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A new study conducted by researchers at the University of Electronic Science and Technology of China explores the use of artificial intelligence (AI) in diagnosing myopia. The study developed an interpretable deep learning model that achieved promising results in assessing myopia status using retinal refraction maps. The AI model demonstrated performance that surpassed human capabilities, indicating its potential for use in clinical practice. This finding suggests that AI can be utilized in scenarios where human knowledge is limited. The study provides valuable insights into the application of AI in the field of ophthalmology. [Extracted from the article]

A report from Monash University in Melbourne, Australia discusses research on retinal diseases and conditions. The study highlights the challenges of training deep learning models on medical datasets that have a long-tailed data distribution, with a few classes dominating the data and rare diseases having limited samples. To address these challenges, the researchers propose a novel method that incorporates prior knowledge in ophthalmology, adopts a class-balanced sampling strategy, and uses hybrid knowledge distillation. The experimental results show that their approach outperforms existing methods, particularly for rare diseases. The research has been peer-reviewed and published in IEEE Transactions on Medical Imaging. [Extracted from the article]

A study conducted by Fudan University in Shanghai, China, explored the use of artificial intelligence (AI) and big data analytics to predict the vault and size of an implantable collamer lens (ICL) for myopia correction. The researchers collected data from over 6,000 eyes implanted with an ICL and used regression and classification models, such as Random Forest, Gradient Boosting, and XGBoost, to predict the vault and ICL size. The results showed that these AI models had satisfactory accuracy in predicting the vault and sizing the ICL. The researchers concluded that AI could assist ophthalmologists in improving the safety and outcomes of ICL surgery. [Extracted from the article]

Keywords for this news article include: Guangzhou, People's Republic of China, Asia, Vision Science, Science, Sun Yat-sen University. The news reporters obtained a quote from the research from Sun Yat-sen University, "Medical Transformer network and Small Attention U-Net were used to automatically segment the choroid boundaries and the nulla (the deepest point within the fovea). Keywords: Guangzhou; People's Republic of China; Asia; Vision Science; Science EN Guangzhou People's Republic of China Asia Vision Science Science 4796 4796 1 08/14/23 20230818 NES 230818 2023 AUG 18 (NewsRx) -- By a News Reporter-Staff News Editor at Health & Medicine Week -- Investigators publish new report on Science - Vision Science. [Extracted from the article]