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Updated: Jul 14, 2026

In Vivo Multimodal Imaging and Analysis of Mouse Laser-Induced Choroidal Neovascularization Model
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Multicenter OCT-Based Visual Field Representations via Segmentation-Free 3D CNNs: Forecasting, Longitudinal

Makoto Koyama1, Hidenori Takahashi2,3, Satoru Inoda3

  • 1Minamikoyasu Eye Clinic, Kimitsu, Japan.

Translational Vision Science & Technology
|July 13, 2026
PubMed
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Three-dimensional convolutional neural networks (3DCNNs) can create visual field (VF)-shaped representations from optical coherence tomography (OCT) scans. These OCT-based estimated VFs (OCT-VF) improve future visual field predictions and disease monitoring.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Automated perimetry, such as Humphrey Field Analyzer (HFA), is standard for monitoring glaucoma and other visual field defects.
  • However, HFA measurements can be affected by noise and variability, potentially obscuring early signs of progression.
  • Novel methods are needed to enhance the accuracy and sensitivity of visual field monitoring.

Purpose of the Study:

  • To evaluate segmentation-free 3DCNNs for generating OCT-based estimated visual fields (OCT-VF) from macular OCT volumes.
  • To assess the performance of these OCT-VF models in forecasting future HFA measurements.
  • To characterize the longitudinal variability of OCT-VF compared to HFA data.

Main Methods:

  • Trained 3DCNN models on a large dataset of 129,007 paired OCT-HFA scans from 13,366 patients.

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  • Evaluated forecasting accuracy using HFA-only models and hybrid models (AOS-AVG) incorporating OCT-VF.
  • Assessed longitudinal variability by analyzing residual deviations from linear trends.
  • Main Results:

    • Hybrid forecasting models (AOS-AVG) significantly reduced mean absolute error compared to HFA-only forecasts.
    • OCT-VF demonstrated significantly lower residual variability than HFA measurements.
    • Eyes identified as progressing by OCT-VF but not HFA showed greater variability and faster HFA MD decline.

    Conclusions:

    • Hybrid forecasting incorporating OCT-VF-derived slopes enhances prediction of future visual field outcomes.
    • The reduced variability of OCT-VF may help detect progression signals masked by perimetric noise.
    • OCT-VF shows potential as a complementary tool for precision disease monitoring in ophthalmology.