The primary interest of Yali Jia, PhD, is the development and evaluation of optical imaging techniques for ophthalmic care. Specifically, Dr. Jia developed the innovative "split-spectrum amplitude-decorrelation angiography" algorithm that improves the signal-to-noise ratio and flow detection efficiency in optical coherence tomographic angiography (OCTA), which has been licensed and commercialized. Later, as the principal investigator of several project funded by the National Institutes of Health, she developed quantitative OCTA for detecting neovascularization and capillary dropout in retinal vascular diseases. This work ignited a rapidly growing field of study.
Her group has continued to improve OCTA by compensating for signal strength variation and removing both motion and projection artifacts. By leveraging advances in deep learning, her team pioneered the development of deep-learning-based algorithms to automate the detection and quantification of retinal pathologies using optical coherence tomography (OCT). In combining these advances, their OCTA reading software (COOL-ART) has been used by several large clinical studies and many international collaborators. Dr. Jia's other contributions to the OCT community include ultra-high-speed real-time OCTA, high-dynamic-range OCTA, artifact-resolved OCTA, nanoparticle-enhanced spectroscopic OCT, visible-light OCT, and OCT oximetry.