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Evaluation of Computer-aided Detection for Identifying Missed Gastric Cancer After Endoscopic Submucosal Dissection
Tatsunori Minamide1,2, Tomonori Sato3, Seiichiro Sakaguchi3
1Division of Endoscopy Shizuoka Cancer Center Shizuoka Japan.
Objectives:
Computer-aided detection (CADe) using deep learning is promising for reducing missed gastric cancers (MGCs) and supporting physicians in double-checking endoscopic images. We aimed to evaluate the CADe efficacy for MGCs after endoscopic submucosal dissection (ESD).
Methods:
We collected 2324 endoscopic images, including 60 of MGCs detected during surveillance esophagogastroduodenoscopy within 18 months after initial ESD. The performance of the CADe system, developed for early GC detection using deep learning, was compared with that of 10 endoscopists in a detection study using collected images. Per-lesion sensitivity, per-image detection performance, and diagnostic time were compared.
Results:
The per-lesion sensitivity for MGCs was 15.0% (9/60) and 13.8% (83/600) for CADe and endoscopist detection, respectively (p = 0.538). The respective per-image computer-aided and endoscopist performance sensitivity was 11.3% and 10.6% (p = 0.548), specificity 88.5% and 94.8% (p < 0.001), positive predictive value 4.4% and 8.3%, and negative predictive value 95.7% and 96.0%. The per-image CADe time was significantly shorter (0.03 s vs. 2.86 s, p < 0.001). CADe showed higher sensitivity in certain subgroups, although these findings should be interpreted cautiously given the small sample size.
Conclusions:
No significant difference in sensitivity was observed between CADe and endoscopist detection for MGCs after ESD, and the absolute sensitivity remained low. Further improvements are needed before clinical implementation of CADe as a double-checking tool.
Trial Registration:
The authors have confirmed clinical trial registration is not needed for this submission.
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