- Tablet-based, AI-driven eye tracking combined with clinical variables enabled accurate, feasible early screening for 3-month PSCI; nomogram AUC 0.86.
- Independent predictors of 3-month PSCI were older age, lower education, higher admission NIHSS score, and prolonged correct saccade latency.
- Acute-phase eye-tracking differences: longer saccade latency, higher uncorrected error rates, and reduced novelty preference suggest cognitive dysfunction poststroke.
JMIR Mhealth Uhealth. 2026 Aug 6;14:e83194. doi: 10.2196/83194.
ABSTRACT
BACKGROUND: Poststroke cognitive impairment (PSCI) is a common and disabling complication after stroke; however, early screening remains challenging due to limited access to neuropsychological testing and the high cost of neuroimaging. Portable, tablet-based eye-tracking technology may offer a scalable, low-cost solution for early PSCI detection.
OBJECTIVE: This study aimed to evaluate the clinical utility of a tablet-based, AI-driven eye-tracking system for early screening of PSCI at 3 months after acute ischemic stroke. We sought to quantify oculomotor-cognitive associations and develop a practical nomogram for individualized risk prediction.
METHODS: We prospectively enrolled 142 hospitalized patients with acute cerebral infarction between May 2023 and October 2024, of whom 122 completed the 3-month follow-up and were included in the final analysis, along with 20 healthy community-dwelling controls. All patients underwent tablet-based eye tracking (visual paired comparison and antisaccade tasks) during the acute phase, as well as baseline and 3-month neuropsychological assessments. PSCI was defined using validated cutoffs. Multivariable logistic regression was used to identify independent predictors, and a nomogram was constructed. Internal validation was performed using bootstrap resampling (1000 samples).
RESULTS: At 3 months, out of 122 patients, 47 (38.5%) met PSCI criteria. Compared with patients with non-PSCI (n=75), patients with PSCI showed significantly prolonged correct saccade latency (median 322.96, IQR 209.45-445.59 ms vs 194.55, IQR 141.50-299.75 ms; Z=-4.03, P<.001), increased uncorrected error rate (median 30.00%, IQR 15.00%-42.00% vs 5.00%, IQR 0.00%-28.00%; Z=-4.24, P<.001), and reduced novelty preference ratio (median 1.44, IQR 0.97-1.70 vs 2.12, IQR 1.27-4.56; Z=-3.44, P=.001). Multivariable analysis identified 4 independent predictors of 3-month PSCI: older age (odds ratio [OR] 1.067 per year, 95% CI 1.009-1.129; P=.02), lower education level (OR 0.841 per year, 95% CI 0.708-0.999; P=.049), higher NIHSS (National Institutes of Health Stroke Scale) scores (OR 1.557 per point, 95% CI 1.075-2.256; P=.02), and prolonged correct saccade latency (OR 1.004 per ms, 95% CI 1.000-1.007; P=.04). A nomogram incorporating these 4 factors achieved good discriminative performance (area under the receiver operating characteristic curve 0.86, 95% CI 0.793-0.927) with satisfactory calibration.
CONCLUSIONS: Age, education, admission NIHSS, and correct saccade latency were identified as possible independent predictors of 3-month PSCI in this cohort. The tablet-based eye-tracking system, when combined with clinical variables, may represent a feasible approach for early PSCI screening. A nomogram based on these variables demonstrated high accuracy and potential clinical utility for early PSCI identification. This approach may facilitate early identification of high-risk patients and enable timely, personalized interventions in resource-limited settings.
PMID:42561408 | DOI:10.2196/83194
Share Evidence Blueprint
Save to Google Notes
Search Google Scholar
Save as PDF
⭐ My Revision List
