A clinical perspective on the expanding role of artificial intelligence in age-related macular degeneration.
Himeesh KumarKai Lyn GohRobyn H GuymerZhichao WuPublished in: Clinical & experimental optometry (2022)
In recent years, there has been intense development of artificial intelligence (AI) techniques, which have the potential to improve the clinical management of age-related macular degeneration (AMD) and facilitate the prevention of irreversible vision loss from this condition. Such AI techniques could be used as clinical decision support tools to: (i) improve the detection of AMD by community eye health practitioners, (ii) enhance risk stratification to enable personalised monitoring strategies for those with the early stages of AMD, and (iii) enable early detection of signs indicative of possible choroidal neovascularisation allowing triaging of patients requiring urgent review. This review discusses the latest developments in AI techniques that show promise for these tasks, as well as how they may help in the management of patients being treated for choroidal neovascularisation and in accelerating the discovery of new treatments in AMD.
Keyphrases
- age related macular degeneration
- artificial intelligence
- big data
- machine learning
- deep learning
- clinical decision support
- end stage renal disease
- healthcare
- newly diagnosed
- mental health
- ejection fraction
- public health
- chronic kidney disease
- primary care
- small molecule
- prognostic factors
- electronic health record
- working memory
- peritoneal dialysis
- risk assessment
- human health
- high throughput
- label free
- loop mediated isothermal amplification
- optical coherence tomography