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Lazarus MD, Truong M, Douglas P, Selwyn N. Artificial intelligence and clinical anatomical education: Promises and perils. ANATOMICAL SCIENCES EDUCATION 2024; 17:249-262. [PMID: 36030525 DOI: 10.1002/ase.2221] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 07/14/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
Anatomy educators are often at the forefront of adopting innovative and advanced technologies for teaching, such as artificial intelligence (AI). While AI offers potential new opportunities for anatomical education, hard lessons learned from the deployment of AI tools in other domains (e.g., criminal justice, healthcare, and finance) suggest that these opportunities are likely to be tempered by disadvantages for at least some learners and within certain educational contexts. From the perspectives of an anatomy educator, public health researcher, medical ethicist, and an educational technology expert, this article examines five tensions between the promises and the perils of integrating AI into anatomy education. These tensions highlight the ways in which AI is currently ill-suited for incorporating the uncertainties intrinsic to anatomy education in the areas of (1) human variations, (2) healthcare practice, (3) diversity and social justice, (4) student support, and (5) student learning. Practical recommendations for a considered approach to working alongside AI in the contemporary (and future) anatomy education learning environment are provided, including enhanced transparency about how AI is integrated, AI developer diversity, inclusion of uncertainty and anatomical variations within deployed AI, provisions made for educator awareness of AI benefits and limitations, building in curricular "AI-free" time, and engaging AI to extend human capacities. These recommendations serve as a guiding framework for how the clinical anatomy discipline, and anatomy educators, can work alongside AI, and develop a more nuanced and considered approach to the role of AI in healthcare education.
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Affiliation(s)
- Michelle D Lazarus
- Centre for Human Anatomy Education (CHAE), Department of Anatomy and Developmental Biology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
- Monash Centre for Scholarship in Health Education (MCSHE), Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Mandy Truong
- Monash Nursing and Midwifery, Faculty of Medicine Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
- Menzies School of Health Research, Darwin, Northern Territory, Australia
| | - Peter Douglas
- Monash Bioethics Centre, Faculty of Arts, Monash University, Clayton, Victoria, Australia
| | - Neil Selwyn
- Monash Data Futures Institute, Monash University, Clayton, Victoria, Australia
- Faculty of Education, Monash University, Clayton, Victoria, Australia
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Matuck B, Ferraz da Silva LF, Warner BM, Byrd KM. The need for integrated research autopsies in the era of precision oral medicine. J Am Dent Assoc 2023; 154:194-205. [PMID: 36710158 PMCID: PMC9974796 DOI: 10.1016/j.adaj.2022.11.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 11/18/2022] [Accepted: 11/20/2022] [Indexed: 01/29/2023]
Abstract
BACKGROUND Autopsy has benefited the practice of medicine for centuries; however, its use to advance the practice of oral health care is relatively limited. In the era of precision oral medicine, the research autopsy is poised to play an important role in understanding oral-systemic health, including infectious disease, autoimmunity, craniofacial genetics, and cancer. TYPES OF STUDIES REVIEWED The authors reviewed relevant articles that used medical and dental research autopsies to summarize the advantages of minimally invasive autopsies of dental, oral, and craniofacial tissues and to outline practices for supporting research autopsies of the oral and craniofacial complex. RESULTS The authors provide a historical summary of research autopsy in dentistry and provide a perspective on the value of autopsies for high-resolution multiomic studies to benefit precision oral medicine. As the promise of high-resolution multiomics is being realized, there is a need to integrate the oral and craniofacial complex into the practice of autopsy in medicine. Furthermore, the collaboration of autopsy centers with researchers will accelerate the understanding of dental, oral, and craniofacial tissues as part of the whole body. CONCLUSIONS Autopsies must integrate oral and craniofacial tissues as part of biobanking procedures. As new technologies allow for high-resolution, multimodal phenotyping of human samples, using optimized sampling procedures will allow for unprecedented understanding of common and rare dental, oral, and craniofacial diseases in the future. PRACTICAL IMPLICATIONS The COVID-19 pandemic highlighted the oral cavity as a site for viral infection and transmission potential; this was only discovered via clinical autopsies. The realization of the integrated autopsy's value in full body health initiatives will benefit patients across the globe.
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Affiliation(s)
- Bruno Matuck
- Department of Pathology, School of Medicine University of São Paulo, São Paulo, Brazil
- Division of Oral and Craniofacial Health Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Blake M. Warner
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, Maryland, USA
| | - Kevin Matthew Byrd
- Salivary Disorders Unit, National Institute of Dental and Craniofacial Research, National Institutes of Health, Bethesda, Maryland, USA
- Division of Oral and Craniofacial Health Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lab of Oral & Craniofacial Innovation (LOCI), Department of Innovation and Technology Research, ADA Science & Research Institute, Gaithersburg, MD, USA
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Liu YJ, Chen CW, Cheng KY, Juan CJ. Editorial for "Post-Mortem MR Relaxometry of In Utero Fetuses and Its Relationship With Post-Mortem Interval; a Multi-Organ Observational Study on Reduced Fetuses of Complicated Multiple Pregnancies". J Magn Reson Imaging 2023; 57:962-963. [PMID: 35950610 DOI: 10.1002/jmri.28387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 04/28/2022] [Indexed: 11/10/2022] Open
Affiliation(s)
- Yi-Jui Liu
- Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan.,Master's Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan
| | - Chun-Wen Chen
- Department of Medical Imaging and Radiological Sciences, Central Taiwan University of Science and Technology, Taichung, Taiwan.,Department of Radiology, Taichung Armed Forces General Hospital, Taichung, Taiwan.,Department of Radiology, School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Kai-Yuan Cheng
- Department of Medical Imaging and Radiological Sciences, Central Taiwan University of Science and Technology, Taichung, Taiwan
| | - Chun-Jung Juan
- Department of Radiology, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Medical Imaging, China Medical University Hsinchu Hospital, Hsinchu, Taiwan.,Department of Medical Imaging, China Medical University Hospital, Taichung, Taiwan
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Yuan HX, Wang C, Tang CY, You QQ, Zhang Q, Wang WP. Differential diagnosis of gallbladder neoplastic polyps and cholesterol polyps with radiomics of dual modal ultrasound: a pilot study. BMC Med Imaging 2023; 23:26. [PMID: 36747143 PMCID: PMC9901123 DOI: 10.1186/s12880-023-00982-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 02/01/2023] [Indexed: 02/08/2023] Open
Abstract
PURPOSE To verify whether radiomics techniques based on dual-modality ultrasound consisting of B-mode and superb microvascular imaging (SMI) can improve the accuracy of the differentiation between gallbladder neoplastic polyps and cholesterol polyps. METHODS A total of 100 patients with 100 pathologically proven gallbladder polypoid lesions were enrolled in this retrospective study. Radiomics features on B-mode ultrasound and SMI of each lesion were extracted. Support vector machine was used to classify adenomas and cholesterol polyps of gallbladder for B-mode, SMI and dual-modality ultrasound, respectively, and the classification results were compared among the three groups. RESULTS Six, eight and nine features were extracted for each lesion at B-mode ultrasound, SMI and dual-modality ultrasound, respectively. In dual-modality ultrasound model, the area under the receiver operating characteristic curve (AUC), classification accuracy, sensitivity, specificity, and Youden's index were 0.850 ± 0.090, 0.828 ± 0.097, 0.892 ± 0.144, 0.803 ± 0.149 and 0.695 ± 0.157, respectively. The AUC and Youden's index of the dual-modality model were higher than those of the B-mode model (p < 0.05). The AUC, accuracy, specificity and Youden's index of the dual-modality model were higher than those of the SMI model (p < 0.05). CONCLUSIONS Radiomics analysis of the dual-modality ultrasound composed of B-mode and SMI can improve the accuracy of classification between gallbladder neoplastic polyps and cholesterol polyps.
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Affiliation(s)
- Hai-xia Yuan
- grid.413087.90000 0004 1755 3939Department of Ultrasound, Zhongshan Hospital of Fudan University (Qingpu Branch), Shanghai, China ,grid.413087.90000 0004 1755 3939Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443Department of Ultrasound, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, Fujian Province China
| | - Changyan Wang
- grid.39436.3b0000 0001 2323 5732School of Communication and Information Engineering, Shanghai University, Shanghai, 200444 China ,grid.39436.3b0000 0001 2323 5732The SMART (Smart Medicine and AI-Based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Cong-yu Tang
- grid.413087.90000 0004 1755 3939Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China
| | - Qi-qin You
- grid.413087.90000 0004 1755 3939Department of Ultrasound, Zhongshan Hospital of Fudan University (Qingpu Branch), Shanghai, China
| | - Qi Zhang
- School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China. .,The SMART (Smart Medicine and AI-Based Radiology Technology) Lab, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
| | - Wen-ping Wang
- grid.413087.90000 0004 1755 3939Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China
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Cadaveric skull and tissue cutting manipulator: autopsy equipment that provides safety against airborne infection and COVID-19. RESEARCH ON BIOMEDICAL ENGINEERING 2022. [PMCID: PMC7568460 DOI: 10.1007/s42600-020-00104-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Ingalhalikar M, Shinde S, Karmarkar A, Rajan A, Rangaprakash D, Deshpande G. Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset. IEEE Trans Biomed Eng 2021; 68:3628-3637. [PMID: 33989150 PMCID: PMC8696194 DOI: 10.1109/tbme.2021.3080259] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The larger sample sizes available from multi-site publicly available neuroimaging data repositories makes machine-learning based diagnostic classification of mental disorders more feasible by alleviating the curse of dimensionality. However, since multi-site data are aggregated post-hoc, i.e. they were acquired from different scanners with different acquisition parameters, non-neural inter-site variability may mask inter-group differences that are at least in part neural in origin. Hence, the advantages gained by the larger sample size in the context of machine-learning based diagnostic classification may not be realized. METHODS We address this issue using harmonization of multi-site neuroimaging data using the ComBat technique, which is based on an empirical Bayes formulation to remove inter-site differences in data distributions, to improve diagnostic classification accuracy. Specifically, we demonstrate this using ABIDE (Autism Brain Imaging Data Exchange) multi-site data for classifying individuals with Autism from healthy controls using resting state fMRI-based functional connectivity data. RESULTS Our results show that higher classification accuracies across multiple classification models can be obtained (especially for models based on artificial neural networks) from multi-site data post harmonization with the ComBat technique as compared to without harmonization, outperforming earlier results from existing studies using ABIDE. Furthermore, our network ablation analysis facilitated important insights into autism spectrum disorder pathology and the connectivity in networks shown to be important for classification covaried with verbal communication impairments in Autism. CONCLUSION Multi-site data harmonization using ComBat improves neuroimaging-based diagnostic classification of mental disorders. SIGNIFICANCE ComBat has the potential to make AI-based clinical decision-support systems more feasible in psychiatry.
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The Impact of Tumor Edema on T2-Weighted 3T-MRI Invasive Breast Cancer Histological Characterization: A Pilot Radiomics Study. Cancers (Basel) 2021; 13:cancers13184635. [PMID: 34572862 PMCID: PMC8464682 DOI: 10.3390/cancers13184635] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 09/11/2021] [Accepted: 09/14/2021] [Indexed: 12/24/2022] Open
Abstract
Simple Summary Breast cancer is the most common cancer in women worldwide. Currently the use of MR is mandatory in staging phase. The standard protocol includes T2-weighted sequences for morphology and signal analysis, T1-weighted images for adding information (i.e., ematic or adipous components), diffusion-weighted sequences which provide information on tissue cellularity, and dynamic post-contrast sequences useful for detecting and locating lesions. Although not considered among the main prognostic factors in current guidelines, tumor-associated edema provides useful information on tumor aggressiveness, and has been shown to be associated with the main histological tumor characteristics. With this work, entitled “The Impact of Tumor Edema on T2-weighted 3T-MRI Invasive Breast Cancer Histological Characterization: a Pilot Radiomics Study”, we want to demonstrate that radiomics edema, based on algorithms that allow the extraction of imaging features not visible to the human eye, can further increase the accuracy in the prediction of histological factors compared to the use of traditional information only. Abstract Background: to evaluate the contribution of edema associated with histological features to the prediction of breast cancer (BC) prognosis using T2-weighted MRI radiomics. Methods: 160 patients who underwent staging 3T-MRI from January 2015 to January 2019, with 164 histologically proven invasive BC lesions, were retrospectively reviewed. Patient data (age, menopausal status, family history, hormone therapy), tumor MRI-features (location, margins, enhancement) and histological features (histological type, grading, ER, PgR, HER2, Ki-67 index) were collected. Of the 160 MRI exams, 120 were considered eligible, corresponding to 127 lesions. T2-MRI were used to identify edema, which was classified in four groups: peritumoral, pre-pectoral, subcutaneous, or diffuse. A semi-automatic segmentation of the edema was performed for each lesion, using 3D Slicer open-source software. Main radiomics features were extracted and selected using a wrapper selection method. A Random Forest type classifier was trained to measure the performance of predicting histological factors using semantic features (patient data and MRI features) alone and semantic features associated with edema radiomics features. Results: edema was absent in 37 lesions and present in 127 (62 peritumoral, 26 pre-pectoral, 16 subcutaneous, 23 diffuse). The AUC-classifier obtained by associating edema radiomics with semantic features was always higher compared to the AUC-classifier obtained from semantic features alone, for all five histological classes prediction (0.645 vs. 0.520 for histological type, 0.789 vs. 0.590 for grading, 0.487 vs. 0.466 for ER, 0.659 vs. 0.546 for PgR, and 0.62 vs. 0.573 for Ki67). Conclusions: radiomic features extracted from tumor edema contribute significantly to predicting tumor histology, increasing the accuracy obtained from the combination of patient clinical characteristics and breast imaging data.
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Verma RK, Pandey M, Chawla P, Choudhury H, Mayuren J, Bhattamisra SK, Gorain B, Raja MAG, Amjad MW, Obaidur Rahman S. An insight into the role of Artificial Intelligence in the early diagnosis of Alzheimer's disease. CNS & NEUROLOGICAL DISORDERS-DRUG TARGETS 2021; 21:901-912. [PMID: 33982657 DOI: 10.2174/1871527320666210512014505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/12/2021] [Accepted: 02/17/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND The complication of Alzheimer's disease (AD) has made the development of its therapeutic a challenging task. Even after decades of research, we have achieved no more than a few years of symptomatic relief. The inability to diagnose the disease early is the foremost hurdle behind its treatment. Several studies have aimed to identify potential biomarkers that can be detected in body fluids (CSF, blood, urine, etc) or assessed by neuroimaging (i.e., PET and MRI). However, the clinical implementation of these biomarkers is incomplete as they cannot be validated. METHOD To overcome the limitation, the use of artificial intelligence along with technical tools has been extensively investigated for AD diagnosis. For developing a promising artificial intelligence strategy that can diagnose AD early, it is critical to supervise neuropsychological outcomes and imaging-based readouts with a proper clinical review. CONCLUSION Profound knowledge, a large data pool, and detailed investigations are required for the successful implementation of this tool. This review will enlighten various aspects of early diagnosis of AD using artificial intelligence.
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Affiliation(s)
- Rohit Kumar Verma
- International Medical University Department of Pharmacy Practice, School of Pharmacy, Malaysia
| | - Manisha Pandey
- Department of Pharmaceutical Technology, School of Pharmacy, International Medical University-Bukit Jalil 57000, Kuala Lumpur, Malaysia School of Pharmacy,, Malaysia
| | - Pooja Chawla
- ISF College of Pharmacy, Moga Pharmaceutical Chemistry, India
| | - Hira Choudhury
- International Medical University Pharmaceutical Technology, Malaysia
| | - Jayashree Mayuren
- School of Pharmacy, International Medical University Department of Pharmaceutical Technology,, Malaysia
| | | | - Bapi Gorain
- Lincoln University College Faculty of Pharmacy, Malaysia
| | | | | | - Syed Obaidur Rahman
- Department of Pharmaceutical Medicine, School of Pharmaceutical Education and Research, Jamia Humdard, New Delhi India Pharmacology, India
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Chen X, Tao X, Wang FL, Xie H. Global research on artificial intelligence-enhanced human electroencephalogram analysis. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05588-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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Yuan HX, Yu QH, Zhang YQ, Yu Q, Zhang Q, Wang WP. Ultrasound Radiomics Effective for Preoperative Identification of True and Pseudo Gallbladder Polyps Based on Spatial and Morphological Features. Front Oncol 2020; 10:1719. [PMID: 33042816 PMCID: PMC7518113 DOI: 10.3389/fonc.2020.01719] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Accepted: 07/31/2020] [Indexed: 12/12/2022] Open
Abstract
Purpose: To explore the value of ultrasound radiomics in the preoperative identification of true and pseudo gallbladder polyps and to evaluate the associated diagnostic accuracy. Methods: Totally, 99 pathologically proven gallbladder polyps in 96 patients were enrolled, including 58 cholesterol polyps (55 patients) and 41 gallbladder tubular adenomas (41 patients). Features on preoperative ultrasound images, including spatial and morphological features, were acquired for each lesion. Following this, two-stage feature selection was adopted using Fisher's inter-intraclass variance ratios and Z-scores for the selection of intrinsic features important for differential diagnosis achievement with support vector machine use. Results: Eighty radiomic features were extracted from each polyp. Eight intrinsic features were identified after two-stage selection. The contrast 14 (Cont14) and entropy 6 (Entr6) values in the cholesterol polyp group were significantly higher than those in the gallbladder adenoma group (4.063 ± 1.682 vs. 2.715 ± 1.867, p < 0.001 for Cont14; 4.712 ± 0.427 vs. 4.380 ± 0.720, p = 0.003 for Entr6); however, the homogeneity 13 (Homo13) and energy 8 (Ener8) values in the cholesterol polyp group were significantly lower (0.500 ± 0.069 vs. 0.572 ± 0.057, p < 0.001 for Homo13; 0.050 ± 0.023 vs. 0.068 ± 0.038, p = 0.002 for Ener8). These results indicate that the pixel distribution of cholesterol polyps was more uneven than that of gallbladder tubular adenomas. The dispersion degree was also significantly lower in the cholesterol polyp group than the gallbladder adenoma group (0.579 ± 0.054 vs. 0.608 ± 0.041, p = 0.005), indicating a lower dispersion of high-intensity areas in the cholesterol polyps. The long axis length of the fitting ellipse (Maj.Len), diameter of a circle equal to the lesion area (Eq.Dia) and perimeter (Per) values in the cholesterol polyp group were significantly lower than those in the gallbladder adenoma group (0.971 ± 0.485 vs. 1.738 ± 0.912, p < 0.001 for Maj.Len; 0.818 ± 0.393 vs. 1.438 ± 0.650, p < 0.001 for Eq.Dia; 2.637 ± 1.281 vs. 5.033 ± 2.353, p < 0.001 for Per), demonstrating that the cholesterol polyps were smaller and more regular in terms of morphology. The classification accuracy, sensitivity, specificity, and area under the curve values were 0.875, 0.885, 0.857, and 0.898, respectively. Conclusions: Ultrasound radiomic analysis based on the spatial and morphological features extracted from ultrasound images effectively contributed to the preoperative diagnosis of true and pseudo gallbladder polyps and may be valuable in their clinical management.
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Affiliation(s)
- Hai-Xia Yuan
- Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China.,Department of Ultrasound, Xiamen Branch, Zhongshan Hospital of Fudan University, Xiamen, China
| | - Qi-Hui Yu
- The SMART (Smart Medicine and AI-based Radiology Technology) Lab, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Yan-Qun Zhang
- Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China.,Department of Ultrasound, Xiamen Branch, Zhongshan Hospital of Fudan University, Xiamen, China
| | - Qing Yu
- Shanghai Institute of Medical Imaging, Shanghai, China
| | - Qi Zhang
- The SMART (Smart Medicine and AI-based Radiology Technology) Lab, School of Communication and Information Engineering, Shanghai University, Shanghai, China.,Hangzhou YITU Healthcare Technology, Hangzhou, China
| | - Wen-Ping Wang
- Department of Ultrasound, Zhongshan Hospital of Fudan University, Shanghai, China.,Department of Ultrasound, Xiamen Branch, Zhongshan Hospital of Fudan University, Xiamen, China.,Shanghai Institute of Medical Imaging, Shanghai, China
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Garland J, Ondruschka B, Stables S, Morrow P, Kesha K, Glenn C, Tse R. Identifying Fatal Head Injuries on Postmortem Computed Tomography Using Convolutional Neural Network/Deep Learning: A Feasibility Study. J Forensic Sci 2020; 65:2019-2022. [PMID: 32639630 DOI: 10.1111/1556-4029.14502] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 06/07/2020] [Accepted: 06/15/2020] [Indexed: 12/27/2022]
Abstract
Postmortem computed tomography (PMCT) is a relatively recent advancement in forensic pathology practice that has been increasingly used as an ancillary investigation and screening tool. One area of clinical CT imaging that has garnered a lot of research interest recently is the area of "artificial intelligence" (AI), such as in screening and computer-assisted diagnostics. This feasibility study investigated the application of convolutional neural network, a form of deep learning AI, to PMCT head imaging in differentiating fatal head injury from controls. PMCT images of a transverse section of the head at the level of the frontal sinus from 25 cases of fatal head injury were combined with 25 nonhead-injury controls and divided into training and testing datasets. A convolutional neural network was constructed using Keras and was trained against the training data before being assessed against the testing dataset. The results of this study demonstrated an accuracy of between 70% and 92.5%, with difficulties in recognizing subarachnoid hemorrhage and in distinguishing congested vessels and prominent falx from head injury. These results are promising for potential applications as a screening tool or in computer-assisted diagnostics in the future.
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Affiliation(s)
- Jack Garland
- Forensic and Analytical Science Service, 480 Weeroona Rd, Lidcombe, NSW, 2141, Australia
| | - Benjamin Ondruschka
- Institute of Legal Medicine, University Medical Center Hamburg-Eppendorf, Martinistraße 52 20251, Hamburg, Germany
| | - Simon Stables
- Department of Forensic Pathology, LabPLUS, Auckland City Hospital, 2 Park Road, Grafton, Auckland, New Zealand, 1023
| | - Paul Morrow
- Department of Forensic Pathology, LabPLUS, Auckland City Hospital, 2 Park Road, Grafton, Auckland, New Zealand, 1023
| | - Kilak Kesha
- Department of Forensic Pathology, LabPLUS, Auckland City Hospital, 2 Park Road, Grafton, Auckland, New Zealand, 1023
| | - Charley Glenn
- Department of Forensic Pathology, LabPLUS, Auckland City Hospital, 2 Park Road, Grafton, Auckland, New Zealand, 1023
| | - Rexson Tse
- Department of Forensic Pathology, LabPLUS, Auckland City Hospital, 2 Park Road, Grafton, Auckland, New Zealand, 1023.,University of Auckland Faculty of Medical and Health Sciences, 85 Park Road, Grafton, Auckland, New Zealand, 1023
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Hu H. Recent Advances of Bioresponsive Nano-Sized Contrast Agents for Ultra-High-Field Magnetic Resonance Imaging. Front Chem 2020; 8:203. [PMID: 32266217 PMCID: PMC7100386 DOI: 10.3389/fchem.2020.00203] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 03/04/2020] [Indexed: 12/11/2022] Open
Abstract
The ultra-high-field magnetic resonance imaging (MRI) nowadays has been receiving enormous attention in both biomaterial research and clinical diagnosis. MRI contrast agents are generally comprising of T1-weighted and T2-weighted contrast agent types, where T1-weighted contrast agents show positive contrast enhancement with brighter images by decreasing the proton's longitudinal relaxation times and T2-weighted contrast agents show negative contrast enhancement with darker images by decreasing the proton's transverse relaxation times. To meet the incredible demand of MRI, ultra-high-field T2 MRI is gradually attracting the attention of research and medical needs owing to its high resolution and high accuracy for detection. It is anticipated that high field MRI contrast agents can achieve high performance in MRI imaging, where parameters of chemical composition, molecular structure and size of varied contrast agents show contrasted influence in each specific diagnostic test. This review firstly presents the recent advances of nanoparticle contrast agents for MRI. Moreover, multimodal molecular imaging with MRI for better monitoring is discussed during biological process. To fasten the process of developing better contrast agents, deep learning of artificial intelligent (AI) can be well-integrated into optimizing the crucial parameters of nanoparticle contrast agents and achieving high resolution MRI prior to the clinical applications. Finally, prospects and challenges are summarized.
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Affiliation(s)
- Hailong Hu
- School of Aeronautics and Astronautics, Central South University, Changsha, China
- Research Center in Intelligent Thermal Structures for Aerospace, Central South University, Changsha, China
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