Using artificial intelligence to assess macular edema treatments in retinitis pigmentosa.

Abstract

Purpose: This study validates a deep learning-based artificial intelligence (AI) tool for quantifying macular edema (ME) intraretinal fluid (IRF) volumes in retinitis pigmentosa, and through longitudinal analysis of IRF, provides new insight into treatment efficacy and disease natural history.

Methods: This retrospective, longitudinal study identified RP patients with ME. A commercially available retinal analysis tool quantified IRF, and was validated for segmentation of ME using spectral-domain optical coherence tomography volume scans. Baseline analysis of IRF versus traditional central subfield thickness (CST), and longitudinal analyses of IRF versus treatment and best-corrected visual acuity (BCVA) were performed.

Results: Forty-four patients were identified. For treatment studies, 52 eyes were in the treated group and 14 eyes in the untreated ME group. Mean follow-up was 5.3 exams (3.7, 6.9) over 2.3 years (1.7, 3.0). Software validation compared automated and manual IRF segmentation of 490 image pairs, finding a Dice coefficient of 0.928 (95% CI: 0.92, 0.99). Cohort mean IRF volume was 230.85 nL (57.42, 403.91) at baseline. IRF change in eyes treated with topical carbonic anhydrase inhibitors (CAIs) was -2.1 nL/year (P=0.81). Oral acetazolamide (AZM)-treated eyes had significant IRF reduction (-33.6 nL/year, P=0.009), and significant improvements in BCVA (logMAR/yr; ETDRS letters equivalent) (-0.041; +2 letters) (P=0.025).

Conclusion: A deep learning tool was able to rapidly and accurately quantify IRF in RP-associated ME. Using this analysis tool, we confirmed that treatment with AZM led to significant reduction in long-term IRF. Structural changes (IRF) only translated to significant functional improvements (BCVA) in eyes treated with AZM.