Classifying Microscopic Acute and Old Myocardial Infarction Using Convolutional Neural Networks. (8th September 2021)

• Record Type: Journal Article
• Authors: Garland, Jack; Hu, Mindy; Duffy, Michael; Kesha, Kilak; Glenn, Charley; Morrow, Paul; Stables, Simon; Ondruschka, Benjamin; Da Broi, Ugo; Tse, Rexson Datquen
• Is Part Of: American journal of forensic medicine & pathology. Volume 42:Number 3(2021)
• Page Start: 230
• Page End: 234
• Publication Date: 2021-09-08
• Subjects: postmortem -- autopsy -- forensic -- deep learning -- convolutional neural network -- image classification -- myocardial infarction -- heart -- computer vision; Medical jurisprudence -- Periodicals; Forensic pathology -- Periodicals; 614.105
• DOI: 10.1097/PAF.0000000000000672
• Languages: English
• ISSNs: 0195-7910

Abstract

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.