| Title: | A Machine Learning Prediction Model to Identify Individuals at Risk of 5-Year Incident Stroke Based on Retinal Imaging |
| Journal: | Sensors |
| Published: | 19 Mar 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40293071/ |
| DOI: | https://doi.org/10.3390/s25061917 |
Publication 15565
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Abstract
Stroke is a leading cause of death and disability in developed countries. We validated an AI-based prediction model for incident stroke using sensors such as fundus cameras and ophthalmoscopes for retinal images, along with socio-demographic data and traditional risk factors. The model was trained on a proprietary dataset of over 6500 participants, including 171 with 5-year incident strokes and 242 with 10-year incident strokes. The model provides separate 5-year and 10-year risk scores. The model was externally validated on the UK Biobank dataset (3000 subjects with 5-year incident strokes). Using retinal imaging, our models identified individuals with 5-year incident strokes with 80% sensitivity, 82% specificity, and an AUC of 0.83, and predicted 10-year incidents with 72% sensitivity, 78% specificity, and an AUC of 0.79. In comparison, for the 10-year model, the AUC for the Framingham score was 0.73, and the CHADS2 score was 0.74. On the Biobank external dataset, our 5-year model (without retinal features) demonstrated moderate but lower sensitivity (69.3%) and specificity (66.4%) compared to its performance on the proprietary dataset (with retinal features). Using a multi-ethnic dataset, we developed and validated a prediction model that improves stroke risk identification for 5-year and 10-year incidences by incorporating retinal features.</p>
11 Keywords
- Aged
- Female
- Humans
- Incidence
- Machine Learning
- Male
- Middle Aged
- Retina
- Risk Assessment
- Risk Factors
- Stroke
5 Authors
- Arun Govindaiah
- Tasin Bhuiyan
- R. Theodore Smith
- Mandip S. Dhamoon
- Alauddin Bhuiyan
1 Application
| Application ID | Title |
|---|---|
| 44226 | A Machine Learning based screening system for Retinal and Systemic Diseases |
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