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

Acta Ophthalmologica, vol. 103, pp. 707–714

Abstract

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.

Authors 6

1. Jonathan Hensman

   Amsterdam University Medical Centers

   Affiliation as printed

   Department of Ophthalmology Amsterdam University Medical Centre Amsterdam the Netherlands

2. Yasmine El Allali

   Amsterdam University Medical Centers

   Affiliation as printed

   Department of Ophthalmology Amsterdam University Medical Centre Amsterdam the Netherlands

3. Hind Almushattat

   Amsterdam University Medical Centers

   Affiliation as printed

   Department of Ophthalmology Amsterdam University Medical Centre Amsterdam the Netherlands

4. Coen de Vente

   Amsterdam UMC Location University of Amsterdam · University of Amsterdam

   Affiliation as printed

   Biomedical Engineering and Physics Amsterdam UMC Location University of Amsterdam Amsterdam the Netherlands

   Quantitative Healthcare Analysis (qurAI) Group, Informatics Institute University of Amsterdam Amsterdam the Netherlands

5. Clara I. Sá‎nchez

   Amsterdam UMC Location University of Amsterdam · University of Amsterdam

   Affiliation as printed

   Biomedical Engineering and Physics Amsterdam UMC Location University of Amsterdam Amsterdam the Netherlands

   Quantitative Healthcare Analysis (qurAI) Group, Informatics Institute University of Amsterdam Amsterdam the Netherlands

6. Camiel J. F. Boon corresponding Aachen Department of Ophthalmology Leiden University Medical Centre Leiden The Netherlands

   Leiden University · Leiden University Medical Center · Amsterdam University Medical Centers

   Affiliation as printed

   Department of Ophthalmology Amsterdam University Medical Centre Amsterdam the Netherlands

   Department of Ophthalmology Leiden University Medical Centre Leiden the Netherlands