Interpretability and Individuality in Knee MRI: Patient-Specific Radiomic Fingerprint with Reconstructed Healthy Personas

2026-01-13

AI Summary

This study addresses the limitations of conventional radiomics, which relies on population-level features and struggles to capture individual variability while balancing performance and interpretability. The authors propose two complementary strategies: first, constructing patient-specific radiomic fingerprints by dynamically selecting the most predictive individual features through image-conditioned feature correlation; and second, leveraging a diffusion model to generate personalized healthy knee MRI scans as baselines to highlight pathological deviations. This work represents the first integration of individualized feature selection with diffusion-based generation of healthy anatomical references. The approach achieves performance comparable to or exceeding state-of-the-art deep learning models across three clinical tasks, while enabling case-level pathological localization, biomarker discovery, and multi-level interpretability.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Computer Vision: Diffusion Models for Vision
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

For automated assessment of knee MRI scans, both accuracy and interpretability are essential for clinical use and adoption. Traditional radiomics rely on predefined features chosen at the population level; while more interpretable, they are often too restrictive to capture patient-specific variability and can underperform end-to-end deep learning (DL). To address this, we propose two complementary strategies that bring individuality and interpretability: radiomic fingerprints and healthy personas. First, a radiomic fingerprint is a dynamically constructed, patient-specific feature set derived from MRI. Instead of applying a uniform population-level signature, our model predicts feature relevance from a pool of candidate features and selects only those most predictive for each patient, while maintaining feature-level interpretability. This fingerprint can be viewed as a latent-variable model of feature usage, where an image-conditioned predictor estimates usage probabilities and a transparent logistic regression with global coefficients performs classification. Second, a healthy persona synthesises a pathology-free baseline for each patient using a diffusion model trained to reconstruct healthy knee MRIs. Comparing features extracted from pathological images against their personas highlights deviations from normal anatomy, enabling intuitive, case-specific explanations of disease manifestations. We systematically compare fingerprints, personas, and their combination across three clinical tasks. Experimental results show that both approaches yield performance comparable to or surpassing state-of-the-art DL models, while supporting interpretability at multiple levels. Case studies further illustrate how these perspectives facilitate human-explainable biomarker discovery and pathology localisation.

Problem

• interpretability
• individuality
• knee MRI
• radiomics
• patient-specific

Innovation

• radiomic fingerprint
• healthy persona
• interpretable AI
• diffusion model
• patient-specific modeling