Abstract
Abstract Currently, various AI-based systems for computerassisted adenoma- and polyp-detection during colonoscopy have been brought to the market and are under clinical investigation. With these systems available to be used during routine screening colonoscopy and first results published about experiments and findings, it has become of interest how and to which extend such systems are used during the examination. Specifically, similarly to automotive navigation, it is of interest of how much visual focus is put onto the augmented image of the above-mentioned devices, signalling possible hypothesis of adenomas or polyps, and how much time-of-attention remains on the original colonoscopic video data. Thus, within a study, N = 36 participants using a prototype of a polypdetection system have been observed with an eye-tracker-system, to capture and evaluate the relative time of attention with respect to the original and augmented video data and differentiate these values between various sub-groups based on experience, education and gender. T-tests were conducted to identify potential significant differences. Based on the obtained data, the augmented video data is used with a very high attention (up to 75%) depending on the regarded sub-group. Experienced as well as less-experienced users (with > 500 colonoscopies) both preferred looking at the original data. In contrast, gastroenterologists (in contrast to nurses, students, engineers) were more interested in the outcome of the novel AIsystem. The female group preferred looking at the unobstructed data, while the male group was highly interested in the AI-based data.
Highlights
After almost thirty years of research and development in the field of machine learning and artificial intelligence (AI) with the goal design and develop devices to support gastroenterologists during colonoscopy [1], various commercially available AI-based systems for computer-assisted adenoma- and polypdetection during screening colonoscopy have been brought to the market and are currently under investigation
Amongst the commercially available products, providing visual augmented hints about possible adenomas are e.g., GI Genius by Medtronics (USA) [2,3,4], the CAD EYE system from FujiFilm (Japan) [5, 6], or the DISCOVERY by Pentax Medical (Japan) [7,8]. With these systems available to be used during routine screening colonoscopy and first results published about experiments and findings [2,3,4,5,6,7,8], it has become of interest how and to which extend such systems are used during colonoscopy
To automotive navigation, it is of interest of how much visual focus is put onto the augmented image of the above-mentioned devices, signalling possible hypothesis of adenomas or polyps, and how much time-of-attention remains on the original colonoscopic video data
Summary
After almost thirty years of research and development in the field of machine learning and artificial intelligence (AI) with the goal design and develop devices to support gastroenterologists during colonoscopy [1], various commercially available AI-based systems for computer-assisted adenoma- and polypdetection during screening colonoscopy have been brought to the market and are currently under investigation. Amongst the commercially available products, providing visual augmented hints about possible adenomas are e.g., GI Genius by Medtronics (USA) [2,3,4], the CAD EYE system from FujiFilm (Japan) [5, 6], or the DISCOVERY by Pentax Medical (Japan) [7,8] With these systems available to be used during routine screening colonoscopy and first results published about experiments and findings [2,3,4,5,6,7,8], it has become of interest how and to which extend such systems are used during colonoscopy.
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