Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
Translational and Clinical Research Institute, Newcastle University and Newcastle upon Tyne Hospitals NHS Trust, Newcastle upon Tyne, UK.
Ann Neurol. 2022 Dec;92(6):943-957. doi: 10.1002/ana.26495. Epub 2022 Sep 23.
The purpose of this study was to test e-ASPECTS software in patients with stroke. Marketed as a decision-support tool, e-ASPECTS may detect features of ischemia or hemorrhage on computed tomography (CT) imaging and quantify ischemic extent using Alberta Stroke Program Early CT Score (ASPECTS).
Using CT from 9 stroke studies, we compared software with masked experts. As per indications for software use, we assessed e-ASPECTS results for patients with/without middle cerebral artery (MCA) ischemia but no other cause of stroke. In an analysis outside the intended use of the software, we enriched our dataset with non-MCA ischemia, hemorrhage, and mimics to simulate a representative "front door" hospital population. With final diagnosis as the reference standard, we tested the diagnostic accuracy of e-ASPECTS for identifying stroke features (ischemia, hyperattenuated arteries, and hemorrhage) in the representative population.
We included 4,100 patients (51% women, median age = 78 years, National Institutes of Health Stroke Scale [NIHSS] = 10, onset to scan = 2.5 hours). Final diagnosis was ischemia (78%), hemorrhage (14%), or mimic (8%). From 3,035 CTs with expert-rated ASPECTS, most (2084/3035, 69%) e-ASPECTS results were within one point of experts. In the representative population, the diagnostic accuracy of e-ASPECTS was 71% (95% confidence interval [CI] = 70-72%) for detecting ischemic features, 85% (83-86%) for hemorrhage. Software identified more false positive ischemia (12% vs 2%) and hemorrhage (14% vs <1%) than experts.
On independent testing, e-ASPECTS provided moderate agreement with experts and overcalled stroke features. Therefore, future prospective trials testing impacts of artificial intelligence (AI) software on patient care and outcome are required before widespread implementation of stroke decision-support software. ANN NEUROL 2022;92:943-957.
本研究旨在测试 e-ASPECTS 软件在脑卒中患者中的应用。该软件被宣传为一种决策支持工具，可在计算机断层扫描（CT）影像上检测到缺血或出血特征，并使用 Alberta 卒中项目早期 CT 评分（ASPECTS）量化缺血程度。
我们使用 9 项脑卒中研究的 CT 数据，将软件结果与盲法专家进行比较。根据软件使用指征，我们评估了 e-ASPECTS 软件在有无大脑中动脉（MCA）缺血但无其他卒中病因的患者中的应用结果。在超出软件预期用途的分析中，我们通过非 MCA 缺血、出血和模拟病例来丰富数据集，以模拟具有代表性的“前门”医院人群。以最终诊断为参考标准，我们测试了 e-ASPECTS 在代表人群中识别卒中特征（缺血、高信号动脉和出血）的诊断准确性。
共纳入 4100 例患者（51%为女性，中位年龄为 78 岁，美国国立卫生研究院卒中量表 [NIHSS]评分为 10 分，发病至扫描时间为 2.5 小时）。最终诊断为缺血（78%）、出血（14%）或模拟病例（8%）。在有专家评分的 3035 例 CT 中，大多数（2084/3035，69%）e-ASPECTS 结果与专家评分相差 1 分以内。在代表性人群中，e-ASPECTS 检测缺血特征的诊断准确性为 71%（95%置信区间 [CI]为 70-72%），检测出血的诊断准确性为 85%（83-86%）。与专家相比，软件检测到更多的假阳性缺血（12%比 2%）和出血（14%比 <1%）。
在独立测试中，e-ASPECTS 与专家的一致性为中等水平，且过度诊断了卒中特征。因此，在广泛实施卒中决策支持软件之前，需要进行前瞻性临床试验来检验人工智能（AI）软件对患者护理和结局的影响。
