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In this study, we proposed a wavelet-based deep learning network to estimate retinal function from retinal structure in patients with Retinitis Pigmentosa. We used macular integrity assessment microperimetry to measure retinal sensitivities (functional information) and spectral domain optical coherence tomography to assess retinal layer thicknesses (structural information). Outer, inner, and total retinal thicknesses were extracted. We found a strong correlation between outer retinal thickness and retinal sensitivity. Leveraging this correlation, we employed machine learning models for functional estimation from retinal layer thicknesses and vice versa. For functional estimation, we incorporated discrete wavelet transform and max-pooling features in a ResNet18-based architecture, significantly improving the accuracy to an \(R^2\) score of 0.79. Our results demonstrate that machine learning models can effectively predict retinal function from retinal structure, and vice versa. Furthermore, the integration of discrete wavelet transform features in the convolutional neural network improved the performance of functional estimation from retinal structure.

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Wavelet Deep Learning Network for Objective Retinal Functional Estimation from Multimodal Retinal Imaging

  • 会议论文

  • 发表日期：

    2025年

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  • An D. Le,
  • Shaden H. Yassin,
  • William R. Freeman,
  • Anna Heinke,
  • Dirk-Uwe G. Bartsch,
  • Shyamanga Borooah,
  • Shiwei Jin,
  • Truong Nguyen,
  • Cheolhong An

摘要

In this study, we proposed a wavelet-based deep learning network to estimate retinal function from retinal structure in patients with Retinitis Pigmentosa. We used macular integrity assessment microperimetry to measure retinal sensitivities (functional information) and spectral domain optical coherence tomography to assess retinal layer thicknesses (structural information). Outer, inner, and total retinal thicknesses were extracted. We found a strong correlation between outer retinal thickness and retinal sensitivity. Leveraging this correlation, we employed machine learning models for functional estimation from retinal layer thicknesses and vice versa. For functional estimation, we incorporated discrete wavelet transform and max-pooling features in a ResNet18-based architecture, significantly improving the accuracy to an \(R^2\) score of 0.79. Our results demonstrate that machine learning models can effectively predict retinal function from retinal structure, and vice versa. Furthermore, the integration of discrete wavelet transform features in the convolutional neural network improved the performance of functional estimation from retinal structure.