Neves Gabriel, Warman Pranav I, Warman Anmol, Warman Roshan, Bueso Tulio, Vadhan Jason D, Windisch Thomas
Department of Neurology, Texas Tech University Medical Sciences Center, Lubbock, Texas, USA.
Caire Health Inc., Tampa, Florida, USA.
World Neurosurg. 2023 May;173:e800-e807. doi: 10.1016/j.wneu.2023.03.019. Epub 2023 Mar 9.
Artificial intelligence applications have gained traction in the field of cerebrovascular disease by assisting in the triage, classification, and prognostication of both ischemic and hemorrhagic stroke. The Caire ICH system aims to be the first device to move into the realm of assisted diagnosis for intracranial hemorrhage (ICH) and its subtypes.
A single-center retrospective dataset of 402 head noncontrast CT scans (NCCT) with an intracranial hemorrhage were retrospectively collected from January 2012 to July 2020; an additional 108 NCCT scans with no intracranial hemorrhage findings were also included. The presence of an ICH and its subtype were determined from the International Classification of Diseases-10 code associated with the scan and validated by an expert panel. We used the Caire ICH vR1 to analyze these scans, and we evaluated its performance in terms of accuracy, sensitivity, and specificity.
We found the Caire ICH system to have an accuracy of 98.05% (95% confidence interval [CI]: 96.44%-99.06%), a sensitivity of 97.52% (95% CI: 95.50%-98.81%), and a specificity of 100% (95% CI: 96.67%-100.00%) in the detection of ICH. Experts reviewed the 10 incorrectly classified scans.
The Caire ICH vR1 algorithm was highly accurate, sensitive, and specific in detecting the presence or absence of an ICH and its subtypes in NCCTs. This work suggests that the Caire ICH device has potential to minimize clinical errors in ICH diagnosis that could improve patient outcomes and current workflows as both a point-of-care tool for diagnostics and as a safety net for radiologists.
人工智能应用通过辅助缺血性和出血性中风的分诊、分类及预后评估，在脑血管疾病领域受到关注。Caire ICH系统旨在成为首个进入颅内出血（ICH）及其亚型辅助诊断领域的设备。
回顾性收集2012年1月至2020年7月间402例颅内出血头部非增强CT扫描（NCCT）的单中心回顾性数据集；另外还纳入了108例无颅内出血发现的NCCT扫描。根据与扫描相关的国际疾病分类第10版代码确定ICH及其亚型的存在，并由专家小组进行验证。我们使用Caire ICH vR1分析这些扫描，并从准确性、敏感性和特异性方面评估其性能。
我们发现Caire ICH系统在检测ICH时，准确性为98.05%（95%置信区间[CI]：96.44%-99.06%），敏感性为97.52%（95%CI：95.50%-98.81%），特异性为100%（95%CI：96.67%-100.00%）。专家对10例分类错误的扫描进行了复查。
Caire ICH vR1算法在检测NCCT中ICH及其亚型的存在与否方面具有高度准确性、敏感性和特异性。这项工作表明，Caire ICH设备有潜力将ICH诊断中的临床错误降至最低，这可能改善患者预后和当前工作流程，既作为即时诊断工具，也作为放射科医生的安全保障。
