Multi-label dynamic diagnosis of pancreatic diseases using AI-enhanced endoscopic ultrasound: a multi-cohort real-world study.

Authors

Ke Chen, Kai Zhang, Chunbing Zhu, Chao Zhang, Xiangpeng Hu, Zhixi Li, Jing Du, Qianqian Fang, Qijie Rui, Jianwei Qi, Bin Yao, Lingyu Zhang, Liting Zhang, Yuan Liu, Jin Xu, Xianjun Yu, Si Shi

Published in

Surgical endoscopy. Jun 17, 2026. Epub Jun 17, 2026.

Abstract

Artificial intelligence (AI) is a promising tool for pancreatic disease diagnosis using Endoscopic Ultrasound (EUS) images. But, current models often fail to fully account for real-world clinical applicability. To address this limitation, we propose the multi-label, dynamic AI system, designed to mimic real-world physician assessments.
We included 2 783 patients (330 706 EUS images) from three cohorts: FUSCC training (n = 2 498), FUSCC internal testing (n = 178), and external testing (n = 107). The AI-Enhanced Pancreatic Multi-Disease Diagnostic System with EUS (AI-Paradise) integrates module 1 (image type classification), module 2 (image quality control), and module 3 (multi-label classification). A computer-assisted diagnostic test (CADT) assessed the diagnostic performance of endoscopists with AI-Paradise assistance.
Module 1 and Module 2 achieved mean accuracies of 79·0 and 94·7%, respectively. These modules filtered out low-quality images, selecting 81 540 B-mode images for Module 3. In internal cross-validation, the best area under the curve (AUC) for six pancreatic diseases ranged from 71·5 to 87·6%. Module 3 demonstrated strong per-disease diagnostic performance in image-level testing, with accuracies ranging from 73.3 to 85.5% for the six pancreatic diseases (Table 2). The overall patient-level correct diagnosis rates, which are secondary summary metrics, were 66.9% (internal) and 63.6% (external). In CADT, performance of novice endoscopists significantly improved, with the best-performing novice achieving an increase from 39·4 to 57·4% (p < 0·0001).
AI-Paradise enhances diagnostic performance by assisting endoscopists in filtering out low-quality images and making accurate multiple-disease diagnoses.

PMID:
42310197
Bibliographic data and abstract were imported from PubMed on 18 Jun 2026.

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