Circular functional analysis of OCT data for precise identification of structural phenotypes in the eye

Area of Science:

• Ophthalmology
• Medical Imaging
• Computational Biology

Background:

• Glaucoma and other optic neuropathies are leading causes of global blindness.
• Early diagnosis and management are challenged by subjectivity and analytical complexities.
• Neuroretinal rim (NRR) thinning is a key indicator of glaucomatous damage.

Purpose of the Study:

• To introduce CIFU, a novel computational pipeline for circular functional data analysis.
• To analyze Optical Coherence Tomography (OCT) derived NRR phenotypes in a large cohort.
• To identify potential early indicators of glaucoma risk using structural data.

Main Methods:

• Generation of a dataset of OCT NRR measurements from 3973 healthy Asian Indian eyes.
• Application of CIFU for unsupervised circular functional clustering of NRR data.
• Meta-clustering to characterize identified clusters using clinical covariates and circular visualization.

Main Results:

• CIFU successfully clustered NRR phenotypes.
• A specific healthy NRR phenotype cluster was identified in the 40-49 year age group.
• This phenotype showed predictive potential for glaucoma and addresses population representation in OCT databases.

Conclusions:

• The CIFU pipeline offers a robust method for analyzing circular functional data in ophthalmology.
• Early identification of glaucoma risk may be possible through analysis of specific NRR phenotypes in middle-aged individuals.
• The study contributes a valuable dataset and analytical approach, enhancing representation for Asian Indian participants in normative OCT databases.