Classifying Microscopic Acute and Old Myocardial Infarction Using Convolutional Neural Networks.
Journal
The American journal of forensic medicine and pathology
ISSN: 1533-404X
Titre abrégé: Am J Forensic Med Pathol
Pays: United States
ID NLM: 8108948
Informations de publication
Date de publication:
01 Sep 2021
01 Sep 2021
Historique:
pubmed:
10
4
2021
medline:
26
8
2021
entrez:
9
4
2021
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 33833193
doi: 10.1097/PAF.0000000000000672
pii: 00000433-202109000-00006
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
230-234Informations de copyright
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
The authors report no conflict of interest.
Références
Chollet F. Keras. GitHub Repository . 2015.
Peña-Solórzano CA, Albrecht DW, Bassed RB, et al. Findings from machine learning in clinical medical imaging applications—lessons for translation to the forensic setting. Forensic Sci Int . 2020;316:110538.
Dobay A, Ford J, Decker S, et al. Potential use ofe deep learning techniques for postmortem imaging. Forensic Sci Med Pathol . 2020;16:671–679.
Ghatak A. Deep Learning with R . Singapore: Springer; 2019.
Chollet F. Deep Lerning With Python . Shelter Island, NY: Manning; 2020.
Chollet F, Allaire J. Deep Learning with R . Shelter Island, NY: Manning; 2018.
van Ginneken B. Deep learning for triage of chest radiographs: should every institution train its own system? Radiology . 2019;290:545–546.
Pérez-Sianes J, Pérez-Sánchez H, Díaz F. Virtual screening meets deep learning. Curr Comput Aided Drug Des . 2019;15:6–28.
Negahdar M, Coy A, Beymer D. An end-to-end deep learning pipeline for emphysema quantification using multi-label learning. Annu Int Conf IEEE Eng Med Biol Soc . 2019;2019:929–932.
Moen E, Bannon D, Kudo T, et al. Deep learning for cellular image analysis. Nat Methods . 2019;16:1233–1246.
Kather JN, Krisam J, Charoentong P, et al. Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med . 2019;16:e1002730.
Fourcade A, Khonsari RH. Deep learning in medical image analysis: a third eye for doctors. J Stomatol Oral Maxillofac Surg . 2019;120:279–288.
Brent R, Boucheron L. Deep learning to predict microscope images. Nat Methods . 2018;15:868–870.
Chan HP, Samala RK, Hadjiiski LM, et al. Deep learning in medical image analysis. Adv Exp Med Biol . 2020;1213:3–21.
Strack R. Deep learning in imaging. Nat Methods . 2019;16:17.
Mostapha M, Styner M. Role of deep learning in infant brain MRI analysis. Magn Reson Imaging . 2019;64:171–189.
Litjens G, Ciompi F, Wolterink JM, et al. State-of-the-art deep learning in cardiovascular image analysis. JACC Cardiovasc Imaging . 2019;12:1549–1565.
Currie GM. Intelligent imaging: anatomy of machine learning and deep learning. J Nucl Med Technol . 2019;47:273–281: jnmt.119.232470.
Way GP, Greene CS. Bayesian deep learning for single-cell analysis. Nat Methods . 2018;15:1009–1010.
Giger ML. Machine learning in medical imaging. J Am Coll Radiol . 2018;15:512–520.
Garland J, Ondruschka B, Tse R. Potential use of deep learning techniques for postmortem imaging-moving beyond postmortem radiology. Forensic Sci Med Pathol . 2020. doi: 10.1007/s12024-020-00330-4. Epub ahead of print. PMID: 33175309.
doi: 10.1007/s12024-020-00330-4.
Garland J, Hu M, Kesha K, et al. Identifying gross post-mortem organ images using a pre-trained convolutional neural network. J Forensic Sci . 2021;66:630–635.
Tirado J, Mauricio D. Bruise dating using deep learning. J Forensic Sci . 2021;66:336–346.
Garland J, Ondruschka B, Stables S, et al. Identifying fatal head injuries on postmortem computed tomography using convolutional neural network/deep learning: a feasibility study. J Forensic Sci . 2020;65:2019–2022.
Ebert LC, Heimer J, Schweitzer W, et al. Automatic detection of hemorrhagic pericardial effusion on PMCT using deep learning—a feasibility study. Forensic Sci Med Pathol . 2017;13:426–431.
Dettmeyer RB. Forenisc Histopathology: Fundementals and Perspectives . Berlin: Springer; 2014.
Vinay Kumar AKA, Fausto N. Robbins and Cotran Pathological Basis of Disease . Philadelphia, PA: Elsevier Saunders; 2005.
Chollet F. Xception: deep learning with depthwise separable convolutions. Proc IEEE Conf Comput Vis Pattern Recognit . 2017;1251–1258.
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. Proc IEEE Conf Comput Vis Pattern Recognit . 2016;770–778.
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556 . 2014.
Szegedy C, Ioffe S, Vanhoucke V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv preprint arXiv:160207261 . 2016.
Srinidhi CL, Ciga O, Martel AL. Deep neural network models for computational histopathology: a survey. Med Image Anal . 2021;67:101813.
DiMaio V, DiMaio D. Forensic Pathology . Boca Raton, FL: CRC Press; 2001.
Dolinak D, Matshes E, Lew E. Forensic Pathology . Murlington MA: Elsevier; 2005.
Saukko P, Knight B. Kinght's Forensic Pathology . London: Arnold; 2004.
Yang YJ, Bang CS. Application of artificial intelligence in gastroenterology. World J Gastroenterol . 2019;25:1666–1683.
Diao S, Hou J, Yu H, et al. Computer-aided pathologic diagnosis of nasopharyngeal carcinoma based on deep learning. Am J Pathol . 2020;190:1691–1700.
Duggento A, Conti A, Mauriello A, et al. Deep computational pathology in breast cancer. Semin Cancer Biol . 2020.