Communications medicine · 2026
In vitro development of the Autonomous Colonoscope Robot System (ACRS) for fully automated colonoscope insertion.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Kume Keiichiro, Taira Tatsuru, Terao Seigo, Sakai Nobuo
Аннотация
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.
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.