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评估偏见和质量问题在交叉方式图像翻译研究神经放射学:一个系统的审查

Shannon L Walston¹, Hiroyuki Tatekawa¹, Hirotaka Takita¹

¹From the Department of Diagnostic and Interventional Radiology (S.L.W., H.Tatekawa, H.Takita, Y.M., D.U.), Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan.

AJNR. American journal of neuroradiology

|April 25, 2024

概括

用于脑图像翻译的人工智能 (AI) 面临着阻碍临床使用的关键问题. 医学和工程领域之间的合作对于改善AI模型报告和验证以获得更好的临床应用至关重要.

科学领域:

医疗成像医学成像
人工智能的人工智能
放射学放射学是一门学科.

背景情况:

间模式图像对图像翻译是一种人工智能技术,用于从另一个图像模式生成一种图像模式.
人工智能模型在医学成像中越来越多地用于诸如脑图像翻译等任务.

研究的目的:

系统地识别和量化人工智能模型中的偏差和质量问题,以实现多种方式的大脑图像翻译.
评估阻止这些AI模型临床应用的因素.

主要方法:

在PubMed,Scopus和IEEE Xplore搜索了基于人工智能的图像翻译模型的放射性脑图像 (2017年4月-2023年8月).
通过使用医学成像人工智能检查清单 (CLAIM) 评估了102项研究的质量,并使用PROBAST进行了偏差.
在医学专注和工程专注的文章之间,对CLAIM和PROBAST标准的遵守程度进行了比较.

主要成果:

对CLAIM的中位坚持率为69%,对PROBAST的中位坚持率为38%.
专注于医学的文章显示,总体上,CLAIM的遵守率 (73%) 比专注于工程的文章 (65%) 高.
专注于工程研究的研究对模型描述的坚持性更好,但整体坚持性低于医学研究.

结论:

大多数研究都存在关键问题,阻止AI脑图像翻译的临床应用.
专注于工程的人工智能研究,虽然在技术描述方面强大,但与医学研究相比,在整体遵守方面落后.
改善医疗和工程领域之间的报告和协作对于推进临床AI应用至关重要.

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