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CC BY-NC-ND 4.0 · Endosc Int Open 2024; 12(04): E520-E525
DOI: 10.1055/a-2239-9959
Innovation forum

Development of a novel endoscopic hemostasis-assisted navigation AI system in the standardization of post-ESD coagulation

Haruka Fujinami 
1   Endoscopy, University of Toyama Hospital, Toyama, Japan (Ringgold ID: RIN476163)
,
Shun Kuraishi
2   Medical Device Management Center, University of Toyama Hospital, Toyama, Japan (Ringgold ID: RIN476163)
,
Akira Teramoto
3   Third department of Internal medicine, University of Toyama, Toyama, Japan (Ringgold ID: RIN34823)
,
Seitaro Shimada
3   Third department of Internal medicine, University of Toyama, Toyama, Japan (Ringgold ID: RIN34823)
,
Saeko Takahashi
3   Third department of Internal medicine, University of Toyama, Toyama, Japan (Ringgold ID: RIN34823)
,
Takayuki Ando
3   Third department of Internal medicine, University of Toyama, Toyama, Japan (Ringgold ID: RIN34823)
,
Ichiro Yasuda
3   Third department of Internal medicine, University of Toyama, Toyama, Japan (Ringgold ID: RIN34823)
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Abstract

Background and study aims While gastric endoscopic submucosal dissection (ESD) has become a treatment with fewer complications, delayed bleeding remains a challenge. Post-ESD coagulation (PEC) is performed to prevent delayed bleeding. Therefore, we developed an artificial intelligence (AI) to detect vessels that require PEC in real time.

Materials and methods Training data were extracted from 153 gastric ESD videos with sufficient images taken with a second-look endoscopy (SLE) and annotated as follows: (1) vessels that showed bleeding during SLE without PEC; (2) vessels that did not bleed during SLE with PEC; and (3) vessels that did not bleed even without PEC. The training model was created using Google Cloud Vertex AI and a program was created to display the vessels requiring PEC in real time using a bounding box. The evaluation of this AI was verified with 12 unlearned test videos, including four cases that required additional coagulation during SLE.

Results The results of the test video validation indicated that 109 vessels on the ulcer required cauterization. Of these, 80 vessels (73.4%) were correctly determined as not requiring additional treatment. However, 25 vessels (22.9%), which did not require PEC, were overestimated. In the four videos that required additional coagulation in SLE, AI was able to detect all bleeding vessels.

Conclusions The effectiveness and safety of this endoscopic treatment-assisted AI system that identifies visible vessels requiring PEC should be confirmed in future studies.

Supplementary Material



Publication History

Received: 28 June 2023

Accepted after revision: 04 January 2024

Accepted Manuscript online:
08 January 2024

Article published online:
15 April 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).

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