External Validation of e-ASPECTS Software for Interpreting Brain CT in Stroke.
External Validation of e-ASPECTS Software for Interpreting Brain CT in Stroke.
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DOI：
10.1002/ana.26495
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发表时间：
2022-12
影响因子：
11.2
通讯作者：
Wardlaw, Joanna M.
中科院分区：
文献类型：
--
作者：
Mair, Grant;White, Philip;Bath, Philip M.;Muir, Keith W.;Salman, Rustam Al-Shahi;Martin, Chloe;Dye, David;Chappell, Francesca M.;Vacek, Adam;von Kummer, Rudiger;Macleod, Malcolm;Sprigg, Nikola;Wardlaw, Joanna M.
本研究的目的是在卒中患者中测试e-ASPECTS软件。作为一种决策支持工具，e-ASPECTS可以在计算机断层扫描（CT）成像上检测缺血或出血的特征，并使用阿尔伯塔卒中项目早期CT评分（ASPECTS）量化缺血程度。使用来自9项卒中研究的CT，我们将软件与盲法专家进行了比较。根据软件使用适应症，我们评估了有/无大脑中动脉（MCA）缺血但无其他卒中原因的患者的e-ASPECTS结果。在软件预期用途之外的分析中，我们用非MCA缺血、出血和模拟物丰富了我们的数据集，以模拟代表性的“前门”医院人群。以最终诊断为参考标准，我们测试了e-ASPECTS在代表性人群中识别卒中特征（缺血、动脉过度衰减和出血）的诊断准确性。我们纳入了4，100例患者（51%为女性，中位年龄= 78岁，美国国立卫生研究院卒中量表[NIHSS] = 10，发作至扫描= 2.5小时）。最终诊断为缺血（78%）、出血（14%）或疑似出血（8%）。在3，035个具有专家评定的ASPECTS的CT中，大多数（2084/3035，69%）e-ASPECTS结果在一个专家点内。在代表性人群中，e-ASPECTS检测缺血特征的诊断准确率为71%（95%置信区间[CI] = 70-72%），出血的诊断准确率为85%（83-86%）。软件比专家识别出更多的假阳性缺血（12% vs 2%）和出血（14% vs <1%）。在独立测试中，e-ASPECTS提供了与专家和过度调用中风特征的中度一致性。因此，在广泛实施中风决策支持软件之前，需要进行未来的前瞻性试验，测试人工智能（AI）软件对患者护理和结局的影响。神经网络2022;92：943-957
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
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DOI：
10.12688/amrcopenres.12904.1
发表时间：
2020-04-28
期刊：
AMRC open research
影响因子：
--
作者：
Mair G;Chappell F;Martin C;Dye D;Bath PM;Muir KW;von Kummer R;Al-Shahi Salman R;Sandercock PAG;Macleod M;Sprigg N;White P;Wardlaw JM
通讯作者：
Wardlaw JM
影响因子：
105.7
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
Moons, Karel G. M.
影响因子：
4
作者：
Freeman, Suzanne C.;Kerby, Clareece R.;Sutton, Alex J.
通讯作者：
Sutton, Alex J.
影响因子：
6.7
作者：
Nagel, Simon;Sinha, Devesh;Grunwald, Iris Q.
通讯作者：
Grunwald, Iris Q.
影响因子：
6.7
作者：
El-Tawil, Salwa;Wardlaw, Joanna;Muir, Keith W.
通讯作者：
Muir, Keith W.