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A NOVEL ARTIFICIAL INTELLIGENCE ASSISTED WHITE LIGHT ENDOSCOPIC MODEL IN THE DIAGNOSIS OF CARDIA ATROPHY: A MULTICENTER STUDY

Fangmei An 1, Yao Shen 1, Shuping Si 1, Lin Ji 1, Guifang Xu 2, Hongna Lu 3, Qian Sun 4, Yiwei Fu 5, Wenying Tian 1, Jiale Lv 1, Lili Guo 1, Jiawei Chen 1, Jinzheng Ren 1, Hui Cao 1, Guoming Lu 1, Yunan Zhang 1, Xiquan Ke 6, Qiang Zhan 1

Affiliations

1 The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China

2 NanjingDrumTowerHospital, Nanjing, China

3 The Affiliated LiHuili Hospital of Ningbo University, Ningbo, China

4 People’s Hospital of Anshun city, Anshun, China

5 The Affiliated Taizhou People’s Hospital of Nanjing Medical University, Taizhou, China

6 The First Affiliated Hospital of Bengbu Medical University, Bengbu, China

Summary

AI Generated

A multicenter study developed and validated an AI-assisted white light endoscopy system for diagnosing cardia atrophy that outperformed senior endoscopists in internal and external test sets.

The AI system was trained on 895 patients from 2 Chinese hospitals and tested on 220 internal and 354 external test patients between November 2022 and December 2024.
In the internal test set, the AI system achieved accuracy of 0.927, sensitivity of 0.941, specificity of 0.919, and AUC of 0.967.
In the external test set, the AI system achieved accuracy of 0.918, sensitivity of 0.894, specificity of 0.931, and AUC of 0.953.
The AI system outperformed senior endoscopists across all diagnostic metrics in both internal and external test sets.
The system is proposed for use in guiding white light endoscopic judgment and targeted biopsies for cardia atrophy, addressing the significant difficulty in diagnosing cardia atrophy under conventional endoscopy.

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Abstract

Introduction

The extent of gastric atrophy(GA) is closely related to the incidence of Intestinal-type gastric cancer (GC). According to the Kimura-Takemoto classification, determining whether the atrophy extends beyond the cardia is key to assess the extent of GA. However, there is significant difficulty in determining whether the cardia is atrophied under endoscopy.

Aims & Methods

This study developed a novel artificial intelligence (AI)-assisted white light endoscopy system for identifying the cardia part and diagnosing cardia atrophy . In this multicenter study, patients undergoing upper gastrointestinal endoscopy at 5 hospitals in China were prospectively included between November 2022 and December 2024. Among them, a total of 895 patients from 2 units were assigned to the training and validation sets (292 with cardia atrophy and 603 without cardia atrophy) to establish a cardia atrophy diagnostic model. 220 patients from our unit (84 with cardia atrophy and 136 without cardia atrophy) were included in the internal test set. Additionally, a total of 354 patients from 3 other units were included in the external test set (106 with cardia atrophy and 248 without cardia atrophy). In the internal and external test sets, 3 senior and 3 junior endoscopists made online diagnoses, comparing the performance differences between physicians with different levles of experience and the AI-assisted cardia atrophy detection system.

Results

This study established a AI-assisted cardia atrophy diagnostic model and tested its diagnostic performance with multicenter image data in internal and external tests. In the internal test dataset, the accuracy, sensitivity, and specificity of the AI-assisted system were 0.927, 0.941, and 0.919, respectively, with a PPV of 0.878, NPV of 0.962, and AUC of 0.967. In the external test dataset, the accuracy, sensitivity, and specificity were 0.918, 0.894, and 0.931 respectively, with a PPV of 0.874, NPV of 0.943, and AUC of 0.953. Further comparison of the diagnostic efficiency of the AI-assisted system with endoscopists of different levels of experience revealed that both in the internal and external test set, the diagnosis accuracy, sensitivity, specificity, PPV, and NPV of the AI-assisted system were all higher than those of senior endoscopists. TheAI-assisted cardia atrophy diagnostic ROC curve area (AUC) was 0.967 and 0.953 in the internal and external test sets, respectively. Heat map analysis further showed a high consistency between theAI-assisted system and endoscopists in cardia atrophy diagnosis.

Conclusion

Our newly developed AI-assisted cardia atrophy diagnostic system shows superior performance in identifying cardia atrophy compared to senior endoscopists, and can be used to guide white light endoscopic judgment and targeted biopsies for cardia atrophy.

Conference

UEG Week Berlin 2025

Topics

Oesophagus

Submission format

Abstract

Session

AI in the upper GI tract (Posters)

Citation

United European Gastroenterology Journal 2025; 13 (Supplement 8)

Published

2025

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