Lee Aaron Y

Automated Detection of Glaucoma With Interpretable Machine Learning Using Clinical Data and Multimodal Retinal Images.

The accuracy of our model suggests distinct sources of information in each imaging modality and in the different clinical and demographic variables.

Smoking Is Associated with Higher Intraocular Pressure Regardless of Glaucoma: A Retrospective Study of 12.5 Million Patients Using the Intelligent Research in Sight (IRIS®) Registry.

Current smokers and past smokers have higher IOP than patients who never smoked. This difference is higher in patients with an underlying glaucoma diagnosis.

Policy-Driven, Multimodal Deep Learning for Predicting Visual Fields from the Optic Disc and OCT Imaging.

The multimodal, policy DL model performed the best; it provided explainable maps of its confidence in fusing data from single modalities and provides a pathway for probing the structure-function relationship in glaucoma.

Refractive Outcomes After Immediate Sequential vs Delayed Sequential Bilateral Cataract Surgery.

The results of this cohort study of patients in the IRIS Registry suggest that compared with DSBCS-14 or DSBCS-90, ISBCS is associated with worse visual outcomes, which may or may not be clinically relevant, depending on patients' additional risk factors.

Changes in Performance of Glaucoma Surgeries 1994 through 2017 Based on Claims and Payment Data for United States Medicare Beneficiaries.

Glaucoma practice patterns change each time a new device or procedure is introduced.

Visual Field Endpoints for Neuroprotective Trials: A Case for AI-Driven Patient Enrichment.

An AI model can identify high-risk patients to substantially reduce the number of patients needed or study duration required to meet clinical trial endpoints.

Finding Glaucoma in Color Fundus Photographs Using Deep Learning.

Assessing the Clinical Utility of Expanded Macular OCTs Using Machine Learning.

Systematically expanded macular coverage models demonstrated significant differences in total macular coverage required for improved diagnostic accuracy, with the largest macular area being relevant in POAG followed by DME and then AMD.

Using Deep Learning to Automate Goldmann Applanation Tonometry Readings.

Preliminary measurements using deep learning to automate GAT demonstrate results comparable with those of standard GAT.

Validity of Administrative Claims and Electronic Health Registry Data From a Single Practice for Eye Health Surveillance.

In this cross-sectional study of current and recent ophthalmology patients with high rates of eye disorders and vision loss, identification of major vision-threatening eye disorders based on diagnosis codes in claims and EHR records was accurate.

Differences in Tertiary Glaucoma Care in the Veterans Affairs Health Care System.

Disparities exist in the use of tertiary glaucoma services within the VA, and different care delivery models may play a role.

Data-Driven, Feature-Agnostic Deep Learning vs Retinal Nerve Fiber Layer Thickness for the Diagnosis of Glaucoma.

Gap Analysis of Standard Automated Perimetry Concept Representation in Medical Terminologies.

There is a lack of representation of some SAP data elements in standardized medical terminologies, hampering interoperability and data sharing.