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Photoacoustic Cystography
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使用深度学习的增强型囊镜实时检测膀癌:试点研究
Timothy Chan Chang1, Eugene Shkolyar2, Francesco Del Giudice3
1Stanford University School of Medicine, Department of Urology, 453 Quarry Road, Urology - 5656, Palo Alto, California, United States, 94304; tcchang@stanford.edu.
Journal of endourology
|July 11, 2023
概括
这项研究表明,实时人工智能 (AI) 可以集成到囊镜和膀瘤切除程序中,为外科医生提供即时反,以改善膀癌的检测.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 在白光囊泡镜检查 (WLC) 中检测膀瘤是困难的,并且会影响治疗.
- 人工智能 (AI) 显示出改善瘤检测的前景,但实时应用尚未探索.
- 之前,人工智能在膀癌检测中的使用主要集中在记录图像的术后分析上.
研究的目的:
- 评估将实时人工智能系统集成到实时囊镜和膀瘤通过尿道切除 (TURBT) 程序中的可行性.
- 评估一个新的实时人工智能预警系统 (CystoNet) 在手术视频直播期间的性能.
主要方法:
- 预期招募接受柔性囊镜和TURBT的患者.
- 开发和整合一个实时AI警报设备 (CystoNet) 与标准囊镜设备.
- 实时处理流式囊镜视频,提供同步的警报框,并测量每诊断准确度.
主要成果:
- 实时CystoNet在50名患者的55个程序 (21个临床囊镜,34个TURBT) 中成功实施.
- 对于临床囊镜,CystoNet 实现了每98.8%的瘤特异性,平均误差率为3.6%.
- 对于TURBT,CystoNet表现出52.9%的每敏感度和95.4%的膀癌特异性,错误率为16.7%.
结论:
- 该试点研究证实了实时AI (CystoNet) 的可行性,为外科医生提供在囊镜和TURBT期间的积极反.
- 进一步优化CystoNet可能会提高其在AI增强型囊镜中的效用,以改善临床结果.
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