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.

Introduction: The quality of stroke care in India is unknown. This multicentric study from the Indian Registry of Stroke Care Quality (RES-Q) provides prospectively entered stroke data from 46 stroke centers across the country. We analyzed demographics, therapeutic interventions, quality metrics, and functional outcomes of ischemic stroke from India. Methods: It was an analysis of prospectively entered data into the RES-Q from 2022 to 2024. The inclusion criteria were patients ≥18 years of age with an acute ischemic stroke or transient ischemic attack based on the AHA/ASA 2019 criteria within 2 weeks of stroke onset. All demographic, clinical, and radiological details were entered into a predesigned proforma. All quality and performance measures in the hospital were collected. Results: Approximately 80% of all strokes (n = 7,337) during the study period were ischemic. The median age was 61 years (interquartile range [IQR] 52–71), with 67% being male. The median National Institute of Health Stroke Scale at admission was 7 (IQR 4–12). Brain parenchymal imaging was done for all patients, but vascular imaging was performed for 60% of patients only. The thrombolysis rate was 32% with a median door-to-needle time of 37 min. Endovascular thrombectomy was performed in 6.5% of cases. Swallowing assessment was done in 72% of patients within 24 h of admission and 90% received physiotherapy in the stroke unit. Conclusion: This landmark dataset marks the first comprehensive nationwide effort to assess stroke care quality in India. Routine quality monitoring through platforms like RES-Q can help standardize care, reduce disparities, and enable hospitals to benchmark performance and implement targeted improvements.

Neurol India is an peer-reviewed biomedical periodical of Neurological Society of India.