自动 ASPECTS 分段和评分工具:为哥伦比亚远程中风网络量身定制的方法

IF 2.9 2区 工程技术 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Esteban Ortiz, Juan Rivera, Manuel Granja, Nelson Agudelo, Marcela Hernández Hoyos, Antonio Salazar
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引用次数: 0

摘要

评估我们的两种基于非机器学习(non-ML)的算法,用于检测急性缺血性中风症状患者脑部 CT 图像上的早期缺血性梗死。113 名急性中风患者(不包括出血性、亚急性和慢性患者)的脑 CT 图像被分为校准集和测试集。金标准由三位神经放射学专家共同确定。四位神经放射学专家独立报告阿尔伯塔省卒中计划早期 CT 评分(ASPECTS)。ASPECTS 也是通过商业 ML 解决方案 (CMLS) 和我们的两种方法获得的,即平均 Hounsfield 单位 (HU) 相对差值 (RELDIF) 和密度分布等值测试 (DDET),后者用于统计分析每个区域及其对侧的 HU 值。大脑皮层区域的自动分割非常完美,而基底节区域的自动分割只需极少的调整。对于测试集中的二分法-ASPECTS(ASPECTS <6),DDET 方法的接收器操作特征曲线下面积(AUC)为 0.85,RELDIF 方法为 0.84,CMLS 为 0.64,而神经放射科医师的接收器操作特征曲线下面积为 0.71-0.89。DDET 方法的准确度为 0.85,RELDIF 方法为 0.88,神经放射科医生的准确度为 0.83 - 0.96。DDET 法、RELDIF 法和黄金标准在平均 ASPECTS 上的等效性为 5%。对 AUC 和梗死检测准确性的非劣效性测试表明,DDET 和 RELDIF 与 CMLS 以及至少一位神经放射学家的方法相似。我们的方法与神经放射科医生和 CMLS 的评估结果一致,这表明我们的方法有潜力成为临床环境中的辅助工具,促进及时准确的中风诊断,尤其是在哥伦比亚等神经放射科医生有限的医疗环境中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Automated ASPECTS Segmentation and Scoring Tool: a Method Tailored for a Colombian Telestroke Network

Automated ASPECTS Segmentation and Scoring Tool: a Method Tailored for a Colombian Telestroke Network

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.

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来源期刊
Journal of Digital Imaging
Journal of Digital Imaging 医学-核医学
CiteScore
7.50
自引率
6.80%
发文量
192
审稿时长
6-12 weeks
期刊介绍: The Journal of Digital Imaging (JDI) is the official peer-reviewed journal of the Society for Imaging Informatics in Medicine (SIIM). JDI’s goal is to enhance the exchange of knowledge encompassed by the general topic of Imaging Informatics in Medicine such as research and practice in clinical, engineering, and information technologies and techniques in all medical imaging environments. JDI topics are of interest to researchers, developers, educators, physicians, and imaging informatics professionals. Suggested Topics PACS and component systems; imaging informatics for the enterprise; image-enabled electronic medical records; RIS and HIS; digital image acquisition; image processing; image data compression; 3D, visualization, and multimedia; speech recognition; computer-aided diagnosis; facilities design; imaging vocabularies and ontologies; Transforming the Radiological Interpretation Process (TRIP™); DICOM and other standards; workflow and process modeling and simulation; quality assurance; archive integrity and security; teleradiology; digital mammography; and radiological informatics education.
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