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Related Experiment Video

Updated: Jun 9, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

Automated Triage for New Keratoconus Referrals Using Multimodal Deep Learning.

Shafi Balal1,2, Lynn Kandakji2, Marcello Leucci1

  • 1Moorfields Eye Hospital NHS Trust, London, UK.

Ophthalmology Science
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

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Deep learning models predict keratoconus progression using multimodal imaging and clinical data. AI enables risk-stratified monitoring, reducing unnecessary follow-ups for low-risk patients and optimizing care for high-risk individuals.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Keratoconus is a progressive corneal ectasia requiring careful monitoring.
  • Accurate prediction of keratoconus progression is crucial for timely intervention.
  • Current monitoring methods may lead to overtreatment or delayed care.

Purpose of the Study:

  • To develop and validate deep learning models for predicting keratoconus progression risk.
  • To utilize multimodal imaging and clinical data for enhanced predictive accuracy.
  • To enable risk-stratified patient monitoring pathways.

Main Methods:

  • Retrospective cohort study with internal and external validation datasets.
  • Analysis of anterior-segment OCT (AS-OCT) and Placido topography data.
Keywords:
Artificial intelligenceAutomationKeratoconusTriage

Related Experiment Videos

Last Updated: Jun 9, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

  • Comparison of machine learning, unimodal, and multimodal deep learning architectures, including recurrent neural networks for sequential data.

    • Main Results:

    • Multimodal models integrating AS-OCT, Placido topography, and tabular data achieved high prediction accuracy (AUROC 0.84).
    • Incorporating sequential clinic visit data with long short-term memory networks further improved prediction to AUROC 0.93.
    • Risk stratification identified 58% of patients as low risk after one visit, increasing to 83% after two visits.

    Conclusions:

    • Artificial intelligence models can effectively predict keratoconus progression risk.
    • Risk-stratified monitoring pathways can optimize patient management and resource allocation.
    • AI facilitates personalized follow-up schedules, ensuring timely intervention for high-risk patients.