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Predicting Visual Outcomes in Congenital Posterior Lens Opacities: A Deep Learning Model Using Ultra-Widefield Fundus

Chaokun Luo1, Qingruo Zhang1, Yuhui Pang2

  • 1State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.

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Summary
This summary is machine-generated.

A deep learning model accurately segments lens opacity projected shadow (LOPS) in children with congenital posterior lens opacities (CPLOs). A lower shadow-to-optic disc ratio (SODR) predicts better visual outcomes, aiding in surgical planning.

Keywords:
Congenital posterior lens opacitiesDeep learningShadow-to-optic disc ratioUltra-widefield fundus imaging systemVisual outcome

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Congenital posterior lens opacities (CPLOs) can impact visual development in children.
  • Accurate assessment of lens opacity is crucial for surgical planning and predicting visual outcomes.
  • Ultra-widefield (UWF) fundus imaging offers a comprehensive view of the posterior segment.

Purpose of the Study:

  • To develop and validate a deep learning (DL) model for automatic segmentation of lens opacity projected shadow (LOPS) on UWF fundus images.
  • To predict visual outcomes in children with CPLOs using DL-derived metrics.
  • To establish the correlation between LOPS segmentation and visual acuity post-surgery.

Main Methods:

  • A DL model based on U-Net architecture was implemented for segmenting LOPS and optic discs on UWF fundus images.
  • The shadow-to-optic disc ratio (SODR) was calculated using segmented areas.
  • Model performance was evaluated using confusion matrix and Dice coefficients; visual outcome prediction involved regression analyses and ROC curves.

Main Results:

  • The DL model demonstrated high accuracy in segmenting optic discs (Dice=0.90) and LOPS (Dice=0.83 for total, 0.84 for dense).
  • 58.3% of children achieved optimal visual outcomes (BCVA > 0.30 logMAR).
  • A reduced dense-SODR (≤14.0) was significantly correlated with optimal visual outcomes (OR=0.951, P=0.039).

Conclusions:

  • Deep learning provides accurate segmentation of LOPS on UWF fundus images.
  • The dense-SODR is a novel and valuable predictor of visual outcomes in children with CPLOs.
  • This approach can aid in personalized surgical strategies and prognosis for CPLO patients.