Development of a colon endoscope robot that adjusts its locomotion through the use of reinforcement learning

• Published in: International journal for computer assisted radiology and surgery Vol. 5; no. 4; pp. 317 - 325
• Main Authors: Trovato, G., Shikanai, M., Ukawa, G., Kinoshita, J., Murai, N., Lee, J. W., Ishii, H., Takanishi, A., Tanoue, K., Ieiri, S., Konishi, K., Hashizume, M.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.07.2010
• Subjects: Algorithms; Animals; Colon - anatomy & histology; Colonoscopes; Colonoscopy - methods; Computer Imaging; Computer Science; Equipment Design; Feasibility Studies; Fiber Optic Technology; Health Informatics; Imaging; Medicine; Medicine & Public Health; Original Article; Pattern Recognition and Graphics; Pliability; Radiology; Robotics - instrumentation; Software; Surgery; Swine; Torque; Vision; Medical robot; Forward/reverse screw; Colon endoscope; Autonomous colonoscope; Reinforcement learning
• ISSN: 1861-6410; 1861-6429
• DOI: 10.1007/s11548-010-0481-0

Purpose Fibre optic colonoscopy is usually performed with manual introduction and advancement of the endoscope, but there is potential for a robot capable of locomoting autonomously from the rectum to the caecum. A prototype robot was designed and tested. Methods The robot colonic endoscope consists in a front body with clockwise helical fin and a rear body with anticlockwise one, both connected via a DC motor. Input voltage is adjusted automatically by the robot, through the use of reinforcement learning, determining speed and direction (forward or backward). Results Experiments were performed both in-vitro and in-vivo, showing the feasibility of the robot. The device is capable of moving in a slippery environment, and reinforcement learning algorithms such as Q-learning and SARSA can obtain better results than simply applying full tension to the robot. Conclusions This self-propelled robotic endoscope has potential as an alternative to current fibre optic colonoscopy examination methods, especially with the addition of new sensors under development.