Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning

Abstract

Background and purpose: Alberta Stroke Program Early CT Score (ASPECTS) was devised as a systematic method to assess the extent of early ischemic change on noncontrast CT (NCCT) in patients with acute ischemic stroke (AIS). Our aim was to automate ASPECTS to objectively score NCCT of AIS patients.

Materials and methods: We collected NCCT images with a 5-mm thickness of 257 patients with acute ischemic stroke (<8 hours from onset to scans) followed by a diffusion-weighted imaging acquisition within 1 hour. Expert ASPECTS readings on DWI were used as ground truth. Texture features were extracted from each ASPECTS region of the 157 training patient images to train a random forest classifier. The unseen 100 testing patient images were used to evaluate the performance of the trained classifier. Statistical analyses on the total ASPECTS and region-level ASPECTS were conducted.

Results: For the total ASPECTS of the unseen 100 patients, the intraclass correlation coefficient between the automated ASPECTS method and DWI ASPECTS scores of expert readings was 0.76 (95% confidence interval, 0.67-0.83) and the mean ASPECTS difference in the Bland-Altman plot was 0.3 (limits of agreement, -3.3, 2.6). Individual ASPECTS region-level analysis showed that our method yielded κ = 0.60, sensitivity of 66.2%, specificity of 91.8%, and area under curve of 0.79 for 100 × 10 ASPECTS regions. Additionally, when ASPECTS was dichotomized (>4 and ≤4), κ = 0.78, sensitivity of 97.8%, specificity of 80%, and area under the curve of 0.89 were generated between the proposed method and expert readings on DWI.

Conclusions: The proposed automated ASPECTS scoring approach shows reasonable ability to determine ASPECTS on NCCT images in patients presenting with acute ischemic stroke.

Fig 1.

Examples of each ASPECTS region. L indicates lentiform; I, insula; C, caudate; IC, internal capsule; M, MCA.

Fig 2.

A flowchart of the training and testing processes used in the study for each ASPECTS region.

Fig 3.

A, Boxplot with a scatterplot showing the distribution of the automated CT ASPECTS at each individual ASPECTS on DWI. B, A Bland-Altman plot illustrating agreement between a total automated ASPECTS score and ASPECTS scores on DWI. Random jitter has been added to illustrate the number of measurements at each ASPECTS point. The horizontal black line represents the mean difference in the ASPECTS score between the 2 methods, while the dotted lines represent a 1.96 SD around the difference.

Fig 4.

Examples of DWI ASPECTS, the automated CT ASPECTS derived in this study, and expert-read CT ASPECTS. ASPECTS regions with ischemic changes are shown in color.