학술논문
Development and multi‐institutional validation of an artificial intelligence‐based diagnostic system for gastric biopsy
Document Type
Report
Author
Source
Cancer Science. October 2022, Vol. 113 Issue 10, p3608, 10 p.
Subject
Language
English
ISSN
1347-9032
Abstract
Abbreviations INTRODUCTION Artificial intelligence has played a crucial part in numerous fields of human research, including medicine. The DCNN is the major driver of this marked development,[sup.1] especially in the [...]
: To overcome the increasing burden on pathologists in diagnosing gastric biopsies, we developed an artificial intelligence‐based system for the pathological diagnosis of gastric biopsies (AI‐G), which is expected to work well in daily clinical practice in multiple institutes. The multistage semantic segmentation for pathology (MSP) method utilizes the distribution of feature values extracted from patches of whole‐slide images (WSI) like pathologists’ “low‐power view” information of microscopy. The training dataset included WSIs of 4511 gastric biopsy tissues from 984 patients. In tissue‐level validation, MSP AI‐G showed better accuracy (91.0%) than that of conventional patch‐based AI‐G (PB AI‐G) (89.8%). Importantly, MSP AI‐G unanimously achieved higher accuracy rates (0.946 ± 0.023) than PB AI‐G (0.861 ± 0.078) in tissue‐level analysis, when applied to the cohorts of 10 different institutes (3450 samples of 1772 patients in all institutes, 198–555 samples of 143–206 patients in each institute). MSP AI‐G had high diagnostic accuracy and robustness in multi‐institutions. When pathologists selectively review specimens in which pathologist’s diagnosis and AI prediction are discordant, the requirement of a secondary review process is significantly less compared with reviewing all specimens by another pathologist.
: To overcome the increasing burden on pathologists in diagnosing gastric biopsies, we developed an artificial intelligence‐based system for the pathological diagnosis of gastric biopsies (AI‐G), which is expected to work well in daily clinical practice in multiple institutes. The multistage semantic segmentation for pathology (MSP) method utilizes the distribution of feature values extracted from patches of whole‐slide images (WSI) like pathologists’ “low‐power view” information of microscopy. The training dataset included WSIs of 4511 gastric biopsy tissues from 984 patients. In tissue‐level validation, MSP AI‐G showed better accuracy (91.0%) than that of conventional patch‐based AI‐G (PB AI‐G) (89.8%). Importantly, MSP AI‐G unanimously achieved higher accuracy rates (0.946 ± 0.023) than PB AI‐G (0.861 ± 0.078) in tissue‐level analysis, when applied to the cohorts of 10 different institutes (3450 samples of 1772 patients in all institutes, 198–555 samples of 143–206 patients in each institute). MSP AI‐G had high diagnostic accuracy and robustness in multi‐institutions. When pathologists selectively review specimens in which pathologist’s diagnosis and AI prediction are discordant, the requirement of a secondary review process is significantly less compared with reviewing all specimens by another pathologist.