针对小质成像的自动与手动细分.
Kristen McAlpine1, Nikhil Mirajkar2, Dominik Deniffel3,4
1Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada.
概括
人工智能 (AI) 与手工方法相比,在CT扫描上显著加快了小质 (SRM) 的细分. 人工智能细分是高效,准确和可接受的,改善了SRM评估的放射学.
科学领域:
- 医学成像分析 医学成像分析
- 放射学中的人工智能
- 瘤成像学成像学
背景情况:
- 使用人工智能的自动细分提供了小型质 (SRM) 的快速3D细分.
- 目前的手动细分方法耗时.
- 人工智能有可能增强放射学对SRM的临床实用性.
研究的目的:
- 为了比较人工智能驱动的SRM在CT扫描上的时间,准确性和可靠性与手动细分.
- 评估细分方法之间的差异的临床和统计意义.
主要方法:
- 在630个SRMCT扫描数据集上训练了一个AI模型 (nnU-Net),加上来自KiTS23.23的488个.
- 40个测试病例由人工智能,放射科医生和泌尿科医生进行细分.
- 比较了细分时间和Dice系数;独立的放射科医生评价了细分的可接受性,并确定了细分器.
主要成果:
- 人工智能细分速度明显更快,占放射科医生的三分之一和泌尿科医生的五分之一 (p<0.001).
- 观察到高级别之间的可靠性 (中位数为0.86-0.90).
- 人工智能细分获得了最高的可接受度得分 (中位数为4.1/5),表现优于放射科医生 (3.8/5) 和泌尿科医生 (3.3/5).
结论:
- 针对SRM的CT扫描的自动化AI细分是高效,准确和临床上可接受的.
- 这种人工智能方法有望改善SRM患者护理中的放射学应用.
- 人工智能驱动的细分可以简化对小质的医疗图像的评估.
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