Chinese Medical Sciences Journal ›› 2021, Vol. 36 ›› Issue (3): 204-209.doi: 10.24920/003962

Special Issue: 人工智能与精准肿瘤学

• Original Article • Previous Articles     Next Articles

Histopathological Diagnosis System for Gastritis Using Deep Learning Algorithm

Wei Ba1, Shuhao Wang2, Cancheng Liu2, Yuefeng Wang2, Huaiyin Shi1, Zhigang Song1, *()   

  1. 1Department of Pathology, Chinese PLA General Hospital & Medical School, Beijing 100853, China
    2Artificial Intelligence Lab, Thorough Images,Beijing 100853, China
  • Received:2021-06-29 Accepted:2021-08-18 Published:2021-09-30 Online:2021-08-31
  • Contact: Zhigang Song
A deep learning algorithm for pathological classification of chronic superficial, active, atrophic gastritis was developed and assessed using whole-slide images (WSIs), which demonstrated good performance.

Objective To develope a deep learning algorithm for pathological classification of chronic gastritis and assess its performance using whole-slide images (WSIs).
Methods We retrospectively collected 1,250 gastric biopsy specimens (1,128 gastritis, 122 normal mucosa) from PLA General Hospital. The deep learning algorithm based on DeepLab v3 (ResNet-50) architecture was trained and validated using 1,008 WSIs and 100 WSIs, respectively. The diagnostic performance of the algorithm was tested on an independent test set of 142 WSIs, with the pathologists’ consensus diagnosis as the gold standard.
Results The receiver operating characteristic (ROC) curves were generated for chronic superficial gastritis (CSuG), chronic active gastritis (CAcG), and chronic atrophic gastritis (CAtG) in the test set, respectively.The areas under the ROC curves (AUCs) of the algorithm for CSuG, CAcG, and CAtG were 0.882, 0.905 and 0.910, respectively. The sensitivity and specificity of the deep learning algorithm for the classification of CSuG, CAcG, and CAtG were 0.790 and 1.000 (accuracy 0.880), 0.985 and 0.829 (accuracy 0.901), 0.952 and 0.992 (accuracy 0.986), respectively. The overall predicted accuracy for three different types of gastritis was 0.867. By flagging the suspicious regions identified by the algorithm in WSI, a more transparent and interpretable diagnosis can be generated.
Conclusion The deep learning algorithm achieved high accuracy for chronic gastritis classification using WSIs. By pre-highlighting the different gastritis regions, it might be used as an auxiliary diagnostic tool to improve the work efficiency of pathologists.

Key words: artificial intelligence, deep learning, algorithm, gastritis, whole-slide pathological images

Funding: supported by the PLA General Hospital Medical Big Data and Artificial Intelligence Project(2019MBD-038)

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