Ongoing Projects

We are developing an AI model to detect thyroid eye disease (TED) from facial images using federated learning, a cutting edge approach that addresses key challenges in data privacy when working with sensitive patient data. 

In collaboration with Vanderbilt and Stanford, we are implementing federated learning to train our model across multiple institutions without sharing patient data. This method enhances model robustness and generalizability by incorporating diverse, real-world data while preserving patient privacy.

By enabling earlier and more accurate TED detection, our work aims to facilitate timely referrals to oculoplastics, preventing complications and irreversible ocular damage. 

Portable Optical Coherence Tomography (pOCTs) significantly reduces the cost and increases accessibility compared to commercial OCT. Though pOCTs’ image resolution is sacrificed, we develop generative AI models to do super-resolution. Specifically, we used IMFusion software to register and denoise (preprocessing), and implemented OCTDiff, a bridged diffusion model to achieve image-to-image translation from low-res to high-res. 

The downstream disease classification results using OCTDiff-generated images further prove the clinical utility of our AI-powered tool for portable OCT's application. With the successful development of AI model, we will integrate this AI-package onto physical devices for improved healthcare in clinics. 

Recent findings support the ancient saying “the eyes are the window to the soul.” The eye is the only organ through which doctors can non-invasively probe blood flow through the body’s tiniest vessels; therefore, our eyes combined with the pattern recognition power of AI (oculomics) can provide access to the internal state of the brain, heart, kidneys, and potentially other organs. 

The goal of this project (which is funded by our Research to Prevent Blindness/Tom Wertheimer Career Development Award in AI/Data Science) is to decipher the connection between retinal microvasculature and systemic state of disease vs. health of the body using the power of AI and multimodal medical data, in order to expedite detection of systemic diseases (of the heart, brain, and/or endocrine system). 

By leveraging internal and external (publicly available) paired datasets of retinal imaging (fundus, OCT, OCT angiography) and cardiac imaging, neurologic imaging, and blood glucose/hemoglobin A1C measurements, this project will develop AI to predict systemic disease from the eye as well as forge novel biomarker discovery for these diseases accessible specifically in the eye.

When your eye is scanned at the ophthalmologist, optical coherence tomography (OCT) captures multiple layers of the retina and reconstructs them into a 3D OCT volume. This volume is then processed and formatted into a 2D OCT report, which ophthalmologists rely on for glaucoma diagnosis. The 3D OCT volume itself is typically not used, as it is time-consuming and challenging to interpret without comparisons to normal populations, which are available in the 2D report.

However, these volumes contain rich structural information that can be leveraged for glaucoma diagnosis. Deep learning offers a powerful approach to processing these volumes and extracting meaningful insights to assist ophthalmologists. We propose incorporating attention mechanisms to capture long-range dependencies within these volumes and embedding them within a 3D convolutional neural network (CNN). Additionally, we developed a 3D attention visualization mechanism to analyze what the model attends to in its attention layers when making a diagnosis, alongside Grad-CAM for the convolutional layers.