Early Diagnosis of Ischemic Stroke on Non-Contrast CT Scans Using a Convolutional Neural Network: A Case Study from...

In low- and middle-income countries, the scarcity of specialist radiologists contributes to delays in ischemic stroke diagnosis. Early recognition is essential for initiating interventions that can reduce morbidity and mortality.

To develop and assess a convolutional neural network (CNN) capable of detecting early ischemic stroke on non-contrast computed tomography (NCCT) scans and to develop a web-based platform for demonstrating potential clinical application.

A retrospective diagnostic accuracy study was performed using 1,000 NCCT scans (500 stroke positive, 500 controls) from adult patients at Mulago National Referral Hospital between January 2022 and June 2023. Lesions were independently annotated by three radiologists (κ = 0.83). Preprocessing included intensity normalization and texture analysis. The CNN was trained with dropout, L2 regularization, and batch normalization. Five-fold cross-validation was conducted. Performance was compared with a senior radiologist (> 5 years' experience) using a balanced 30- case subset. A web-based interface was created for deployment.

The CNN achieved a mean accuracy of 90.2% (± 1.8; 95% CI: 88.0-92.4), sensitivity of 91.4% (± 1.7; 95% CI: 89.3-93.5), specificity of 89.0% (± 2.1; 95% CI: 86.4-91.6), and an area under the receiver operating characteristic curve (AUC) of 91.3% (± 1.7; 95% CI: 89.2-93.4) across five-fold cross-validation. On the independent radiologist comparison subset, the CNN achieved a sensitivity of 86.7% and specificity of 88.7%, missing two stroke cases, while the senior radiologist achieved a sensitivity of 100.0% and specificity of 95.0% with no missed stroke cases. Despite the radiologist demonstrating slightly higher diagnostic performance, the overall difference was not statistically significant (p = 0.21). The model processed scans in approximately 3 seconds per case, compared with approximately 5 minutes required for radiologist interpretation. In addition, a browser-based interface was developed to support scan upload and automated analysis for potential clinical deployment.

The model demonstrated accuracy comparable to that of a senior radiologist while delivering results in significantly less time. These findings suggest that the integrated web platform has promising potential to support stroke triage, particularly in settings with limited radiology expertise.