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CC BY 4.0 · Endosc Int Open 2026; 14: a27790074
DOI: 10.1055/a-2779-0074
Original article

Assessment of early gastric cancer visibility in deep-learning-based virtual indigo carmine chromoendoscopy (with video)

Authors

  • Ayaka Takasu 

    1   Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Koto-ku, Japan

  • Sho Suzuki 

    2   Department of Gastroenterology, International University of Health and Welfare Ichikawa Hospital, Ichikawa, Japan (Ringgold ID: RIN38259)

  • Yusuke Monno

    3   Department of Systems and Control Engineering, School of Engineering, Institute of Science Tokyo, Meguro-ku, Japan (Ringgold ID: RIN13290)

  • Masaki Minai

    3   Department of Systems and Control Engineering, School of Engineering, Institute of Science Tokyo, Meguro-ku, Japan (Ringgold ID: RIN13290)

  • Toshiaki Hirasawa

    1   Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Koto-ku, Japan

  • Hiroyuki Yamamoto

    1   Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Koto-ku, Japan

  • Fumiaki Ishibashi 

    2   Department of Gastroenterology, International University of Health and Welfare Ichikawa Hospital, Ichikawa, Japan (Ringgold ID: RIN38259)

  • Toshihiro Nishizawa 

    4   Department of Gastroenterology, International University of Health and Welfare Narita Hospital, Narita, Japan (Ringgold ID: RIN625200)

  • Masatoshi Okutomi

    3   Department of Systems and Control Engineering, School of Engineering, Institute of Science Tokyo, Meguro-ku, Japan (Ringgold ID: RIN13290)

  • Tomohiro Tada

    5   AI Medical Service Inc., Tokyo, Japan
    6   J's Clinic of Gastrointestinal Endoscopy & Proctology, Saitama, Japan

Supported by: Japan Agency for Medical Research and Development 24hma922022h0001
Supported by: Japan Society for the Promotion of Science JP24K15772
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Abstract

Background and study aims

Indigo carmine chromoendoscopy (IC) enhances diagnosis of early gastric cancer (EGC), but its clinical application is limited by procedure complexity and time. We developed a deep-learning system using a cycle-consistent generative adversarial network (CycleGAN) to generate virtual IC images from white-light endoscopy (WLE) and evaluated visibility of EGC in video-based virtual IC in a pilot study.

Patients and methods

We collected 4,096 endoscopic still images (2,089 WLE, 2,007 real IC) from 262 patients with gastric neoplasms. A CycleGAN model was trained to convert WLE into virtual IC images, and videos with 512 × 512 pixels at 30 frames per second were generated for five EGC cases. For each case, WLE, real IC, and virtual IC videos were prepared and evaluated by 16 endoscopists (6 experts, 10 non-experts). Visibility relative to WLE was rated using a 7-point Likert-type scale (−3 to +3), with positive values indicating improved visibility.

Results

A total of 160 evaluations were performed. Median [IQR] visibility score was 1 [0–2)] for real IC and 0 [−1 to 1] for virtual IC (P < 0.001). In virtual IC, 46.3% of cases achieved a score of +1 or higher. Scores significantly varied by endoscope system (P < 0.001).

Conclusions

Virtual IC improved visibility compared with WLE in nearly half the assessments, although its efficacy did not equal real IC. Optimizing performance for specific endoscope systems may enhance its clinical utility as a practical alternative for improving EGC detection.

Keywords

Endoscopy Upper GI Tract - Diagnosis and imaging (inc chromoendoscopy, NBI, iSCAN, FICE, CLE) - Endoscopic ultrasonography - Gastric cancer - Quality and logistical aspects - Image and data processing, documentation

Supplementary Material



Publication History

Received: 23 September 2025

Accepted after revision: 23 December 2025

Accepted Manuscript online:
23 December 2025

Article published online:
15 January 2026

© 2026. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).

Georg Thieme Verlag KG
Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

Bibliographical Record
Ayaka Takasu, Sho Suzuki, Yusuke Monno, Masaki Minai, Toshiaki Hirasawa, Hiroyuki Yamamoto, Fumiaki Ishibashi, Toshihiro Nishizawa, Masatoshi Okutomi, Tomohiro Tada. Assessment of early gastric cancer visibility in deep-learning-based virtual indigo carmine chromoendoscopy (with video). Endosc Int Open 2026; 14: a27790074.
DOI: 10.1055/a-2779-0074
 

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