A Novel Artificial Intelligence-based Endoscopic Ultrasonography Diagnostic System for Diagnosing the Invasion Depth of Early Gastric Cancer

Overview

Journal J Gastroenterol

Specialty Gastroenterology

Date 2024 May 7

PMID 38713263

Authors

Ryotaro Uema

Yoshito Hayashi

Takashi Kizu

Takumi Igura

Hideharu Ogiyama

Takuya Yamada

Risato Takeda

Kengo Nagai

Takuya Inoue

Masashi Yamamoto

Shinjiro Yamaguchi

Takashi Kanesaka

Takeo Yoshihara

Minoru Kato

Shunsuke Yoshii

Yoshiki Tsujii

Shinichiro Shinzaki

Tetsuo Takehara

Affiliations

Soon will be listed here.

Abstract

Background: We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system.

Methods: A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases).

Results: The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796-0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743-0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable.

Conclusions: Our AI model demonstrated a diagnostic performance equivalent to that of experts.

Citing Articles

Invasion and metastasis in cancer: molecular insights and therapeutic targets.

Li Y, Liu F, Cai Q, Deng L, OuYang Q, Zhang X Signal Transduct Target Ther. 2025; 10(1):57.

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Applications of Artificial Intelligence in Gastrointestinal Endoscopic Ultrasound: Current Developments, Limitations and Future Directions.

Wu Y, Ramai D, Smith E, Mega P, Qatomah A, Spadaccini M Cancers (Basel). 2025; 16(24.

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