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Related Experiment Video

Updated: Oct 2, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Policy-Driven, Multimodal Deep Learning for Predicting Visual Fields from the Optic Disc and OCT Imaging.

Yuka Kihara¹, Giovanni Montesano², Andrew Chen¹

¹University of Washington, Department of Ophthalmology, Seattle, Washington.

|February 24, 2022

Summary

This summary is machine-generated.

A deep learning system accurately predicts visual fields from OCT and disc imaging, improving glaucoma diagnosis. This multimodal approach enhances structure-function mapping for better patient care.

Keywords:

Artificial intelligence Deep learning Glaucoma OCT Perimetry Structure–function Visual field

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Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

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Published on: August 4, 2018

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Glaucoma diagnosis relies on correlating visual field (VF) sensitivity with optic disc structure.
Current methods for structure-function mapping can be limited.
Deep learning (DL) offers potential for advanced image analysis and prediction.

Purpose of the Study:

To develop and validate a deep learning system for predicting visual field sensitivity at each point.
To derive a structure-function map using optical coherence tomography (OCT) and optic disc imaging.
To fuse predictions from single-modality DL models for improved accuracy.

Main Methods:

Retrospective study of 6437 patients with glaucoma.
Paired OCT and infrared reflectance (IR) optic disc images with VF data within 7 days.
Trained EfficientNet B2 DL models for single-modality prediction and a policy DL model for fusion.

Main Results:

A multimodal, policy DL model significantly improved prediction accuracy (PMAe) compared to single modalities.
The fusion model showed statistically significant improvements (P < 0.0001).
Occlusion masking confirmed data-driven, feature-agnostic learning of structure-function relationships.

Conclusions:

The multimodal policy DL model demonstrated superior performance in predicting visual fields.
The system provides explainable confidence maps for data fusion.
This approach offers a novel pathway for investigating the structure-function relationship in glaucoma.