Séminaire de Sajib SAHA

AI methodologies to support automated analysis of sight threatening eye diseases

Sight threatening diseases, such as diabetic retinopathy and age related maculardegeneration have contributed to the 40% increase in outpatient attendances inthe last decade but are amenable to early detection and monitoring.Age-related macular degeneration (AMD) is a degenerative retinal disease thatcan cause irreversible vision loss. AMD affect 1 in 7 over the age of 50. It is theleading cause of blindness in Europe and North America and accounts for overhalf of partially sighted or legally blind certifications in the UK. Diabeticretinopathy (DR) is another leading cause of blindness in working agepopulations in the developed world. It is estimated that up to 50% of peoplewith proliferative DR who do not receive timely treatment will become legallyblind within 5 years. Up to 98% of severe visual loss due to DR can beprevented with early detection and treatment. Two imaging modalities, namely,color fundus photograph and optical coherence tomography are typically usedto diagnose these disease and to monitor their progression. Manual assessmentof these images is time consuming and cost intensive, which is also subjectiveand infeasible in many circumstances. Artificial intelligent methods areeffective alternative.He will discuss about several machine learning and/or deep learning approachesdeveloped by this team to facilitate automated analysis of AMD and DR