Decoding Parkinson’s diagnosis: An OCT-based explainable AI with SHAP/LIME transparency from the Persian Cohort Study - Zohreh Ganji; Farzaneh Nikparast; Naser Shoeibi; Ali Shoeibi; Hoda Zare (Photodiagnosis and Photodynamic Therapy)
Background: Parkinson’s disease (PD) diagnosis remains challenging due to subjective clinical assessments and late-stage symptom manifestation. Retinal optical coherence tomography (OCT) biomarkers, reflecting neurodegenerative changes, offer a non-invasive diagnostic avenue. This study integrates retinal OCT with explainable artificial intelligence (XAI) to address PD diagnostic uncertainties. Methods: Leveraging data from the Persian Cohort Study (202 PD patients, 972 controls), we developed a 6-layer deep neural network (DNN) combining OCT biomarkers (foveal thickness and volume) and clinical variables (motor scores, olfactory dysfunction). Synthetic Minority Oversampling (SMOTE) mitigated class imbalance (PD:Healthy ≈ 1:5). Model interpretability was ensured via SHAP (global feature importance) and LIME (local explanations). Results: This study developed an explainable AI framework integrating retinal OCT biomarkers and clinical data to diagnose Parkinson’s disease (PD) with 95.3 % accuracy and 0.98 AUC-ROC. Using data from the Persian Cohort (1176 participants), the model identified SUPERIOR4 thickness (<120 µm) and foveal volume expansion (>0.15 mm³) as key biomarkers, alongside motor and olfactory deficits. SHAP/LIME provided interpretable thresholds (e.g., SUPERIOR4 <120 µm = high risk), while SMOTE mitigated class imbalance, reducing false negatives by 12 % without compromising specificity (94.8 %). Conclusion: This study pioneers a transparent, OCT-based AI framework for PD diagnosis, emphasizing early detection through retinal neurodegeneration patterns. The integration of multimodal data, explainability, and imbalance robustness positions it as a scalable tool for resource-limited settings. Future work should validate biomarkers across diverse populations and standardize OCT protocols.