A Multi-Dataset Deep Learning and Sequential Pattern Mining Framework for Interpretable Disease Progression Tracking in Brain and Liver MRI

This study makes a significant contribution by proposing a novel hybrid framework that integrates deep learning (CNN/ResNet-18) with Sequential Pattern Mining (PrefixSpan) to bridge the gap between predictive accuracy and clinical interpretability in medical image analysis. The framework introduces a robust discretization technique that transforms continuous, high-dimensional deep imaging features into clinically meaningful symbolic states (Low, Medium, High), enabling the application of pattern mining without losing critical progression information. It further demonstrates a dual application of pattern mining by capturing temporal disease evolution in longitudinal data (LUMIERE) while also uncovering recurring spatial phenotypes in cross-sectional data (BraTS2021 and CirrMRI600plus). The framework’s generalizability is validated through comprehensive testing across three public MRI datasets covering both brain and liver diseases, achieving strong accuracies of 91.2%, 87.5%, and 85.1%, respectively. Ultimately, this research delivers clinically interpretable insights that allow healthcare professionals to understand and trust model outputs without specialized machine learning expertise, offering a practical, scalable, and explainable AI-based clinical decision support solution, particularly suited for resource-limited healthcare environments.