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New research · Ophthalmology
Ophthalmology science · 14h
AI / informaticsOphthalmology science · 2026

Machine Learning-Based Prediction of Long-Term Intraocular Pressure Fluctuations in Open-Angle Glaucoma.

Colya N Englisch, André M Trouvain, Philip Wakili … Peter Szurman
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OphthalmologyAI / informatics

Machine learning predicted long-term glaucoma eye pressure swings with AUROC up to 0.86

Machine Learning-Based Prediction of Long-Term Intraocular Pressure Fluctuations in Open-Angle Glaucoma.

Colya N Englisch … Peter Szurman
Ophthalmology science · 2026
Purpose

To investigate the predictability of long-term intraocular pressure (IOP) fluctuations in open-angle glaucoma eyes implanted with a telemetric IOP sensor.

Methods

A prospective, open-label, single-arm, multicenter study.

n = 8 patients
0.86
Results
0.86
AUROC of 1.0 is perfect; 0.86 indicates strong predictive accuracy for models
n = 8 patients
More results

Short-term fluctuations correlated only weakly with long-term variability (Pearson r ≤ 0.33) and explained at most 15.2% in regression analysis.

On average, 5563 ± 116 valid pairings from 9.2 ± 0.8 patients were used per configuration.

Five final configurations were selected for each threshold-horizon combination based on the highest F1 values.

More results

All models included 7, 14, and 28-day fluctuations, mean nyctohemeral IOP, ocular pulse amplitude, age, body mass index, and central corneal thickness as predictors, with mean nyctohemeral IOP contributing most (38%-55%).

“
Conclusion · 1 of 2

Long-term IOP fluctuations can be predicted from baseline clinical and demographic data combined with IOP-related features.

Conclusion · 2 of 2

Telemetric devices and remote IOP monitoring, combined with predictive modeling, could reduce the burden of time-intensive procedures and health care costs while supporting individualized care in the face of rising demand.

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