Application of deep learning neural network in pathological image classification of non-inflammatory aortic membrane degeneration
10.3760/cma.j.cn112151-20201205-00902
- VernacularTitle:深度学习神经网络在非炎性主动脉中膜变性病理图像分类中的应用
- Author:
Hao WANG
1
;
Dong CHEN
;
Tao WAN
;
Yanli ZHAO
;
Zhongjie SUN
;
Wei FANG
;
Fang DONG
;
Guoliang LIAN
;
Liyuan HAN
Author Information
1. 首都医科大学附属北京安贞医院病理科 100029
- Keywords:
Aortic diseases;
Tunica media;
Artificial intelligence;
Neural networks (computer)
- From:
Chinese Journal of Pathology
2021;50(6):620-625
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of deep learning in classifying non-inflammatory aortic membrane degeneration.Methods:Eighty-nine cases of non-inflammatory aortic media degeneration diagnosed from January to June 2018 were collected at Beijing Anzhen Hospital, Capital Medical University, China and scanned into digital sections. 1 627 hematoxylin and eosin stained photomicrographs were extracted. Combined with the ResNet18-based deep convolution neural network model, 4-category classification of pathological images were performed to diagnose the non-inflammatory aortic lesion.Results:The prediction model of artificial intelligence assisted diagnosis had the best accuracy, sensitivity and precision in identifying lesions with smooth muscle cell nuclei loss, which were 99.39%, 98.36% and 98.36%, respectively. The classification accuracy of elastic fiber fragmentation and/or loss lesions was 98.08%, while that of intralamellar mucoid extracellular matrix accumulation lesions was 96.93%. The overall accuracy of the classification model was 96.32%, and the area under the curve was 0.982.Conclusions:The accuracy of deep learning neural network model in the 4-category classification of non-inflammatory aortic lesionsis confirmed based on digital photomicrographs. This method can effectively improve the diagnostic efficiency of pathologists.
