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Comparing Auto-Machine Learning and Expert-Designed Models in Diagnosing Vitreomacular Interface Disorders.

Ceren Durmaz Engin1,2, Mahmut Ozan Gokkan2, Seher Koksaldi3

  • 1Department of Ophthalmology, Izmir Democracy University Buca Seyfi Demirsoy Education and Research Hospital, Izmir 35390, Turkey.

Journal of Clinical Medicine
|April 26, 2025
PubMed
Summary

An expert-designed deep learning model outperformed AutoML in classifying vitreomacular interface disorders from OCT images, achieving higher accuracy for specific conditions like macular holes.

Keywords:
AutoMLEfficientNet B0ResNet-50deep learningoptical coherence tomographyvitreomacular interface disorders

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Vitreomacular interface (VMI) disorders significantly impact vision and require precise classification.
  • Accurate diagnosis of VMI disorders is crucial for effective patient management.
  • Optical coherence tomography (OCT) is a key imaging modality for VMI assessment.

Purpose of the Study:

  • To compare the diagnostic performance of a custom deep learning (DL) model against an automated machine learning (AutoML) model.
  • To evaluate the effectiveness of these AI models in classifying various VMI disorders using OCT images.
  • To determine the superiority of expert-designed AI versus automated AI in a clinical context.

Main Methods:

  • A balanced dataset of OCT images was curated, including normal cases and five VMI disorder classes: epiretinal membrane (ERM), idiopathic full-thickness macular hole (FTMH), lamellar macular hole (LMH), and vitreomacular traction (VMT).
  • An expert-designed DL model integrating ResNet-50 and EfficientNet-B0 architectures with Monte Carlo cross-validation was developed.
  • A code-free AutoML model was implemented on Google Vertex AI for automated data processing, model selection, and hyperparameter tuning.

Main Results:

  • The expert-designed DL model achieved 95.97% balanced accuracy and 94.65% MCC, outperforming AutoML in classifying FTMH, ERM, and LMH.
  • Both models demonstrated perfect precision and recall for normal OCT images.
  • AutoML showed strong performance in VMT detection (99.5% precision) but lagged in LMH classification (72.3% precision).

Conclusions:

  • Expert-designed deep learning models demonstrate superior accuracy for specific vitreomacular interface disorder classifications compared to current AutoML platforms.
  • While AutoML offers accessibility for healthcare professionals, further advancements are needed to match expert-driven AI performance in clinical OCT image analysis.
  • The findings highlight the potential of tailored AI solutions for improving the diagnostic accuracy of retinal disorders.