Suria S. Mannil

A 3D Deep Learning System for Detecting Referable Glaucoma Using Full OCT Macular Cube Scans.

A 3D deep learning algorithm trained on macular OCT volumes without retinal disease to detect referable glaucoma performs better with retinal segmentation preprocessing and performs reasonably well across all levels of myopia.

Deep Learning for Glaucoma Detection and Identification of Novel Diagnostic Areas in Diverse Real-World Datasets.

A 3D convolutional neural network (CNN) trained on SD-OCT ONH cubes can distinguish glaucoma from normal cases in diverse datasets obtained from four different countries.

Comparison of Structural, Functional, Tonometric, and Visual Acuity Testing for Glaucoma: A Prospective Diagnostic Accuracy Study.

OCT had moderate sensitivity and fair specificity for diagnosing moderate to advanced glaucoma and should be prioritized during an initial assessment for glaucoma.

Diagnostic Accuracy of Frequency-Doubling Technology and the Moorfields Motion Displacement Test for Glaucoma.

Frequency-doubling technology and MDT perimetry had fair diagnostic accuracy for glaucoma detection when administered to naïve test takers in this South Indian population.

Developing a privacy-preserving deep learning model for glaucoma detection: a multicentre study with federated learning.

The 3D FL model could leverage all the datasets and achieve generalisable performance, without data exchange across centres.