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Computer-Assisted Detection of Retinal Injury Following Ocular Trauma Using Machine Learning Algorithms.

Patrick Y Hsun1, Christina L Rettinger1, Heuy-Ching Wang2

  • 1Metis Foundation, San Antonio, TX 78216, United States.

Military Medicine
|September 23, 2025
PubMed
Summary
This summary is machine-generated.

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Machine learning algorithms accurately detect retinal tears after penetrating eye injuries, outperforming traditional methods. This advancement paves the way for AI-powered diagnostic tools to aid in identifying eye trauma and disease progression.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Posterior penetrating eye injury poses risks of retinal tearing, detachment, and fibrosis, necessitating prompt visual assessment.
  • Ophthalmoscopy, the current standard, has limitations in consistently detecting subtle retinal abnormalities post-injury.
  • Automated image processing using machine learning (ML) offers potential for enhanced detection of retinal changes.

Purpose of the Study:

  • To evaluate the efficacy of machine learning (ML) models in identifying subtle retinal abnormalities following posterior penetrating eye injury.
  • To compare the performance of different ML algorithms, including Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), in detecting retinal injuries.

Main Methods:

  • Ten rabbits underwent posterior penetrating eye injury and fundus photography.

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  • A dataset of 743 fundus images was randomly split into training (70%) and testing (30%) sets.
  • Two ML model types (CNN and four SVM varieties) were trained and validated, with performance assessed using accuracy and ROC AUC.

    • Main Results:

    • The Support Vector Machine (SVM) with a polynomial kernel achieved the highest accuracy (92.37%) and ROC AUC (0.96).
    • The Convolutional Neural Network (CNN) model demonstrated strong performance with 91.03% accuracy and 0.91 ROC AUC.
    • Other SVM models showed varied results, with linear kernel performing comparably and sigmoid/radial basis function kernels showing lower efficacy.

    Conclusions:

    • Trained ML algorithms can accurately identify retinal tears resulting from posterior eye trauma.
    • This research represents a significant step towards developing computer-aided diagnostic systems for retinal injury detection.
    • ML-based tools could improve the monitoring of eye injury and disease progression after penetrating trauma.