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基于人工智能的模型的开发和验证,用于使用尿细胞学图像检测尿癌:一个多中心的诊断研究,具有前性验证
Shaoxu Wu1,2,3, Runnan Shen1, Guibin Hong1
1Department of Urology, Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China.
EClinicalMedicine
|April 30, 2024
概括
人工智能 (AI) 增强了尿液细胞学,用于检测尿癌 (UC),提高了灵敏度,并可能减少了像内镜这样的侵入性手术的需要. 这种人工智能解决方案有助于诊断和随访,有利于患者的护理.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 尿细胞学是诊断和监测尿癌 (UC) 的一个关键的非侵入性方法.
- 需要提高尿细胞学的诊断准确度,减少对内镜等侵入性手术的依赖.
- 人工智能 (AI) 具有提高医疗领域诊断能力的潜力.
研究的目的:
- 评估AI解决方案精密尿细胞学AI解决方案 (PUCAS) 的有效性,以提高尿细胞学用于UC检测的灵敏度.
- 评估PUCAS是否可以帮助避免不必要的内镜检查.
- 为了确定PUCAS在追溯和前验证队列中的诊断性能.
主要方法:
- 一项多中心诊断研究,涉及接受基于液体的尿细胞学的患者.
- 使用细胞学幻灯片的全幻灯片图像 (WSIs) 开发和验证PUCAS模型.
- 对PUCAS性能进行后期和前性验证,重点是诊断灵敏度和负预测值.
主要成果:
- 在检测UC时,PUCAS表现出高灵敏度,在回顾性队列中从0.922到1.000不等,在前性队列中为0.896.
- 人工智能模型在鉴定异常尿细胞病例中的恶性瘤方面表现良好 (灵敏度>0.84).
- 在复发检测中,PUCAS减少了57.5%的内镜使用,负预测值为96.4%.
结论:
- PUCAS AI 溶液在提高尿细胞学敏感性以诊断和随访UC方面表现有前途.
- 人工智能辅助尿细胞学可能会减少误诊,减少不必要的内镜检查的需要,并减轻临床负担.
- 建议进行进一步的国际验证,以确认PUCAS发现的概括性.
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