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Predicting ischemic stroke risk from atrial fibrillation based on multi-spectral fundus images using deep learning
Hui Li1,2,3,4,5, Mengdi Gao1,2,3,4,5, Haiqing Song6
1Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China.
Insights
Deep learning models can predict ischemic stroke (IS) risk in atrial fibrillation (AF) patients using multi-spectrum fundus images. Combining different spectral images enhances prediction accuracy for early cardiovascular and cerebrovascular disease prevention.
Area of Science:
- Ophthalmology
- Cardiology
- Neurology
- Artificial Intelligence
Background:
- Atrial fibrillation (AF) significantly increases the risk of ischemic stroke (IS) within one year.
- Both AF and IS share early manifestations of ischemia and hypoxia in small blood vessels.
- The potential of using multi-spectrum fundus imaging for predicting IS risk in AF patients remains underexplored.
Purpose of the Study:
- To predict the risk of IS secondary to AF using deep learning on multi-spectrum fundus images.
- To investigate the utility of different spectral wavelengths and their combinations for IS risk prediction.
- To explore a novel approach for the early detection and prevention of IS in AF patients.
Main Methods:
- Recruited 150 AF patients (no IS within 1 year) and 100 IS patients (with AF history).
- Collected fundus images at 548, 605, and 810 nm wavelengths.
- Trained three deep neural network (DNN) models (Inception V3, ResNet50, SE50) using spectral fundus images and clinical data.
Main Results:
- All DNN models achieved >78% accuracy using single or multi-spectral images.
- The 605 nm spectral images provided more stable IS detection performance.
- Multi-spectral combination models demonstrated higher Area Under the Curve (AUC) scores than single-spectral models.
Conclusions:
- Multi-spectrum fundus imaging combined with deep learning can effectively predict IS risk in AF patients.
- Utilizing diverse spectral fundus images aids in the early prevention of cardiovascular and cerebrovascular diseases.
- This study offers a novel preliminary exploration for predicting challenging conditions like IS.
Background:
Ischemic stroke (IS) is one of the most common serious secondary diseases of atrial fibrillation (AF) within 1 year after its occurrence, both of which have manifestations of ischemia and hypoxia of the small vessels in the early phase of the condition. The fundus is a collection of capillaries, while the retina responds differently to light of different wavelengths. Predicting the risk of IS occurring secondary to AF, based on subtle differences in fundus images of different wavelengths, is yet to be explored. This study was conducted to predict the risk of IS occurring secondary to AF based on multi-spectrum fundus images using deep learning.
Methods:
A total of 150 AF participants without suffering from IS within 1 year after discharge and 100 IS participants with persistent arrhythmia symptoms or a history of AF diagnosis in the last year (defined as patients who would develop IS within 1 year after AF, based on fundus pathological manifestations generally prior to symptoms of the brain) were recruited. Fundus images at 548, 605, and 810 nm wavelengths were collected. Three classical deep neural network (DNN) models (Inception V3, ResNet50, SE50) were trained. Sociodemographic and selected routine clinical data were obtained.
Results:
The accuracy of all DNNs with the single-spectral or multi-spectral combination images at the three wavelengths as input reached above 78%. The IS detection performance of DNNs with 605 nm spectral images as input was relatively more stable than with the other wavelengths. The multi-spectral combination models acquired a higher area under the curve (AUC) scores than the single-spectral models.
Conclusions:
The probability of IS secondary to AF could be predicted based on multi-spectrum fundus images using deep learning, and combinations of multi-spectrum images improved the performance of DNNs. Acquiring different spectral fundus images is advantageous for the early prevention of cardiovascular and cerebrovascular diseases. The method in this study is a beneficial preliminary and initiative exploration for diseases that are difficult to predict the onset time such as IS.
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