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图像采集和协议变量的对人工智能模型性能的影响:ASFNR人工智能竞赛的二次分析
Guangming Zhu1, Burak Berksu Ozkara1, Jason W Allen1
1From the Department of Neurology (G.Z., M.E.), The University of Arizona, Tucson, AZ, USA; Department of Diagnostic, Molecular and Interventional Radiology (BBO), Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Radiology and Imaging Sciences (J.W.A.), Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology (D.P.B.), Duke University Medical Center, Durham, NC, USA; Department of Radiology, Neuroradiology Division (R.C., A.C., S.H., B.J., G.Z.), Stanford University, Stanford, CA, USA; Sutter Imaging (R.C.), Sutter Health, Sacramento, CA, USA; Department of Neuroradiology (H.C.), MD Anderson Cancer Center, Houston, TX, USA; Department of Radiology (C.G.F.), Tufts University, Boston, MA, USA; Department of Radiology (A.E.F.), Thomas Jefferson University, Philadelphia, PA, USA; Department of Radiology (R.G.), University of Alabama at Birmingham, Birmingham, AL, USA; Department of Radiology & Biomedical Imaging (C.H.), University of California, San Francisco, San Francisco, CA, USA; Department of Radiology (K.H.), New York University Grossman School of Medicine, New York, NY, USA; Department of Radiology (J.A. M., S.S.N.), University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Clinical Neurosciences (P.M.), Lausanne University Hospital, Lausanne, Switzerland; Department of Diagnostic Radiology and Nuclear Medicine (P.R.), University of Maryland School of Medicine, Baltimore, MD, USA; The Russell H. Morgan Department of Radiology and Radiological Science (P.R.), Johns Hopkins University, Baltimore, MD, USA; The Malone Center for Engineering in Healthcare (H.I.S.), Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Radiology (H.I.S.), Mayo Clinic, Rochester, MN, USA; and Department of Radiology (H.I.S.), Wake Forest University School of Medicine, Winston-Salem, NC, USA.
人工智能 (AI) 模型在神经放射学中的表现受到成像因素的显著影响. 使用的特定AI模型是最有影响力的因素,其次是扫描仪制造商和切片厚度.
科学领域:
- 神经辐射学神经辐射学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 人工智能模型显示了神经放射学方面的潜力.
- 人工智能的现实世界普遍性受到成像变化的限制.
- 变化源于采集协议和数据源.
研究的目的:
- 评估数据源,扫描仪制造商,扫描模式和切片厚度对AI性能的影响.
- 评估不同人工智能模型对神经放射学任务性能的影响.
- 分析2019年美国功能神经辐射学会 (ASFNR) 人工智能竞赛的二次数据.
主要方法:
- 利用了来自五个机构的1177个匿名非对比头部CT扫描.
- 开发了四种AI模型,用于检测中风,出血,质量效应和正常性.
- 概括估计方程 (GEE) 分析了成像变量对AI模型性能的影响.
主要成果:
- 人工智能模型显著影响了所有任务的性能.
- 扫描仪制造商和切片厚度影响出血和中风检测的准确性.
- 更薄的切片提高了血液和质量效应检测的准确性;扫描模式没有显著的影响.
结论:
- 图像获取和协议变异性显著影响神经放射学中的AI模型性能.
- 开发的特定AI模型是对准确度最有影响的因素.
- 扫描仪制造商和切片厚度是重要的变量,而扫描模式不是.