Thakoor Kaveri A

Detecting glaucoma with only OCT: Implications for the clinic, research, screening, and AI development.

In section 5, the model helps account for the success of an AI deep learning model, which in turn validates our focus on the RNFL p-map.

An Automated Method for Assessing Topographical Structure-Function Agreement in Abnormal Glaucomatous Regions.

The method described here can automatically and objectively evaluate aS-aF agreement with a direct comparison of abnormal regions of function and structure.

Strategies to Improve Convolutional Neural Network Generalizability and Reference Standards for Glaucoma Detection From OCT Scans.

CNN generalizability can be improved with data augmentation, multiple input image modalities, and training on images with confident ratings.

Automated Identification of Clinically Relevant Regions in Glaucoma OCT Reports Using Expert Eye Tracking Data and Deep Learning.

Our approach has the potential to assist ophthalmologists in diagnosing glaucoma by predicting the most medically salient regions on OCT reports.

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.