人类和人工智能在检测焦点皮层发育不良症方面的定量比较
Lennart Walger1, Tobias Bauer, David Kügler
1From the Department of Neuroradiology, University Hospital Bonn, Bonn, Germany (L.W., T. Bauer, M.H.S., F.G., A.L., F.C.S., A. Radbruch, T.R.); Department of Epileptology, University Hospital Bonn, Bonn, Germany (L.W., T. Bauer, M.H.S., F. Schuch, T. Baumgartner, K.O.D., L.O., J.P., A. Racz, K.v.d.R., A.U.-P., P.v.W., R.v.W., R.S., T.R.); German Center for Neurodegenerative Diseases, Bonn, Germany (D.K., M.R., A. Radbruch); Department of Neuroradiology, Goethe University Frankfurt, Frankfurt, Germany (C.A., E.N., E.H.); Department of Neurology, University Hospital Bonn, Bonn, Germany (J.B., J.N.); Department of Neurosurgery, University Hospital Bonn, Bonn, Germany (V.B., M. Vychopen, H.V.); Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Bonn, Germany (C.E., C.I., P.K., A.L., A.-M.O., M. Voigt, U.A.); Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany (M.K., S.M., F. Schrader, A.S., A.P.); Chair of Economic & Social Policy, WHU-Otto Beisheim School of Management, Vallendar, Germany (P.v.W.); Department of Neuropathology, University Hospital Bonn, Bonn, Germany (A.B.); A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA (M.R.); Department of Radiology, Harvard Medical School, Boston, MA (M.R.); Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom (J.W.S.); Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom (J.W.S.); Stichting Epilepsie Instellingen Nederland, Heemstede, the Netherland (J.W.S.); Department of Neurology, West China Hospital, Sichuan University, Chengdu, China (J.W.S.); and Center for Medical Data Usability and Translation, University of Bonn, Bonn, Germany (A. Radbruch, T.R.).
人工智能 (AI) 在检测焦皮质发育不良 (FCD) 方面表现有前途,AI模型补充了人类的表现. 将非专家阅读器与人工智能结合起来,提高了检测率,可能有助于在FCD诊断中进行临床决策.
科学领域:
- 神经学 神经学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 准确检测焦点皮质发育不良 (FCD) 对于有效的临床决策至关重要.
- 评估人类在FCD检测方面的表现对于评估人工智能 (AI) 影响至关重要.
研究的目的:
- 量化评估人类检测FCD的能力.
- 为了比较人类的性能与最先进的AI模型用于FCD检测.
- 确定人工智能如何协助FCD的诊断过程.
主要方法:
- 使用单点和3D边界框检测FCD的读取器性能的未来记录.
- 获取和比较来自三个AI模型与人类读者的预测.
- 对阅读器和人工智能模型的双对组合进行分析.
主要成果:
- 专家读者检测到68%的FCD,而非专家读者检测到47%的FCD. 人工智能模型检测到32%至72%的FCD,错误阳性率各不相同.
- 人类的表现受到特定的MRI特征的影响,例如穿标志和皮质厚度.
- 人工智能模型对异常旋转和灰白物质模糊的敏感性显示出敏感性.
- 专家-专家对提高了13%的检测;人工智能模型提高了高达19%的专家检测.
- 非专家读者与人工智能相结合,单个专家的表现高达13%.
结论:
- 这项研究提供了人类和人工智能在FCD病变检测中的表现的先进比较评估.
- 人工智能和人类的预测存在差异,特别是在FCD的MRI特征方面.
- 人工智能有可能补充FCD的诊断工作.


