paris, France
- Department of Human Anatomy and Experimental Oncology, Faculty of Medicine, UMONS Research Institute for Health and Sciences Technology, University of Mons (UMons), Mons, Belgium
| | - Anaïs Rameau
- Department of Otolaryngology-Head and Neck Surgery, Sean Parker Institute for the Voice, Weill Cornell Medical College, 240 E 59 St, New York, NY, 10022, USA.
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250
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van Diest PJ, Flach RN, van Dooijeweert C, Makineli S, Breimer GE, Stathonikos N, Pham P, Nguyen TQ, Veta M. Pros and cons of artificial intelligence implementation in diagnostic pathology. Histopathology 2024; 84:924-934. [PMID: 38433288 DOI: 10.1111/his.15153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/29/2023] [Accepted: 01/19/2024] [Indexed: 03/05/2024]
Abstract
The rapid introduction of digital pathology has greatly facilitated development of artificial intelligence (AI) models in pathology that have shown great promise in assisting morphological diagnostics and quantitation of therapeutic targets. We are now at a tipping point where companies have started to bring algorithms to the market, and questions arise whether the pathology community is ready to implement AI in routine workflow. However, concerns also arise about the use of AI in pathology. This article reviews the pros and cons of introducing AI in diagnostic pathology.
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Affiliation(s)
- Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Rachel N Flach
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Oncological Urology, University Medical Center Utrecht, Utrecht, the Netherlands
| | | | - Seher Makineli
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Surgical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Gerben E Breimer
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Nikolas Stathonikos
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Paul Pham
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Tri Q Nguyen
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mitko Veta
- Department of Oncological Urology, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
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