Detection of Hydroxychloroquine Retinopathy via Hyperspectral and Deep Learning through Ophthalmoscope Images

Abstract

Hydroxychloroquine, also known as quinine, is primarily utilized to manage various autoimmune diseases, such as systemic lupus erythematosus, rheumatoid arthritis, and Sjogren’s syndrome. However, this drug has side effects, including diarrhea, blurred vision, headache, skin itching, poor appetite, and gastrointestinal discomfort. Blurred vision is caused by irreversible retinal damages and can only be mitigated by reducing hydroxychloroquine dosage or discontinuing the drug under a physician’s supervision. In this study, color fundus images were utilized to identify differences in lesions caused by hydroxychloroquine. A total of 176 color fundus images were captured from a cohort of 91 participants, comprising 25 patients diagnosed with hydroxychloroquine retinopathy and 66 individuals without any retinopathy. The mean age of the participants was 75.67 ± 7.76. Following the selection of a specific region of interest within each image, hyperspectral conversion technology was employed to obtain the spectrum of the sampled image. Spectral analysis was then conducted to discern differences between normal and hydroxychloroquine-induced lesions that are imperceptible to the human eye on the color fundus images. We implemented a deep learning model to detect lesions, leveraging four artificial neural networks (ResNet50, Inception_v3, GoogLeNet, and EfficientNet). The overall accuracy of ResNet50 reached 93% for the original images (ORIs) and 96% for the hyperspectral images (HSIs). The overall accuracy of Inception_v3 was 87% for ORIs and 91% for HSI, and that of GoogLeNet was 88% for ORIs and 91% for HSIs. Finally, EfficientNet achieved an overall accuracy of 94% for ORIs and 97% for HSIs.

Glaucoma Detection through a Novel Hyperspectral Imaging Band Selection and Vision Transformer Integration

“…The results demonstrate high overall accuracy, with EfficientNet achieving 94% accuracy for original images and 97% accuracy for hyperspectral images. This method offers a potential advanced diagnostic tool for identifying imperceptible lesions caused by hydroxychloroquine [ 38 ].…”

Grade classification of Alzheimer's disease using hyperspectral ophthalmoscope images

“…10 In 2023, we utilized color fundus images and hyperspectral technology to detect imperceptible lesions caused by hydroxychloroquine, achieving high accuracy with deep learning models, including ResNet50, Inception v3, GoogLeNet, and EfficientNet. 11 Still, the adoption of hyperspectral data for eye disease diagnosis faces challenges, primarily due to the immense data volume they generate.…”