A Machine Learning Prediction Model to Identify Individuals at Risk of 5-Year Incident Stroke Based on Retinal Imaging
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 o...
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MDPI AG
2025-03-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/6/1917 |
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| author | Arun Govindaiah Tasin Bhuiyan R. Theodore Smith Mandip S. Dhamoon Alauddin Bhuiyan |
| author_facet | Arun Govindaiah Tasin Bhuiyan R. Theodore Smith Mandip S. Dhamoon Alauddin Bhuiyan |
| author_sort | Arun Govindaiah |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-573cf3b49bdb4a0181eb36a7ddfb3237 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-573cf3b49bdb4a0181eb36a7ddfb32372025-08-20T03:43:56ZengMDPI AGSensors1424-82202025-03-01256191710.3390/s25061917A Machine Learning Prediction Model to Identify Individuals at Risk of 5-Year Incident Stroke Based on Retinal ImagingArun Govindaiah0Tasin Bhuiyan1R. Theodore Smith2Mandip S. Dhamoon3Alauddin Bhuiyan4iHealthScreen Inc., Richmond Hill, NY 11418, USAiHealthScreen Inc., Richmond Hill, NY 11418, USABiomolecular Retinal Imaging, Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USADepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USAiHealthScreen Inc., Richmond Hill, NY 11418, USAStroke 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.https://www.mdpi.com/1424-8220/25/6/1917strokerisk scoremachine learningAI in medicine |
| spellingShingle | Arun Govindaiah Tasin Bhuiyan R. Theodore Smith Mandip S. Dhamoon Alauddin Bhuiyan A Machine Learning Prediction Model to Identify Individuals at Risk of 5-Year Incident Stroke Based on Retinal Imaging Sensors stroke risk score machine learning AI in medicine |
| title | A Machine Learning Prediction Model to Identify Individuals at Risk of 5-Year Incident Stroke Based on Retinal Imaging |
| title_full | A Machine Learning Prediction Model to Identify Individuals at Risk of 5-Year Incident Stroke Based on Retinal Imaging |
| title_fullStr | A Machine Learning Prediction Model to Identify Individuals at Risk of 5-Year Incident Stroke Based on Retinal Imaging |
| title_full_unstemmed | A Machine Learning Prediction Model to Identify Individuals at Risk of 5-Year Incident Stroke Based on Retinal Imaging |
| title_short | A Machine Learning Prediction Model to Identify Individuals at Risk of 5-Year Incident Stroke Based on Retinal Imaging |
| title_sort | machine learning prediction model to identify individuals at risk of 5 year incident stroke based on retinal imaging |
| topic | stroke risk score machine learning AI in medicine |
| url | https://www.mdpi.com/1424-8220/25/6/1917 |
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