Expert Knowledge Driven Human-AI Collaboration for Medical Imaging: A Study on Epileptic Seizure Onset Zone Identification

Supervised artificial intelligence (AI) techniques are good at learning class specific characteristic properties despite variance across samples. However, for rare class classification, challenges arise due to class imbalance. On the contrary, knowledge-based techniques encode class specific information without class labels but face difficulties in parsing knowledge which is often vague, uncertain, and result in high intraclass variability. This manuscript presents a human-AI collaboration methodology to integrate AI with expert knowledge for rare class classification, mitigating class imbalance and intraclass variability effects. We present a formal framework for expert knowledge representation using logical connectives of atomic propositions, a rule refinement strategy to derive class specific machine checkable formulas, and a rule implementation strategy that extracts explainable partitions of rare class expert rules for its recognition. A knowledge-AI integration strategy is presented that uses entropy imbalance gain and Gini index to quantify class imbalance and intraclass variability, and orchestrates supervised AI and expert knowledge machines to effectively identify rare class through human-AI collaboration with reduced human effort. We apply the proposed integration framework to develop DeepXSOZ that identifies seizure onset zones (SOZ) in focal epilepsy patients from resting state functional magnetic resonance imaging. DeepXSOZ's performance is validated on multicenter datasets against anatomical MRI-based manual SOZ identification, and Engel outcomes after surgical SOZ alteration. This human-AI collaboration demonstrates increased F1 score compared with state-of-the-art “AI-only” techniques, minimal data leakage effect with statistically similar performance across multicenter datasets without fine tuning, consistent results across age and gender, and reduced manual effort.