Ophthalmic imaging and artificial intelligence
Located in the Department of Ophthalmology, our lab focuses on advancing the intersection of ophthalmology, image processing, and artificial intelligence. We do this by leveraging pioneering imaging technologies and deep learning methodologies to improve the diagnosis, monitoring, and understanding of complex neuro-ophthalmic and retinal diseases.
Meet the Principal Investigator
Jui-Kai (Ray) Wang, Ph.D.
Assistant Professor of Ophthalmology
Jui-Kai (Ray) Wang, Ph.D., is a computer-engineer scientist, specializing in ophthalmic image analysis. The Wang (Jui-Kai) Lab works with multiple ophthalmic image modalities, such as color fundus photographs, optical coherence tomography (OCT), OCT angiography (OCTA), and laser speckle flowgraphy (LSFG). Dr. Wang uses both traditional machine learning and advanced deep learning neural networks to advance the understanding and diagnosis of various ocular diseases.
Meet the Team (Engineering)
Noriyoshi Takahashi
Noriyoshi Takahashi is a Research Assistant in Dr. Ray Wang's lab and a Ph.D. student in Electrical and Computer Engineering at UT Dallas.
His research focuses on applying deep learning to multimodal ophthalmic imaging, including ocular blood-flow imaging, optical coherence tomography (OCT), OCT angiography, and color fundus photography. Before beginning his doctoral studies, he spent more than 15 years developing laser speckle flowgraphy systems to measure blood flow in living tissues, with a particular focus on the eye.
Meet the Team (Medical Students)
Nikhil Gadiraju (Class of 2028)
Nikhil Gadiraju is a third-year medical student at UT Southwestern with a background in biomedical engineering from Duke University and a strong interest in ophthalmology. His research focuses on applying artificial intelligence and computational methods to ophthalmic imaging, particularly in retinal disease and multimodal imaging such as optical coherence tomography and color fundus photography.
His work explores applications of AI to retinal imaging, including disease characterization, imaging device variability, and clinically relevant questions in vitreoretinal disease. He is particularly interested in integrating computational approaches with clinical ophthalmology to better understand ophthalmic disease and develop technologies that can improve diagnosis and patient care.