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

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.