Yan Li
Oregon Health & Science University, OR·Biomedical Engineering
Different eye diseases can look nearly identical in the clinic while demanding opposite treatments, and clinicians lack objective numbers for grading them or tracking change. Both projects here address that gap with optical coherence tomography (OCT), a light-based scanner that builds three-dimensional pictures of eye tissue at micrometer resolution, combined with artificial intelligence that converts those pictures into diagnostic measurements.
- Fields
Biomedical Engineering
Medicine & Clinical Research
Computer Science
- Topics
optical coherence tomography
anterior segment imaging
uveitis
- Grants
- 2 known grants, through Feb 2030
What they work on
- Develop ultrahigh-resolution optical coherence tomography capable of imaging the aqueous humor at the front of the eye and the vitreous gel behind it.
- Quantify inflammatory cell density and estimate protein concentration from background light scattering in uveitis patients, using AI to classify suspended cell types for differential diagnosis.
- Establish OCT-based biomarkers that grade uveitis severity and track individual response to treatment with trial-grade precision.
- Train an OCT-based AI system to distinguish corneal irregularities such as keratoconus, endothelial swelling, epithelial warpage and stromal scars from shape, thickness and reflectance data.
- Define OCT metrics that stage progression and gauge treatment efficacy in keratoconus and Fuchs' endothelial corneal dystrophy.
- Build an algorithm predicting corneal power change after endothelial keratoplasty to guide intraocular lens selection in combined cataract surgery.
More about the research
One effort targets inflammation inside the eye; the other targets distorted corneas.
Why it matters
Earlier and more accurate sorting among uveitis causes and among corneal diseases would let patients start the right treatment sooner and avoid unnecessary procedures. Objective OCT measures of intraocular inflammation could serve as precise endpoints in clinical trials, potentially shortening drug development and cutting cost, while better power prediction could improve lens selection for older patients undergoing combined corneal transplant and cataract surgery.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Artificial Intelligence Assisted Optical Coherence Tomography for Differential Diagnosis and Management of Irregular Corneas
NIH · May 2018 – Feb 2030
Quantification and Classification of Aqueous and Vitreous Inflammation in Uveitis Using Deep Learning Analysis of Ultrahigh-Resolution OCT
NIH · Sep 2024 – Aug 2026
Closest labs
Edmund Tsui
University of California-Los Angeles, CA·Medicine & Clinical Research
Identify which optical coherence tomography features, such as ellipsoid zone integrity, intraretinal hyperreflective foci and disorganization of retinal inner layers, predict response to therapy for uveitic macular edema across four completed randomized trials.
Shuliang Jiao
Florida International University, FL·Biomedical Engineering
Build a VIS-OCT platform combining 480 nm visible-light OCT, 840 nm near-infrared OCT and an autofluorescence detector. Use an A2E-stained reference target to obtain absolute fundus autofluorescence values.
Osamah Saeedi
University of Maryland, Baltimore, MD·Medicine & Clinical Research
Confirm with transgenic mouse models that adaptive optics optical coherence tomography (AO-OCT) signals specifically report retinal ganglion cell bodies and vascular pericytes, validating the imaging against gold-standard histology.
The school
Oregon Health & Science University: 24 labs on PI Pages, $545M a year on research, 54 research doctorates a year.
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