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Neurosymbolic AI Framework for Explainable Retinal Disease Classification From OCT Images
Aleksandar Miladinovic1, Alessandro Biscontin1, Miloš Ajcevic2
1Institute for Maternal and Child Health IRCCS "Burlo Garofolo," Trieste, Italy.
Translational Vision Science & Technology
|January 14, 2026
Summary
A new neurosymbolic AI model enhances retinal disease classification by combining deep learning with clinical rules. This approach improves diagnostic accuracy and provides explainable decisions for conditions like macular degeneration and macular holes.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Medical Imaging
Background:
- Accurate classification of retinal diseases is crucial for effective patient treatment.
- Traditional deep learning models face challenges with imbalanced datasets and lack interpretability in ophthalmology.
Purpose of the Study:
- To develop and evaluate a neurosymbolic framework for improved classification of retinal diseases.
- To enhance diagnostic performance and clinical interpretability in ophthalmology using AI.
Main Methods:
- A hybrid neurosymbolic framework integrating a convolutional neural network (CNN) with a symbolic reasoning layer was proposed.
- 10,846 optical coherence tomography images across seven diagnostic classes were analyzed.
Main Results:
- The neurosymbolic model achieved superior performance on both internal (macro-F1: 0.81) and external (macro-F1: 0.78) datasets compared to a standalone CNN.
- The model demonstrated improved macro-precision and recall, outperforming the CNN in diagnostic accuracy.
Conclusions:
- The proposed neurosymbolic framework offers a unified paradigm coupling symbolic reasoning with CNNs for enhanced diagnostic performance.
- This approach provides transparent, clinically interpretable decisions, particularly beneficial for rare and complex retinal conditions.
