AI Article Synopsis
- Bietti crystalline dystrophy (BCD) is a challenging inherited retinal disease that requires early diagnosis, which this study aims to improve through deep learning techniques.
- The research involves labeling ultra-wide-field color fundus photographs to classify images as BCD, retinitis pigmentosa, or normal, and further categorizing BCD patients into three clinical stages.
- Three deep learning models (ResNeXt, Wide ResNet, and ResNeSt) were evaluated for their diagnostic accuracy and effectiveness, resulting in the creation of a significant BCD database for the Chinese population and a promising automated diagnosis method for future clinical use.
Article Abstract
Bietti crystalline dystrophy (BCD) is an autosomal recessive inherited retinal disease (IRD) and its early precise diagnosis is much challenging. This study aims to diagnose BCD and classify the clinical stage based on ultra-wide-field (UWF) color fundus photographs (CFPs) via deep learning (DL). All CFPs were labeled as BCD, retinitis pigmentosa (RP) or normal, and the BCD patients were further divided into three stages. DL models ResNeXt, Wide ResNet, and ResNeSt were developed, and model performance was evaluated using accuracy and confusion matrix. Then the diagnostic interpretability was verified by the heatmaps. The models achieved good classification results. Our study established the largest BCD database of Chinese population. We developed a quick diagnosing method for BCD and evaluated the potential efficacy of an automatic diagnosis and grading DL algorithm based on UWF fundus photography in a Chinese cohort of BCD patients.
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Source |
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11365386 | PMC |
| http://dx.doi.org/10.1016/j.isci.2024.110579 | DOI Listing |
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