组织病理学
心肌梗塞
医学
卷积神经网络
病理
心脏病学
放射科
计算机科学
梗塞
人工智能
作者
Jack Garland,Mindy Hu,Michael J. Duffy,Kilak Kesha,Charley Glenn,Paul Morrow,Simon Stables,Benjamin Ondruschka,Ugo Da Broi,Rexson Tse
出处
期刊:American Journal of Forensic Medicine and Pathology
[Ovid Technologies (Wolters Kluwer)]
日期:2021-04-08
卷期号:42 (3): 230-234
被引量:7
标识
DOI:10.1097/paf.0000000000000672
摘要
Abstract Convolutional neural network (CNN) has advanced in recent years and translated from research into medical practice, most notably in clinical radiology and histopathology. Research on CNNs in forensic/postmortem pathology is almost exclusive to postmortem computed tomography despite the wealth of research into CNNs in surgical/anatomical histopathology. This study was carried out to investigate whether CNNs are able to identify and age myocardial infarction (a common example of forensic/postmortem histopathology) from histology slides. As a proof of concept, this study compared 4 CNNs commonly used in surgical/anatomical histopathology to identify normal myocardium from myocardial infarction. A total of 150 images of the myocardium (50 images each for normal myocardium, acute myocardial infarction, and old myocardial infarction) were used to train and test each CNN. One of the CNNs used (InceptionResNet v2) was able to show a greater than 95% accuracy in classifying normal myocardium from acute and old myocardial infarction. The result of this study is promising and demonstrates that CNN technology has potential applications as a screening and computer-assisted diagnostics tool in forensic/postmortem histopathology.
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