Abstract：
This study aims to explore the possibility and bottleneck of clinical translation for an artificial intelligence (AI) diagnosis system for bladder cancer based on cystoscopy.We retrospectively collected videos of 101 bladder cancer patients from January to November 2023, at Sun Yat-sen Memorial Hospital, Sun Yat-sen University. Among these patients, with a median age of 63 years and 81.0% were male. The bladder cancer AI diagnosis system was utilized for diagnosis, and the accuracy of diagnoses from the videos was assessed. Additionally, a surgical evaluation scale was formulated to evaluate the quality of the videos, simulating clinical usage.The final test results showed a system sensitivity of 97.8%, a positive predictive value of 81.7%, specificity of 54.2%, and a negative predictive value of 92.3%. Furthermore, the surgical evaluation scale scores ranged from 3.96 to 4.69, indicating the feasibility of clinical application for this system.This study further quantitatively validated the accuracy of an artificial intelligence system using cystoscopy videos and assessed the potential for clinical application.
本研究主要探讨基于膀胱镜的膀胱癌人工智能诊断系统（CAIDS）临床转化的可能性及瓶颈问题。回顾性收集2023年1~11月中山大学孙逸仙纪念医院的101例膀胱癌患者膀胱镜视频，患者年龄中位数为63岁，其中男性占比81.0%（82/101）。使用CAIDS进行诊断，并对视频的诊断准确性进行评估。同时制定手术评价量表，基于量表对视频质量进行评估，以模拟临床使用。使用膀胱镜视频来定量验证人工智能系统的准确性。最终测试结果系统灵敏度为97.8%，阳性预测值为81.7%，特异度54.2%，阴性预测值为92.3%。此外，手术评价量表评分在3.96~4.69，表明该系统具有临床推广的可行性。.
本研究主要探讨基于膀胱镜的膀胱癌人工智能诊断系统（CAIDS）临床转化的可能性及瓶颈问题。回顾性收集2023年1~11月中山大学孙逸仙纪念医院的101例膀胱癌患者膀胱镜视频，患者年龄中位数为63岁，其中男性占比81.0%（82/101）。使用CAIDS进行诊断，并对视频的诊断准确性进行评估。同时制定手术评价量表，基于量表对视频质量进行评估，以模拟临床使用。使用膀胱镜视频来定量验证人工智能系统的准确性。最终测试结果系统灵敏度为97.8%，阳性预测值为81.7%，特异度54.2%，阴性预测值为92.3%。此外，手术评价量表评分在3.96~4.69，表明该系统具有临床推广的可行性。.
摘要：
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