Exploiting the Generative Adversarial Network Approach to Create a Synthetic Topography Corneal Image | JoVE Visualize

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Corneal diseases are prevalent eye disorders often diagnosed using automated methods.
Deep learning (DL) models for medical image analysis require extensive annotated datasets, a significant limitation.
Existing datasets for corneal disease diagnosis are often imbalanced, affecting classifier performance.

Purpose of the Study:

To present a method for synthesizing medical images of corneal diseases using conditional generative adversarial networks (CGANs).
To demonstrate the utility of synthesized images in augmenting medical data for improved clinical decisions.
To evaluate the impact of data augmentation and balancing on the performance of conventional neural networks (CNNs) for corneal image diagnosis.

Main Methods:

Corneal topography images from 3448 patients with corneal diseases were collected using a Pentacam device.
Conditional generative adversarial networks (CGANs) were employed to synthesize new corneal images.
A resampling approach was used to balance the dataset, and CNNs were trained on both balanced and imbalanced datasets.
Performance was evaluated using accuracy, precision, and F1-score metrics, with expert evaluation of synthesized images.

Main Results:

Synthesized medical images were found to be useful for medical diagnosis and severity classification.
CNNs trained on the balanced dataset, augmented with synthesized images, showed improved performance compared to those trained on the imbalanced dataset.
Expert evaluation confirmed the clinical utility of generated images for identifying disease stages.

Conclusions:

Conditional generative adversarial networks (CGANs) offer a viable solution for generating synthetic medical images to address data scarcity in corneal disease diagnosis.
Data augmentation and balancing techniques significantly improve the performance of deep learning models in diagnosing corneal conditions.
The synthesized images hold potential for enhancing medical education, clinical decision support, and the development of more robust diagnostic tools.