ProMUS-NET:人工智能检测的前列腺癌比泌尿科医生通过微超声波检测的更多
Steve R Zhou1, Lichun Zhang2, Moon Hyung Choi1,2,3
1Department of Urology, Stanford School of Medicine, Palo Alto, CA, USA.
BJU international
|August 27, 2025
概括
一个新的人工智能 (AI) 模型,ProMUS-NET,改善了微超声波 (MUS) 图像上的前列腺癌检测. 人工智能模型显示比专家泌尿科医生更高的灵敏度,有助于活检诊断.
科学领域:
- 医学成像
- 医学的人工智能
- 尿道病学
背景情况:
- 前列腺癌的检测依赖于成像和活检.
- 微超声波 (MUS) 提供高分辨率可视化.
- 提高癌症定位的准确性和一致性至关重要.
研究的目的:
- 开发一个深度学习模型用于MUS的自动前列腺癌细分.
- 在癌症局部化方面提高敏感性和相互一致性.
- 将人工智能模型的性能与专家泌尿科医生进行比较.
主要方法:
- 从接受MRI-超声波融合活检的患者中收集MUS图像.
- 在MUS图像上标注临床显著的癌症 (等级≥2).
- 培训一个基于U-Net的模型 (ProMUS-NET),使用五重交叉验证.
主要成果:
- 人工智能模型实现了0.92的曲线下面面积 (AUC).
- 与泌尿科医生相比,ProMUS- NET的病变水平 (73%与58%) 和患者水平 (77%与66%) 的敏感性更高.
- 在周围区域的病变中观察到高灵敏度 (86. 2%).
结论:
- 人工智能模型准确地识别了MUS上的前列腺癌病变.
- 人工智能辅助检测显示有潜力提高活检诊断的准确性.
- 进一步的研究将集中在外部验证和减少假阳性.
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