• Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, National Engineering Research Center for Ophthalmology, Beijing 100730, China;
Jin Zibing, Email: jinzb502@ccmu.edu.cn
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Lattice retinal degeneration is a common peripheral retinal degenerative condition and is widely recognized as a significant precursor to retinal detachment, resulting in severe visual loss. Recent advances in deep learning technologies have driven the development and adoption of automated screening systems for lattice retinal degeneration using ultra-widefield fundus imaging. These systems have demonstrated notable success in large-scale screening of peripheral retinal diseases, offering valuable support for the early identification and risk stratification of lattice degeneration. Currently, retinal laser photocoagulation remains the mainstay treatment for lattice degeneration. This intervention effectively mitigates the risk of rhegmatogenous retinal detachment. However, controversies persist regarding the optimal selection of treatment candidates and the evaluation of therapeutic efficacy. In the future, the continuous evolution of imaging analysis techniques and artificial intelligence holds promise for the development of personalized and precision-based intervention strategies. Such advancements are expected to provide more robust evidence to guide the diagnosis and treatment of lattice retinal degeneration, ultimately improving patient outcomes.

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