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Guo Y, Sun L, Zhong W, Zhang N, Zhao Z, Tian W. Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges. Neural Regen Res 2024; 19:663-670. [PMID: 37721299 PMCID: PMC10581578 DOI: 10.4103/1673-5374.380909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 05/20/2023] [Accepted: 06/13/2023] [Indexed: 09/19/2023] Open
Abstract
Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury. Specifically, it can be used to analyze and process data regarding peripheral nerve injury and repair, while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms. To investigate advances in the use of artificial intelligence in the diagnosis, rehabilitation, and scientific examination of peripheral nerve injury, we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994-2023. We identified the following research hotspots in peripheral nerve injury and repair: (1) diagnosis, classification, and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques, such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy; (2) motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms, such as wearable devices and assisted wheelchair systems; (3) improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning, such as implantable peripheral nerve interfaces; (4) the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility, enabling them to control devices such as networked hand prostheses; (5) artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation, thereby reducing surgical risk and complications, and facilitating postoperative recovery. Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair, there are some limitations to this technology, such as the consequences of missing or imbalanced data, low data accuracy and reproducibility, and ethical issues (e.g., privacy, data security, research transparency). Future research should address the issue of data collection, as large-scale, high-quality clinical datasets are required to establish effective artificial intelligence models. Multimodal data processing is also necessary, along with interdisciplinary collaboration, medical-industrial integration, and multicenter, large-sample clinical studies.
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Affiliation(s)
- Yang Guo
- Department of Hand Surgery, Beijing Jishuitan Hospital, Beijing, China
| | - Liying Sun
- Department of Hand Surgery, Beijing Jishuitan Hospital, Beijing, China
| | - Wenyao Zhong
- Department of Hand Surgery, Beijing Jishuitan Hospital, Beijing, China
| | - Nan Zhang
- Department of Hand Surgery, Beijing Jishuitan Hospital, Beijing, China
| | - Zongxuan Zhao
- Department of Hand Surgery, Beijing Jishuitan Hospital, Beijing, China
| | - Wen Tian
- Department of Hand Surgery, Beijing Jishuitan Hospital, Beijing, China
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Nishitani T, Mitoh Y, Yajima T, Tachiya D, Hoshika T, Fukunaga T, Nishitani Y, Yoshida R, Mizoguchi I, Ichikawa H, Sato T. Distribution of alpha-synuclein in rat salivary glands. Anat Rec (Hoboken) 2024. [PMID: 38284507 DOI: 10.1002/ar.25395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/09/2024] [Accepted: 01/10/2024] [Indexed: 01/30/2024]
Abstract
Expression of alpha-synuclein (Syn), a presynaptic neuronal protein, was immunohistochemically examined in intact rat submandibular, sublingual, and lingual glands. The submandibular gland contained abundant periductal Syn-immunoreactive (-ir) nerve fibers. Abundant Syn-ir varicosities were present in acini of the sublingual and serous lingual glands. By confocal laser scanning microscopy, Syn-ir nerve fibers around smooth muscle actin (SMA)-ir cells alone were infrequent; however, those around aquaporin-5 (AQP5)-ir cells alone and both SMA- and AQP5-ir cells were abundant in the sublingual and serous lingual glands. SMA-ir cells were occasionally immunoreactive for toll-like receptor 4, a Syn receptor. Syn-ir nerve fibers contained tyrosine hydroxylase (TH) in the submandibular gland and choline acetyltransferase (ChAT) in all examined salivary glands. In the superior cervical (SCG), submandibular, and intralingual ganglia, sympathetic and parasympathetic neurons co-expressed Syn with TH and ChAT, respectively. SCG neurons innervating the submandibular gland contained mostly Syn. In the thoracic spinal cord, 14.7% of ChAT-ir preganglionic sympathetic neurons co-expressed Syn. In the superior salivatory nucleus, preganglionic parasympathetic neurons projecting to the lingual nerve co-expressed Syn and ChAT. The present findings indicate that released Syn acts on myoepithelial cells. Syn in pre- and post-ganglionic neurons may regulate neurotransmitter release and salivary volume and composition.
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Affiliation(s)
- Tomiko Nishitani
- Divisions of Oral and Craniofacial Anatomy, Graduate School of Dentistry, Tohoku University, Sendai, Japan
- Department of Restorative Dentistry and Endodontology, Research Field in Dentistry, Medical and Dental Sciences Area, Research and Education Assembly, Kagoshima University, Kagoshima, Japan
| | - Yoshihiro Mitoh
- Department of Oral Physiology, Okayama University Graduate School of Medicine and Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Takehiro Yajima
- Divisions of Oral and Craniofacial Anatomy, Graduate School of Dentistry, Tohoku University, Sendai, Japan
| | - Daisuke Tachiya
- Divisions of Oral and Craniofacial Anatomy, Graduate School of Dentistry, Tohoku University, Sendai, Japan
| | - Tomohiro Hoshika
- Department of Restorative Dentistry and Endodontology, Research Field in Dentistry, Medical and Dental Sciences Area, Research and Education Assembly, Kagoshima University, Kagoshima, Japan
| | - Tomohiro Fukunaga
- Division of Orthodontics and Dentofacial Orthopedics, Graduate School of Dentistry, Tohoku University, Sendai, Japan
| | - Yoshihiro Nishitani
- Department of Restorative Dentistry and Endodontology, Research Field in Dentistry, Medical and Dental Sciences Area, Research and Education Assembly, Kagoshima University, Kagoshima, Japan
| | - Ryusuke Yoshida
- Department of Oral Physiology, Okayama University Graduate School of Medicine and Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Itaru Mizoguchi
- Division of Orthodontics and Dentofacial Orthopedics, Graduate School of Dentistry, Tohoku University, Sendai, Japan
| | - Hiroyuki Ichikawa
- Divisions of Oral and Craniofacial Anatomy, Graduate School of Dentistry, Tohoku University, Sendai, Japan
| | - Tadasu Sato
- Divisions of Oral and Craniofacial Anatomy, Graduate School of Dentistry, Tohoku University, Sendai, Japan
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Mohamed AA, Marques O. Diagnostic Efficacy and Clinical Relevance of Artificial Intelligence in Detecting Cognitive Decline. Cureus 2023; 15:e47004. [PMID: 37965412 PMCID: PMC10641267 DOI: 10.7759/cureus.47004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2023] [Indexed: 11/16/2023] Open
Abstract
Cognitive impairment is an age-associated disorder of increasing prevalence as the aging population continues to grow. Classified based on the level of cognitive decline, memory, function, and capacity to conduct activities of daily living, cognitive impairment ranges from mild cognitive impairment to dementia. When considering the insidious nature of the etiologies responsible for varying degrees of cognitive impairment, early diagnosis may provide a clinical benefit through the facilitation of early treatment. Typical diagnosis relies heavily on evaluation in a primary care setting. However, there is evidence that other diagnostic tools may aid in an earlier diagnosis of the different underlying pathologies responsible for cognitive impairment. Artificial intelligence represents a new intersecting field with healthcare that may aid in the early detection of neurodegenerative disorders. When assessing the role of AI in detecting cognitive decline, it is important to consider both the diagnostic efficacy of AI algorithms and the clinical relevance and impact of early interventions as a result of early detection. Thus, this review highlights promising investigations and developments in the space of artificial intelligence and healthcare and their potential to impact patient outcomes.
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Affiliation(s)
- Ali A Mohamed
- Neurological Surgery, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, USA
- Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, USA
| | - Oge Marques
- Biomedical Sciences, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, USA
- Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, USA
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Krishnan G, Singh S, Pathania M, Gosavi S, Abhishek S, Parchani A, Dhar M. Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm. Front Artif Intell 2023; 6:1227091. [PMID: 37705603 PMCID: PMC10497111 DOI: 10.3389/frai.2023.1227091] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/09/2023] [Indexed: 09/15/2023] Open
Abstract
As the demand for quality healthcare increases, healthcare systems worldwide are grappling with time constraints and excessive workloads, which can compromise the quality of patient care. Artificial intelligence (AI) has emerged as a powerful tool in clinical medicine, revolutionizing various aspects of patient care and medical research. The integration of AI in clinical medicine has not only improved diagnostic accuracy and treatment outcomes, but also contributed to more efficient healthcare delivery, reduced costs, and facilitated better patient experiences. This review article provides an extensive overview of AI applications in history taking, clinical examination, imaging, therapeutics, prognosis and research. Furthermore, it highlights the critical role AI has played in transforming healthcare in developing nations.
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Affiliation(s)
- Gokul Krishnan
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Shiana Singh
- Department of Emergency Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Monika Pathania
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Siddharth Gosavi
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Shuchi Abhishek
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Ashwin Parchani
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Minakshi Dhar
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
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Scalco R, Hamsafar Y, White CL, Schneider JA, Reichard RR, Prokop S, Perrin RJ, Nelson PT, Mooney S, Lieberman AP, Kukull WA, Kofler J, Keene CD, Kapasi A, Irwin DJ, Gutman DA, Flanagan ME, Crary JF, Chan KC, Murray ME, Dugger BN. The status of digital pathology and associated infrastructure within Alzheimer's Disease Centers. J Neuropathol Exp Neurol 2023; 82:202-211. [PMID: 36692179 PMCID: PMC9941826 DOI: 10.1093/jnen/nlac127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Digital pathology (DP) has transformative potential, especially for Alzheimer disease and related disorders. However, infrastructure barriers may limit adoption. To provide benchmarks and insights into implementation barriers, a survey was conducted in 2019 within National Institutes of Health's Alzheimer's Disease Centers (ADCs). Questions covered infrastructure, funding sources, and data management related to digital pathology. Of the 35 ADCs to which the survey was sent, 33 responded. Most respondents (81%) stated that their ADC had digital slide scanner access, with the most frequent brand being Aperio/Leica (62.9%). Approximately a third of respondents stated there were fees to utilize the scanner. For DP and machine learning (ML) resources, 41% of respondents stated none was supported by their ADC. For scanner purchasing and operations, 50% of respondents stated they received institutional support. Some were unsure of the file size of scanned digital images (37%) and total amount of storage space files occupied (50%). Most (76%) were aware of other departments at their institution working with ML; a similar (76%) percentage were unaware of multiuniversity or industry partnerships. These results demonstrate many ADCs have access to a digital slide scanner; additional investigations are needed to further understand hurdles to implement DP and ML workflows.
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Affiliation(s)
- Rebeca Scalco
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
| | - Yamah Hamsafar
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
| | - Charles L White
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | | | | | - Stefan Prokop
- Department of Pathology, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Richard J Perrin
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, Missouri, USA
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, USA
- Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, Saint Louis, Missouri, USA
| | | | - Sean Mooney
- Institute for Medical Data Science and Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Andrew P Lieberman
- Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Walter A Kukull
- Institute for Medical Data Science and Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Julia Kofler
- Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Christopher Dirk Keene
- Department Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
| | | | - David J Irwin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David A Gutman
- Departments of Neurology, Psychiatry, and Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Margaret E Flanagan
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - John F Crary
- Department of Pathology, Ronald M. Loeb Center for Alzheimer’s Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Neuroscience, Ronald M. Loeb Center for Alzheimer’s Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Artificial Intelligence & Human Health, Ronald M. Loeb Center for Alzheimer’s Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kwun C Chan
- Institute for Medical Data Science and Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Brittany N Dugger
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
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Marx GA, Koenigsberg DG, McKenzie AT, Kauffman J, Hanson RW, Whitney K, Signaevsky M, Prastawa M, Iida MA, White CL, Walker JM, Richardson TE, Koll J, Fernandez G, Zeineh J, Cordon-Cardo C, Crary JF, Farrell K. Artificial intelligence-derived neurofibrillary tangle burden is associated with antemortem cognitive impairment. Acta Neuropathol Commun 2022; 10:157. [PMID: 36316708 PMCID: PMC9620665 DOI: 10.1186/s40478-022-01457-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 10/06/2022] [Indexed: 11/10/2022] Open
Abstract
Tauopathies are a category of neurodegenerative diseases characterized by the presence of abnormal tau protein-containing neurofibrillary tangles (NFTs). NFTs are universally observed in aging, occurring with or without the concomitant accumulation of amyloid-beta peptide (Aβ) in plaques that typifies Alzheimer disease (AD), the most common tauopathy. Primary age-related tauopathy (PART) is an Aβ-independent process that affects the medial temporal lobe in both cognitively normal and impaired subjects. Determinants of symptomology in subjects with PART are poorly understood and require clinicopathologic correlation; however, classical approaches to staging tau pathology have limited quantitative reproducibility. As such, there is a critical need for unbiased methods to quantitatively analyze tau pathology on the histological level. Artificial intelligence (AI)-based convolutional neural networks (CNNs) generate highly accurate and precise computer vision assessments of digitized pathology slides, yielding novel histology metrics at scale. Here, we performed a retrospective autopsy study of a large cohort (n = 706) of human post-mortem brain tissues from normal and cognitively impaired elderly individuals with mild or no Aβ plaques (average age of death of 83.1 yr, range 55-110). We utilized a CNN trained to segment NFTs on hippocampus sections immunohistochemically stained with antisera recognizing abnormal hyperphosphorylated tau (p-tau), which yielded metrics of regional NFT counts, NFT positive pixel density, as well as a novel graph-theory based metric measuring the spatial distribution of NFTs. We found that several AI-derived NFT metrics significantly predicted the presence of cognitive impairment in both the hippocampus proper and entorhinal cortex (p < 0.0001). When controlling for age, AI-derived NFT counts still significantly predicted the presence of cognitive impairment (p = 0.04 in the entorhinal cortex; p = 0.04 overall). In contrast, Braak stage did not predict cognitive impairment in either age-adjusted or unadjusted models. These findings support the hypothesis that NFT burden correlates with cognitive impairment in PART. Furthermore, our analysis strongly suggests that AI-derived metrics of tau pathology provide a powerful tool that can deepen our understanding of the role of neurofibrillary degeneration in cognitive impairment.
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Affiliation(s)
- Gabriel A Marx
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Daniel G Koenigsberg
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Andrew T McKenzie
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Justin Kauffman
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Russell W Hanson
- New York University McSilver Institute for Poverty Policy and Research, New York, NY, USA
| | - Kristen Whitney
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Maxim Signaevsky
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
- Center for Computational and Systems Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marcel Prastawa
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
- Center for Computational and Systems Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Megan A Iida
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA
| | - Charles L White
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jamie M Walker
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
| | - Timothy E Richardson
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
| | - John Koll
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
- Center for Computational and Systems Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gerardo Fernandez
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
- Center for Computational and Systems Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jack Zeineh
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
- Center for Computational and Systems Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Carlos Cordon-Cardo
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA
- Center for Computational and Systems Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John F Crary
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA.
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA.
| | - Kurt Farrell
- Department of Pathology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA.
- Department of Artificial Intelligence and Human Health, Nash Family Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Neuropathology Brain Bank and Research CoRE, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1194, New York, NY, 10029, USA.
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Adler-Milstein J, Aggarwal N, Ahmed M, Castner J, Evans BJ, Gonzalez AA, James CA, Lin S, Mandl KD, Matheny ME, Sendak MP, Shachar C, Williams A. Meeting the Moment: Addressing Barriers and Facilitating Clinical Adoption of Artificial Intelligence in Medical Diagnosis. NAM Perspect 2022; 2022:202209c. [PMID: 36713769 PMCID: PMC9875857 DOI: 10.31478/202209c] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Battineni G, Chintalapudi N, Hossain MA, Losco G, Ruocco C, Sagaro GG, Traini E, Nittari G, Amenta F. Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review. Bioengineering (Basel) 2022; 9:bioengineering9080370. [PMID: 36004895 PMCID: PMC9405227 DOI: 10.3390/bioengineering9080370] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/25/2022] [Accepted: 08/02/2022] [Indexed: 11/16/2022] Open
Abstract
Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle–Ottawa Scale (NOS) rating. Only papers with an NOS score 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer’s disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age.
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Affiliation(s)
- Gopi Battineni
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
- Correspondence: ; Tel.: +39-3331728206
| | - Nalini Chintalapudi
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Mohammad Amran Hossain
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Giuseppe Losco
- School of Architecture and Design, University of Camerino, 63100 Ascoli Piceno, Italy
| | - Ciro Ruocco
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Getu Gamo Sagaro
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Enea Traini
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Giulio Nittari
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Francesco Amenta
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
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