在灵活的尿路透镜手术期间,对人工智能辅助系统进行实时结石检测的临床验证
Chenfeng Wang1,2, Haomin Liang3, Hairui Chen1
1Department of Urology, The Third Medical Center, Chinese PLA General Hospital, Beijing, China.
NPJ digital medicine
|November 27, 2025
概括
灵活尿道透视的人工智能系统 (AiFURS) 准确地检测,分类和实时测量结石. 这种人工智能工具提高了手术精度,并预测了重新手术的风险,改善了患者在内分泌学方面的结果.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗人工智能 医疗人工智能
- 内分泌病学 在内分泌病学.
背景情况:
- 灵活尿道透视 (FURS) 是对结石的标准微创治疗方法.
- 准确的石头负担和类型的手术内评估对于成功的FURS结果至关重要.
- 目前用于石材测量和分类的方法可能是主观的,耗时的.
研究的目的:
- 开发和临床验证一个人工智能系统,用于在FURS (AiFURS) 期间实时检测,分类和测量石头.
- 与专家外科医生相比,评估AiFURS的诊断准确性.
- 为了确定FURS后重新操作的预测因素.
主要方法:
- 在6,170个注释尿道镜视频 (11,870块石头) 上训练了一个AI模型 (AiFURS).
- 通过使用ex vivo,in vivo和外部数据集验证AiFURS性能.
- 与AiFURS石头类型预测准确度与专家泌尿科医生进行比较.
- 利用后勤回归来确定重新操作的预测因素.
主要成果:
- 在预测石头数量 (r > 0.9) 和大小 (r = 0.81对于石头>2mm) 方面,AiFURS表现出很高的准确性.
- 在体内和外部验证显示,患者级石头类型预测的诊断准确度高 (92.2-95.3%和86.8-92.2%),表现优于外科医生.
- 剩余碎片>2mm的比例被确定为重新操作的独立预测因素.
结论:
- 在FURS期间,AiFURS提供实时,准确的石头评估.
- 人工智能系统提高了手术精度,并有可能减少并发症.
- 通过优化石头治疗和预测重新手术风险,AiFURS可以改善患者的治疗结果.
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