Emmanuel Eric Pazo

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Automated deep learning-based retinoschisis and detachment volume measurement in pathological myopia with posterior scleral contraction

Mengdi Chai, Hua Rong, Danyang Yu, Xiu Wang, Di Wu, Emmanuel Eric Pazo, Jiachen Zhang, Ying Liu, Yifan Zhou, Y Y Chen, Anquan Xue, Ruihua Wei

British Journal of Ophthalmology, 2026 · article · doi:10.1136/bjo-2025-328592

Retina & vascular imagingMyopia & refractive errorAI & deep learning

Abstract

Purpose This study developed an automated deep learning-based system to quantify retinoschisis and detachment volume (RDV) in pathological myopia (PM) patients undergoing posterior scleral contraction (PSC). Methods A prospective study included 51 PM patients (36 PSC-treated and 15 controls). Based on the myopic traction maculopathy staging system classification, PSC-treated patients were divided into retinoschisis (RS, Stage 2, n=21) and retinal detachment (RD, Stages 3–4, n=15), while controls were classified as Stage 1 (n=15). Swept-source optical coherence tomography (OCT) and angiography imaging was used to acquire retinal data, and a Mask R-CNN model was trained to segment and calculate RDV. The primary outcome measures were regional macular recovery rate, defined as the percentage reduction in RDV at 12 months postoperatively. Clinical parameters such as axial length (AL) and best-corrected visual acuity (BCVA) were analysed. Result The model achieved an Intersection over Union of 80.9%. RDV increased with more advanced stages of myopic traction maculopathy (MTM). As MTM stage advanced, RDV distribution shifted from predominantly parafoveal/perifoveal involvement to foveal involvement. Macular RDV increased progressively with MTM stage (control: 0.94 mm 3 , RS: 5.24 mm 3 , RD: 9.50 mm 3 ; p<0.001). Postoperative AL decreased significantly and BCVA improved significantly (all p<0.001). Postoperative RDV decreased significantly, achieving macular and foveal recovery rates as 80.90% and 99.50%, respectively. AL reduction demonstrated the strongest correlation with foveal RDV changes (r=0.479, p=0.003). Conclusion The Mask R-CNN-based RDV measurement provides a sensitive 3D biomarker for assessing PSC efficacy and monitoring MTM progression. The system offers objective, high-precision quantification of retinal structural changes, addressing limitations of manual OCT analysis. Trial registration number ChiCTR2200065561.

Keywords: retinoschisis; pathologic myopia; myopic traction maculopathy; Mask R-CNN; retinal segmentation; automated volume measurement; axial length; foveal involvement

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How to cite

Chai, M., Rong, H., Yu, D., Wang, X., Wu, D., Pazo, E. E., Zhang, J., Liu, Y., Zhou, Y., Chen, Y. Y., Xue, A., Wei, R. (2026). Automated deep learning-based retinoschisis and detachment volume measurement in pathological myopia with posterior scleral contraction. British Journal of Ophthalmology. https://doi.org/10.1136/bjo-2025-328592

@article{chai2026automated,
  title = {{Automated deep learning-based retinoschisis and detachment volume measurement in pathological myopia with posterior scleral contraction}},
  author = {Chai, Mengdi and Rong, Hua and Yu, Danyang and Wang, Xiu and Wu, Di and Pazo, Emmanuel Eric and Zhang, Jiachen and Liu, Ying and Zhou, Yifan and Chen, Y Y and Xue, Anquan and Wei, Ruihua},
  journal = {British Journal of Ophthalmology},
  year = {2026},
  doi = {10.1136/bjo-2025-328592},
  url = {https://doi.org/10.1136/bjo-2025-328592},
}

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