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Artificial intelligence-quantified schisis volume as a structural endpoint for gene therapy clinical trials in X-linked retinoschisis
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AI
DOI：10.1111/aos.17485.png)
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Abstract
En 中文
Purpose To use artificial intelligence (AI) for quantifying schisis volume (ASV) in X-linked retinoschisis (XLRS) for use as a structural endpoint in gene therapy clinical trials. Methods We used data from Singapore, the United Kingdom, the Netherlands, and the United States. The AI model was developed on 250 optical coherence tomography (OCT) slices, with human annotation of schisis cavities (Dataset 1). ASV was quantified on Dataset 2 - 16 OCT scans from 8 eyes with XLRS at two time points, and Dataset 4 - 62 OCT scans from 31 eyes at two time points before and after carbonic anhydrase inhibitor (CAI) treatment. A clinical trial was simulated comparing CAI treatment against control. Changes in ASV, central subfield thickness (CST) and central foveal thickness (CFT) were compared. Effect size (Cohen's d) of the three structural endpoints was determined and used in sample size calculations for a future XLRS gene therapy clinical trial, at a 0.05 significance level and 80% power. Results In the simulated clinical trial, all structural metrics showed greater reductions with intervention than with control, but only change in ASV reached statistical significance (p = 0.004). Cohen's d for ASV, CST and CFT were 0.972, 0.685 and 0.521, respectively. For the future gene therapy clinical trial, sample sizes required in each arm for ASV, CST and CFT were 18, 35 and 59 participants, respectively. Conclusions ASV measurements can track changes in schisis volume in response to treatment. As an endpoint, ASV has a greater statistical effect size than CST/CFT, which reduces sample size requirements for future XLRS gene therapy clinical trials.
Keywords:
AI
clinical trial endpoints
gene therapy
inherited retinal disease
RS1
XLRS
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2.8
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