Cataract
cataract
Vision Unveiled: Understanding Face Perception in Children Treated for Congenital Blindness
Despite her still poor visual acuity and minimal visual experience, a 2-3 month old baby will reliably respond to facial expressions, smiling back at her caretaker or older sibling. But what if that same baby had been deprived of her early visual experience? Will she be able to appropriately respond to seemingly mundane interactions, such as a peer’s facial expression, if she begins seeing at the age of 10? My work is part of Project Prakash, a dual humanitarian/scientific mission to identify and treat curably blind children in India and then study how their brain learns to make sense of the visual world when their visual journey begins late in life. In my talk, I will give a brief overview of Project Prakash, and present findings from one of my primary lines of research: plasticity of face perception with late sight onset. Specifically, I will discuss a mixed methods effort to probe and explain the differential windows of plasticity that we find across different aspects of distributed face recognition, from distinguishing a face from a nonface early in the developmental trajectory, to recognizing facial expressions, identifying individuals, and even identifying one’s own caretaker. I will draw connections between our empirical findings and our recent theoretical work hypothesizing that children with late sight onset may suffer persistent face identification difficulties because of the unusual acuity progression they experience relative to typically developing infants. Finally, time permitting, I will point to potential implications of our findings in supporting newly-sighted children as they transition back into society and school, given that their needs and possibilities significantly change upon the introduction of vision into their lives.
Diverse applications of artificial intelligence and mathematical approaches in ophthalmology
Ophthalmology is ideally placed to benefit from recent advances in artificial intelligence. It is a highly image-based specialty and provides unique access to the microvascular circulation and the central nervous system. This talk will demonstrate diverse applications of machine learning and deep learning techniques in ophthalmology, including in age-related macular degeneration (AMD), the leading cause of blindness in industrialized countries, and cataract, the leading cause of blindness worldwide. This will include deep learning approaches to automated diagnosis, quantitative severity classification, and prognostic prediction of disease progression, both from images alone and accompanied by demographic and genetic information. The approaches discussed will include deep feature extraction, label transfer, and multi-modal, multi-task training. Cluster analysis, an unsupervised machine learning approach to data classification, will be demonstrated by its application to geographic atrophy in AMD, including exploration of genotype-phenotype relationships. Finally, mediation analysis will be discussed, with the aim of dissecting complex relationships between AMD disease features, genotype, and progression.