Ortiz Esteban, Rivera Juan, Granja Manuel, Agudelo Nelson, Hernández Hoyos Marcela, Salazar Antonio
Systems and Computing Engineering Department, Universidad de los Andes, Bogotá, Colombia.
Department of Diagnostic Imaging, University Hospital Fundación Santa Fe de Bogotá, Bogotá, Colombia.
J Imaging Inform Med. 2025 Apr;38(2):1076-1090. doi: 10.1007/s10278-024-01258-9. Epub 2024 Sep 16.
To evaluate our two non-machine learning (non-ML)-based algorithmic approaches for detecting early ischemic infarcts on brain CT images of patients with acute ischemic stroke symptoms, tailored to our local population, to be incorporated in our telestroke software. One-hundred and thirteen acute stroke patients, excluding hemorrhagic, subacute, and chronic patients, with accessible brain CT images were divided into calibration and test sets. The gold standard was determined through consensus among three neuroradiologist. Four neuroradiologist independently reported Alberta Stroke Program Early CT Scores (ASPECTSs). ASPECTSs were also obtained using a commercial ML solution (CMLS), and our two methods, namely the Mean Hounsfield Unit (HU) relative difference (RELDIF) and the density distribution equivalence test (DDET), which used statistical analyze the of the HUs of each region and its contralateral side. Automated segmentation was perfect for cortical regions, while minimal adjustment was required for basal ganglia regions. For dichotomized-ASPECTSs (ASPECTS < 6) in the test set, the area under the receiver operating characteristic curve (AUC) was 0.85 for the DDET method, 0.84 for the RELDIF approach, 0.64 for the CMLS, and ranged from 0.71-0.89 for the neuroradiologist. The accuracy was 0.85 for the DDET method, 0.88 for the RELDIF approach, and was ranged from 0.83 - 0.96 for the neuroradiologist. Equivalence at a margin of 5% was documented among the DDET, RELDIF, and gold standard on mean ASPECTSs. Noninferiority tests of the AUC and accuracy of infarct detection revealed similarities between both DDET and RELDIF, and the CMLS, and with at least one neuroradiologist. The alignment of our methods with the evaluations of neuroradiologist and the CMLS indicates the potential of our methods to serve as supportive tools in clinical settings, facilitating prompt and accurate stroke diagnosis, especially in health care settings, such as Colombia, where neuroradiologist are limited.
为评估我们两种基于非机器学习（non-ML）的算法方法，用于在有急性缺血性中风症状患者的脑部CT图像上检测早期缺血性梗死，该方法针对我们当地人群量身定制，以纳入我们的远程中风软件。113例急性中风患者（不包括出血性、亚急性和慢性患者），其脑部CT图像可获取，被分为校准集和测试集。金标准通过三位神经放射科医生的共识确定。四位神经放射科医生独立报告阿尔伯塔中风项目早期CT评分（ASPECTSs）。ASPECTSs也使用商业ML解决方案（CMLS）以及我们的两种方法获得，即平均亨氏单位（HU）相对差异（RELDIF）和密度分布等效性测试（DDET），这两种方法使用统计学分析每个区域及其对侧的HU。自动分割在皮质区域非常完美，而基底节区域只需进行最小调整。对于测试集中二分法的ASPECTSs（ASPECTS < 6），DDET方法的受试者操作特征曲线（AUC）下面积为0.85，RELDIF方法为0.84，CMLS为0.64，神经放射科医生的AUC范围为0.71 - 0.89。DDET方法的准确率为0.85，RELDIF方法为0.88，神经放射科医生的准确率范围为0.83 - 0.96。DDET、RELDIF和金标准在平均ASPECTSs上记录了5% margin的等效性。梗死检测的AUC和准确率的非劣效性测试显示DDET和RELDIF与CMLS以及至少一位神经放射科医生之间存在相似性。我们的方法与神经放射科医生和CMLS的评估结果一致，表明我们的方法有潜力在临床环境中作为辅助工具，促进快速准确的中风诊断，特别是在像哥伦比亚这样神经放射科医生有限的医疗保健环境中。
