Department of Urology, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan.
Master's Programs in Informatics, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan.
J Endourol. 2024 Aug;38(8):865-870. doi: 10.1089/end.2023.0699. Epub 2024 Apr 16.
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). 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. Compared with medical students, urologists exhibited a significantly higher degree of stationary gaze entropy when viewing cystoscopic images ( < 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. 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.
膀胱镜检查的诊断准确性因执行医师的知识和经验而异。在这项研究中，我们评估了医学生和泌尿科医生在膀胱镜检查时注视位置模式的差异，并评估了他们同时观察常规膀胱镜图像和人工智能（AI）检测到的病变图像时的眼球运动差异。进行了眼动跟踪测量，并分析了参与者（24 名医学生和 10 名泌尿科医生）观察常规膀胱镜视频图像的观察模式。该膀胱镜视频是在一名初发非浸润性膀胱癌患者术前捕获的，该患者在后、前和颈部区域有三个低置的乳头状肿瘤（尿路上皮癌，高级别，pTa）。使用非接触式屏幕式注视跟踪和注视测量系统获取观察过程中的视点坐标和停止时间。此外，还比较了医学生和泌尿科医生在同时观察常规膀胱镜视频和 AI 辅助病变检测视频时的观察模式。与医学生相比，泌尿科医生在观察膀胱镜图像时表现出更高的静止注视熵（ < 0.05），这表明具有识别病变能力的专家泌尿科医生能够更有效地观察屏幕上的更广泛的膀胱黏膜表面，可能是有意识地识别病理变化。当参与者并排观察常规和 AI 辅助病变检测图像时，与泌尿科医生相反，医学生更多地将注意力集中在 AI 检测到的病变图像上。在膀胱镜图像评估过程中的眼动跟踪测量表明，经验丰富的专家在膀胱镜检查期间能够有效地观察广泛的视频屏幕。此外，本研究揭示了 AI 检测到的病变图像是如何被观察的。观察者注视的观察模式可能对评估和提高熟练程度以及具有教育意义。据我们所知，这是首次在膀胱镜检查中使用眼动跟踪技术。筑波大学医院，临床研究参考号 R02-122。
