Classifying Microscopic Acute and Old Myocardial Infarction Using Convolutional Neural Networks
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Classifying Microscopic Acute and Old Myocardial Infarction Using Convolutional Neural Networks. / Garland, Jack; Hu, Mindy; Duffy, Michael; Kesha, Kilak; Glenn, Charley; Morrow, Paul; Stables, Simon; Ondruschka, Benjamin; Da Broi, Ugo; Tse, Rexson Datquen.
in: AM J FOREN MED PATH, Jahrgang 42, Nr. 3, 01.09.2021, S. 230-234.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Classifying Microscopic Acute and Old Myocardial Infarction Using Convolutional Neural Networks
AU - Garland, Jack
AU - Hu, Mindy
AU - Duffy, Michael
AU - Kesha, Kilak
AU - Glenn, Charley
AU - Morrow, Paul
AU - Stables, Simon
AU - Ondruschka, Benjamin
AU - Da Broi, Ugo
AU - Tse, Rexson Datquen
N1 - Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2021/9/1
Y1 - 2021/9/1
N2 - 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.
AB - 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.
KW - Fibroblasts/pathology
KW - Fibrosis
KW - Forensic Pathology/methods
KW - Hemorrhage/pathology
KW - Humans
KW - Image Processing, Computer-Assisted
KW - Myocardial Infarction/classification
KW - Myocardium/pathology
KW - Myocytes, Cardiac/pathology
KW - Neural Networks, Computer
KW - Neutrophils/metabolism
U2 - 10.1097/PAF.0000000000000672
DO - 10.1097/PAF.0000000000000672
M3 - SCORING: Journal article
C2 - 33833193
VL - 42
SP - 230
EP - 234
JO - AM J FOREN MED PATH
JF - AM J FOREN MED PATH
SN - 0195-7910
IS - 3
ER -
