Artificial intelligence and glaucoma progression

Detection of progression in glaucoma is crucial to avoid visual impairment and blindness. Throughout the clinical course disease, patients can present very different trajectories, as some may remain stable using single eye drops whereas other require surgical procedures control disease. Thus, decision intensifying a treatment by adding new or performing surgery need rely on precise data true ofthe In addition, assessing velocity help identify rapid progressors that are more prone develop functional impairment. practice, we use both structural (retinography optical coherence tomography) (visual field) measurements, along with clinic-demographical evaluate if patient progressing. However, correlation between exams makes detection challenge. Currently facing growing artificial intelligence medicine application complex algorithms such deep learning models. this review, summarize findings from recent studies investigated detecting progression.