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Background <p>In the field of colonoscopy, robotic systems have been developed to support or replace human operators due to a shortage of trained endoscopists. We developed the Autonomous Colonoscope Robot System (ACRS), based on the Endoscopic Operation Robot version 4, to evaluate whether expert-derived operational data can enable autonomous colonoscope insertion.</p> Methods <p>ACRS was trained using insertion data obtained from an expert endoscopist operating a standardized colonoscopy training model. Automated insertions were evaluated using Pattern 1 of the model, a highly controlled configuration without substantial loop formation. Completely automated insertions were designated Level 4, whereas insertions requiring some manual assistance were designated Level 3. Level 4 insertion times were compared with manual insertions performed by an expert and trainees.</p> Results <p>Of the 72 automated insertions at Level 3 or higher, 62 are classified as Level 4, giving a success rate of 86.1% (95% CI, 75.9–93.1%). The average insertion time for Level 4 procedures is 2.92 ± 1.20 minutes, significantly longer than that of the expert (1.43 ± 0.32 minutes), but comparable to the time taken by trainees (2.97 ± 1.32 minutes; errors are standard deviations).</p> Conclusions <p>ACRS demonstrates proof-of-concept feasibility for autonomous colonoscope insertion under simplified, controlled model conditions. Further validation in more complex models, animal studies, and clinical settings is required before translational application.</p>

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In vitro development of the Autonomous Colonoscope Robot System (ACRS) for fully automated colonoscope insertion

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月30日

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  • Keiichiro Kume,
  • Tatsuru Taira,
  • Seigo Terao,
  • Nobuo Sakai

摘要

Background

In the field of colonoscopy, robotic systems have been developed to support or replace human operators due to a shortage of trained endoscopists. We developed the Autonomous Colonoscope Robot System (ACRS), based on the Endoscopic Operation Robot version 4, to evaluate whether expert-derived operational data can enable autonomous colonoscope insertion.

Methods

ACRS was trained using insertion data obtained from an expert endoscopist operating a standardized colonoscopy training model. Automated insertions were evaluated using Pattern 1 of the model, a highly controlled configuration without substantial loop formation. Completely automated insertions were designated Level 4, whereas insertions requiring some manual assistance were designated Level 3. Level 4 insertion times were compared with manual insertions performed by an expert and trainees.

Results

Of the 72 automated insertions at Level 3 or higher, 62 are classified as Level 4, giving a success rate of 86.1% (95% CI, 75.9–93.1%). The average insertion time for Level 4 procedures is 2.92 ± 1.20 minutes, significantly longer than that of the expert (1.43 ± 0.32 minutes), but comparable to the time taken by trainees (2.97 ± 1.32 minutes; errors are standard deviations).

Conclusions

ACRS demonstrates proof-of-concept feasibility for autonomous colonoscope insertion under simplified, controlled model conditions. Further validation in more complex models, animal studies, and clinical settings is required before translational application.