Deep learning model for detecting cystoid fluid collections on optical coherence tomography in X-linked retinoschisis patients.

Purpose: To validate a deep learning (DL) framework for detecting and quantifying cystoid fluid collections (CFC) on spectral-domain optical coherence tomography (SD-OCT) in X-linked retinoschisis (XLRS) patients.

Methods: A no-new-U-Net model was trained using 112 OCT volumes from the RETOUCH challenge (70 for training and 42 for internal testing). External validation involved 37 SD-OCT scans from 20 XLRS patients, including 20 randomly sampled B-scans and 17 manually selected central B-scans. Three graders manually delineated the CFC on these B-scans in this external test set. The model's efficacy was evaluated using Dice and intraclass correlation coefficient (ICC) scores, assessed exclusively on the test set comprising B-scans from XLRS patients.

Results: For the randomly sampled B-scans, the model achieved a mean Dice score of 0.886 (±0.010), compared to 0.912 (±0.014) for the observers. For the manually selected central B-scans, the Dice scores were 0.936 (±0.012) for the model and 0.946 (±0.012) for the graders. ICC scores between the model and reference were 0.945 (±0.014) for the randomly selected and 0.964 (±0.011) for the manually selected B-scans. Among the graders, ICC scores were 0.979 (±0.008) and 0.981 (±0.011), respectively.

Conclusions: Our validated DL model accurately segments and quantifies CFC on SD-OCT in XLRS, paving the way for reliable monitoring of structural changes. However, systematic overestimation by the DL model was observed, highlighting a key limitation for future refinement.