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奥赛里斯:从基于医院的实施和评估中吸取的教训放射治疗图像细分的开源深度学习模型

A D Constantinou¹, A Hoole¹, D C Wong²

¹Medical Physics, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.

Clinical oncology (Royal College of Radiologists (Great Britain))

|November 10, 2024

概括

这项研究表明,OSAIRIS AI模型在放射治疗规划中显著减少了轮时间,提高了工作流效率. 评估必须包括临床实用性和安全性,而不仅仅是准确度指标,以进行可靠的AI评估.

科学领域:

医学物理医学物理
辐射疗法辐射疗法
人工智能的人工智能

背景情况:

在放射治疗中,对有风险的器官 (OAR) 进行自我细分是广泛研究的准确性.
现有的评估往往忽略了感知到的实用性和安全性,仅专注于指标.
开源AI模型OSAIRIS是作为OAR轮的医疗设备开发的.

研究的目的:

临床评估OSAIRIS AI模型用于放射治疗治疗计划.
在临床工作流程中评估切实的益处,量化实用性和评估风险.
将OSAIRIS的性能与传统方法和商业AI设备进行比较.

主要方法:

对OSAIRIS用于头部和部和男性骨盆 (前列腺) OAR轮的临床评估.
对临床医生节省时间的好处的量化.
准确度指标 (迪斯,豪斯多夫) 和编辑时间之间的相关性分析.
安全评估侧重于临床医生检测和纠正错误.
盲人临床医生轮评分练习和"神秘购物"实验用于风险评估.

主要成果:

奥赛里斯节省了大量的时间:前列腺节省了36%,头轮节省了67%.
豪斯多夫距离与编辑时间的最佳相关性 (斯皮尔曼0.70,肯德尔0.52),表现优于子.
临床医生纠正了大多数错误 (72%的头部和部,81%的前列腺).
在AI轮编辑过程中观察到定偏差.
盲目评分表明AI轮的评分并不低于临床医生的轮.

结论:

在放射治疗中对AI的临床评估必须超越准确度指标,包括工作流程,实用性和安全性.
人与人工智能的交互效应对于评估实际可用性和技术采用至关重要.
对人工智能医疗设备而言,全面的风险评估和缓解策略是必不可少的.

关键词:

在这里,我们可以看到AIAIAI.准确度指标准确度指标准确度指标自动细分汽车细分汽车细分以人为中心的人为中心.机器学习就是机器学习.医疗器械医疗器械医疗器械

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