Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Department of Neurosurgery, Mayo Clinic, Rochester, MN. USA.
Interv Neuroradiol. 2021 Dec;27(6):781-787. doi: 10.1177/15910199211011861. Epub 2021 Apr 14.
There is increased interest in the use of artificial intelligence-based (AI) software packages in the evaluation of neuroimaging studies for acute ischemic stroke. We studied whether, compared to standard image interpretation without AI, Brainomix e-ASPECTS software improved interobserver agreement and accuracy in detecting ASPECTS regions affected in anterior circulation LVO.
We included 60 consecutive patients with anterior circulation LVO who had TICI 3 revascularization within 60 minutes of their baseline CT. A total of 16 readers, including senior neuroradiologists, junior neuroradiologists and vascular neurologists participated. Readers interpreted CT scans on independent workstations and assessed final ASPECTS and evaluated whether each individual ASPECTS region was affected. Two months later, readers again evaluated the CT scans, but with assistance of e-ASPECTS software. We assessed interclass correlation coefficient for total ASPECTS and interobserver agreement with Fleiss' Kappa for each ASPECTS region with and without assistance of the e-ASPECTS. We also assessed accuracy for the readers with and without e-ASPECTS assistance. In our assessment of accuracy, ground truth was the 24 hour CT in this cohort of patients who had prompt and complete revascularization.
Interclass correlation coefficient for total ASPECTS without e-ASPECTS assistance was 0.395, indicating fair agreement compared, to 0.574 with e-ASPECTS assistance, indicating good agreement (P < 0.01). There was significant improvement in inter-rater agreement with e-ASPECTS assistance for each individual region with the exception of M6 and caudate. The e-ASPECTS software had higher accuracy than the overall cohort of readers (with and without e-ASPECTS assistance) for every region except the caudate.
Use of Brainomix e-ASPECTS software resulted in significant improvements in inter-rater agreement and accuracy of ASPECTS score evaluation in a large group of neuroradiologists and neurologists. e-ASPECTS software was more predictive of final infarct/ASPECTS than the overall group interpreting the CT scans with and without e-ASPECTS assistance.
在急性缺血性卒中的神经影像学研究评估中,使用基于人工智能(AI)的软件包的兴趣日益增加。我们研究了与不使用 AI 的标准图像解释相比,Brainomix e-ASPECTS 软件是否能提高观察者间对前循环 LVO 中 ASPECTS 区域的一致性和准确性。
我们纳入了 60 例前循环 LVO 患者,这些患者在基线 CT 后 60 分钟内接受 TICI 3 再通。共有 16 名读者参与,包括高级神经放射学家、初级神经放射学家和血管神经学家。读者在独立的工作站上解释 CT 扫描,并评估最终的 ASPECTS,并评估每个单独的 ASPECTS 区域是否受到影响。两个月后,读者再次评估 CT 扫描,但在 e-ASPECTS 软件的帮助下进行。我们评估了总 ASPECTS 的组内相关系数,并使用 Fleiss' Kappa 评估了每个 ASPECTS 区域在有无 e-ASPECTS 帮助下的观察者间一致性。我们还评估了有和没有 e-ASPECTS 辅助的读者的准确性。在我们的准确性评估中,地面真相是该队列中 24 小时 CT 的患者,他们有及时和完全的再通。
无 e-ASPECTS 辅助时的总 ASPECTS 组内相关系数为 0.395,表明一致性为中等,而有 e-ASPECTS 辅助时为 0.574,表明一致性为良好(P<0.01)。除 M6 和尾状核外,每个单独区域的观察者间一致性都有显著改善。除尾状核外,e-ASPECTS 软件的准确性高于总体读者队列(有和没有 e-ASPECTS 辅助)。
Brainomix e-ASPECTS 软件的使用显著提高了一大组神经放射学家和神经科医生对 ASPECTS 评分评估的观察者间一致性和准确性。e-ASPECTS 软件比解释 CT 扫描的整体读者组(有和没有 e-ASPECTS 辅助)更能预测最终的梗死/ASPECTS。