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Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

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Detecting Glaucoma Worsening Using Optical Coherence Tomography Derived Visual Field Estimates.

Alex T Pham, Chris Bradley, Kaihua Hou

Medrxiv : the Preprint Server for Health Sciences

|November 1, 2024

Summary

This summary is machine-generated.

Machine learning models converting optical coherence tomography (OCT) data to visual field (VF) mean deviation (MD) estimates are not yet accurate enough to detect longitudinal VF progression. Current models require a mean absolute error (MAE) of 1 dB or less to be clinically valuable.

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Area of Science:

Ophthalmology
Medical Imaging
Machine Learning

Background:

Cross-sectional optical coherence tomography (OCT) data has been used to estimate visual field (VF) mean deviation (MD).
The utility of these models in detecting longitudinal VF progression remains uncertain.

Purpose of the Study:

To develop and assess a machine learning (ML) model for converting OCT data to MD estimates.
To evaluate the model's capability in detecting longitudinal VF worsening.

Main Methods:

Trained an ML model using 70,575 paired OCT/VF datasets to convert OCT to VF-MD.
Assessed the model's performance on a progression dataset of 4,044 eyes with at least 5 paired OCT/VFs.
Calculated MD slopes by substituting/supplementing VF-MD with OCT-derived MD (OCT-MD) and compared the area under the curve (AUC) to ground truth.

Main Results:

OCT-MD estimates achieved a mean absolute error (MAE) of 1.62 dB.
AUC of MD slopes with partial OCT-MD substitution was significantly worse than VF-MD slopes.
Supplementing VF-MD with OCT-MD did not improve AUC; an MAE of ≤ 1.00 dB was needed for statistical similarity.

Conclusions:

Current ML models converting OCT to VF-MD, even with improved accuracy (MAE: 1.62 dB), are inferior to VF-MD for detecting trend-based progression.
For clinical utility in detecting VF worsening, OCT-to-VF-MD models require prediction errors of MAE ≤ 1 dB.