EyeHealer:A large-scale anterior eye segment dataset with eye structure and lesion annotations

Anterior segment eye diseases account for a significant proportion of presentations to eye clinics worldwide,including diseases associated with corneal pathologies,anterior chamber abnormalities(e.g.blood or inflammation),and lens diseases.The construction of an automatic tool for segmentation of anterior segment eye lesions would greatly improve the efficiency of clinical care.With research on artificial intelligence progressing in recent years,deep learning models have shown their superiority in image classification and segmentation.The training and evaluation of deep learning models should be based on a large amount of data annotated with expertise;however,such data are relatively scarce in the domain of medicine.Herein,the authors developed a new medical image annotation system,called EyeHealer.It is a large-scale anterior eye segment dataset with both eye structures and lesions annotated at the pixel level.Comprehensive experiments were conducted to verify its performance in disease classification and eye lesion segmentation.The results showed that semantic segmentation models outperformed medical segmentation models.This paper describes the establishment of the system for automated classification and segmentation tasks.The dataset will be made publicly available to encourage future research in this area.

Genetic and environmental factors strongly influence risk,severity and progression of age-related macular degeneration

Age-related macular degeneration(AMD)is characterized by complex interactions between genetic and environmental factors.Here we genotyped the selected 25 single-nucleotide polymorphisms(SNPs)in 983 cases with advanced AMD and 271 cases with intermediate AMD and build an AMD life-risk score model for assessment of progression from intermediate to advanced AMD.We analyzed the performance of the prediction model for geographic atrophy progressors or choroidal neovascularization progressors versus non-progressors based on the 25 SNPs plus body mass index and smoking status.Our results suggest that a class prediction algorithm can be used for the risk assessment of progression from intermediate to late AMD stages.The algorithm could also be potentially applied for therapeutic response,and toward personalized care and precision medicine.