USING ARTIFICIAL INTELLIGENCE TO ASSESS MACULAR EDEMA TREATMENTS IN RETINITIS PIGMENTOSA.

PURPOSE: This study validates a deep learning-based artificial intelligence 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 patients with retinitis pigmentosa 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 and longitudinal analyses of IRF versus treatment and best-corrected visual acuity 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% confidence interval: 0.92‒0.99). Cohort mean IRF volume was 230.85 nL (57.42, 403.91) at baseline. Intraretinal fluid change in eyes treated with topical carbonic anhydrase inhibitors was -2.1 nL/year ( P = 0.81). Oral acetazolamide-treated eyes had significant IRF reduction (-33.6 nL/year, P = 0.009) and significant improvements in best-corrected visual acuity (logMAR/year; Early Treatment Diabetic Retinopathy Study letters equivalent) (-0.041; +2 letters) ( P = 0.025).

CONCLUSION

A deep learning tool was able to rapidly and accurately quantify IRF in retinitis pigmentosa-associated ME. Using this analysis tool, we confirmed that treatment with acetazolamide led to significant reduction in long-term IRF. Structural changes (IRF) only translated to significant functional improvements (best-corrected visual acuity) in eyes treated with acetazolamide.