[Application of deep learning neural network in pathological image classification of non-inflammatory aortic membrane degeneration].
Zhonghua Bing Li Xue Za Zhi
; 50(6): 620-625, 2021 Jun 08.
Article
em Zh
| MEDLINE
| ID: mdl-34078050
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
Tipo de estudo:
Prognostic_studies
País/Região como assunto:
Asia
Idioma:
Zh
Revista:
Zhonghua Bing Li Xue Za Zhi
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
China