ArticleFrontiers in oncology2022
Two-step artificial intelligence system for endoscopic gastric biopsy improves the diagnostic accuracy of pathologists.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Clinical Features and Factors Contributing to Preoperative Misdiagnosis of Gastric Gastrointestinal Stromal Tumors: A Retrospective Analysis of 27 Cases.Gastroenterology research and practice · 2026Article
- Applications of artificial intelligence in digital pathology for gastric cancer.Frontiers in oncology · 2024Review
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Authors and funding
14 authors.
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Abstract
Background: Endoscopic biopsy is the pivotal procedure for the diagnosis of gastric cancer. In this study, we applied whole-slide images (WSIs) of endoscopic gastric biopsy specimens to develop an endoscopic gastric biopsy assistant system (EGBAS). Methods: The EGBAS was trained using 2373 WSIs expertly annotated and internally validated on 245 WSIs. A large-scale, multicenter test dataset of 2003 WSIs was used to externally evaluate EGBAS. Eight pathologists were compared with the EGBAS using a man-machine comparison test dataset. The fully manual performance of the pathologists was also compared with semi-manual performance using EGBAS assistance. Results: The average area under the curve of the EGBAS was 0·979 (0·958-0·990). For the diagnosis of all four categories, the overall accuracy of EGBAS was 86·95%, which was significantly higher than pathologists (P< 0·05). The EGBAS achieved a higher κ score (0·880, very good κ) than junior and senior pathologists (0·641 ± 0·088 and 0·729 ± 0·056). With EGBAS assistance, the overall accuracy (four-tier classification) of the pathologists increased from 66·49 ± 7·73% to 73·83 ± 5·73% (P< 0·05). The length of time for pathologists to manually complete the dataset was 461·44 ± 117·96 minutes; this time was reduced to 305·71 ± 82·43 minutes with EGBAS assistance (P = 0·00). Conclusions: The EGBAS is a promising system for improving the diagnosis ability and reducing the workload of pathologists.
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