The rise of artifcial intelligence(AI)has brought breakthroughs in many areas of medicine.In ophthalmology,AI has delivered robust results in the screening and detection of diabetic retinopathy,age-related macular deg...The rise of artifcial intelligence(AI)has brought breakthroughs in many areas of medicine.In ophthalmology,AI has delivered robust results in the screening and detection of diabetic retinopathy,age-related macular degeneration,glaucoma,and retinopathy of prematurity.Cataract management is another feld that can beneft from greater AI application.Cataract is the leading cause of reversible visual impairment with a rising global clinical burden.Improved diagnosis,monitoring,and surgical management are necessary to address this challenge.In addition,patients in large developing countries often sufer from limited access to tertiary care,a problem further exacerbated by the ongoing COVID-19 pandemic.AI on the other hand,can help transform cataract management by improving automation,efcacy and overcoming geographical barriers.First,AI can be applied as a telediagnostic platform to screen and diagnose patients with cataract using slit-lamp and fundus photographs.This utilizes a deep-learning,convolutional neural network(CNN)to detect and classify referable cataracts appropriately.Second,some of the latest intraocular lens formulas have used AI to enhance prediction accuracy,achieving superior postoperative refractive results compared to traditional formulas.Third,AI can be used to augment cataract surgical skill training by identifying diferent phases of cataract surgery on video and to optimize operating theater workfows by accurately predicting the duration of surgical procedures.Fourth,some AI CNN models are able to efectively predict the progression of posterior capsule opacifcation and eventual need for YAG laser capsulotomy.These advances in AI could transform cataract management and enable delivery of efcient ophthalmic services.The key challenges include ethical management of data,ensuring data security and privacy,demonstrating clinically acceptable performance,improving the generalizability of AI models across heterogeneous populations,and improving the trust of end-users.展开更多
文摘The rise of artifcial intelligence(AI)has brought breakthroughs in many areas of medicine.In ophthalmology,AI has delivered robust results in the screening and detection of diabetic retinopathy,age-related macular degeneration,glaucoma,and retinopathy of prematurity.Cataract management is another feld that can beneft from greater AI application.Cataract is the leading cause of reversible visual impairment with a rising global clinical burden.Improved diagnosis,monitoring,and surgical management are necessary to address this challenge.In addition,patients in large developing countries often sufer from limited access to tertiary care,a problem further exacerbated by the ongoing COVID-19 pandemic.AI on the other hand,can help transform cataract management by improving automation,efcacy and overcoming geographical barriers.First,AI can be applied as a telediagnostic platform to screen and diagnose patients with cataract using slit-lamp and fundus photographs.This utilizes a deep-learning,convolutional neural network(CNN)to detect and classify referable cataracts appropriately.Second,some of the latest intraocular lens formulas have used AI to enhance prediction accuracy,achieving superior postoperative refractive results compared to traditional formulas.Third,AI can be used to augment cataract surgical skill training by identifying diferent phases of cataract surgery on video and to optimize operating theater workfows by accurately predicting the duration of surgical procedures.Fourth,some AI CNN models are able to efectively predict the progression of posterior capsule opacifcation and eventual need for YAG laser capsulotomy.These advances in AI could transform cataract management and enable delivery of efcient ophthalmic services.The key challenges include ethical management of data,ensuring data security and privacy,demonstrating clinically acceptable performance,improving the generalizability of AI models across heterogeneous populations,and improving the trust of end-users.
