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Objective Evaluation of Gaze Location Patterns Using Eye Tracking During Cystoscopy and Artificial
Atsushi Ikeda1, Kazuya Izumi2, Kensuke Katori2
1Department of Urology, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan.
Journal of Endourology
|March 25, 2024
Summary
Urologists demonstrate efficient gaze patterns during cystoscopy, covering more bladder surface than medical students. Artificial intelligence (AI) aids medical students by directing their attention to lesions during cystoscopic image analysis.
Area of Science:
- Urology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Diagnostic accuracy in cystoscopy is physician-dependent.
- Variations in gaze patterns between experts and novices are not well understood.
- The impact of artificial intelligence (AI) on visual search strategies in cystoscopy requires investigation.
Purpose of the Study:
- To compare cystoscopic gaze location patterns between urologists and medical students.
- To assess differences in eye movements when viewing conventional versus AI-assisted cystoscopic images.
- To evaluate the utility of AI in guiding visual attention during cystoscopy.
Main Methods:
- Eye-tracking measurements were used to analyze the observation patterns of 24 medical students and 10 urologists.
- Participants viewed cystoscopic videos of bladder cancer, including conventional and AI-assisted lesion detection images.
- Gaze viewpoint coordinates and stop times were recorded using a screen-based gaze tracking system.
Main Results:
- Urologists exhibited significantly higher stationary gaze entropy than medical students, indicating broader visual scanning of bladder mucosa.
- Medical students, unlike urologists, directed a higher proportion of attention toward AI-detected lesions when viewing images side-by-side.
- AI-assisted lesion detection influenced the gaze patterns of less experienced observers.
Conclusions:
- Experienced urologists efficiently scan a wider area of the video screen during cystoscopy.
- AI-guided lesion detection can direct the attention of medical students to relevant areas.
- Eye-tracking analysis provides insights into visual expertise and has potential for educational applications in cystoscopy training.
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