Objective Evaluation of Gaze Location Patterns Using Eye Tracking During Cystoscopy and Artificial Intelligence-Assisted Lesion Detection.

Publisher: Mary Ann Liebert Country of Publication: United States NLM ID: 8807503 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1557-900X (Electronic) Linking ISSN: 08927790 NLM ISO Abbreviation: J Endourol Subsets: MEDLINE

Original Publication: New York : Mary Ann Liebert, [c1987-

Cystoscopy*/methods ; Artificial Intelligence* ; Eye-Tracking Technology*; Humans ; Male ; Urinary Bladder Neoplasms/diagnostic imaging ; Urinary Bladder Neoplasms/pathology ; Urinary Bladder Neoplasms/diagnosis ; Students, Medical ; Female ; Adult ; Urologists ; Young Adult ; Eye Movements/physiology ; Middle Aged

Background: The diagnostic accuracy of cystoscopy varies according to the knowledge and experience of the performing physician. In this study, we evaluated the difference in cystoscopic gaze location patterns between medical students and urologists and assessed the differences in their eye movements when simultaneously observing conventional cystoscopic images and images with lesions detected by artificial intelligence (AI). Methodology: Eye-tracking measurements were performed, and observation patterns of participants (24 medical students and 10 urologists) viewing images from routine cystoscopic videos were analyzed. The cystoscopic video was captured preoperatively in a case of initial-onset noninvasive bladder cancer with three low-lying papillary tumors in the posterior, anterior, and neck areas (urothelial carcinoma, high grade, and pTa). The viewpoint coordinates and stop times during observation were obtained using a noncontact type of gaze tracking and gaze measurement system for screen-based gaze tracking. In addition, observation patterns of medical students and urologists during parallel observation of conventional cystoscopic videos and AI-assisted lesion detection videos were compared. Results: Compared with medical students, urologists exhibited a significantly higher degree of stationary gaze entropy when viewing cystoscopic images ( p  < 0.05), suggesting that urologists with expertise in identifying lesions efficiently observed a broader range of bladder mucosal surfaces on the screen, presumably with the conscious intent of identifying pathologic changes. When the participants observed conventional and AI-assisted lesion detection images side by side, contrary to urologists, medical students showed a higher proportion of attention directed toward AI-detected lesion images. Conclusion: Eye-tracking measurements during cystoscopic image assessment revealed that experienced specialists efficiently observed a wide range of video screens during cystoscopy. In addition, this study revealed how lesion images detected by AI are viewed. Observation patterns of observers' gaze may have implications for assessing and improving proficiency and serving educational purposes. To the best of our knowledge, this is the first study to utilize eye tracking in cystoscopy. University of Tsukuba Hospital, clinical research reference number R02-122.

Keywords: artificial intelligence; bladder cancer; cystoscopy; diagnostic support; eye tracking; stationary gaze entropy

Date Created: 20240325 Date Completed: 20240809 Latest Revision: 20240809

20240812

10.1089/end.2023.0699

38526374