ClinNet: Evidential Ordinal Regression with Bilateral Asymmetry and Prototype Memory for Knee Osteoarthritis Grading

2026-01-24

AI Summary

This study addresses the challenges in radiographic grading of knee osteoarthritis (KOA)—including subtle inter-grade differences, expert annotation uncertainty, and the ordinal nature of disease progression—by proposing a novel evidential ordinal regression framework. The method uniquely integrates evidential learning with ordinal regression, employing a bilateral asymmetric encoder to model structural asymmetries in the knee joint, a diagnostic prototype memory bank to enhance class representation, and a Normal-Inverse-Gamma distribution-based output head to jointly estimate Kellgren–Lawrence (KL) grades and epistemic uncertainty. Evaluated on standard datasets, the model achieves a quadratic weighted Kappa of 0.892 and an accuracy of 0.768 (p < 0.001), significantly outperforming existing approaches. It also effectively identifies out-of-distribution samples and potential misdiagnoses, offering reliable uncertainty quantification to support safe clinical decision-making.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Knee osteoarthritis (KOA) grading based on radiographic images is a critical yet challenging task due to subtle inter-grade differences, annotation uncertainty, and the inherently ordinal nature of disease progression. Conventional deep learning approaches typically formulate this problem as deterministic multi-class classification, ignoring both the continuous progression of degeneration and the uncertainty in expert annotations. In this work, we propose ClinNet, a novel trustworthy framework that addresses KOA grading as an evidential ordinal regression problem. The proposed method integrates three key components: (1) a Bilateral Asymmetry Encoder (BAE) that explicitly models medial-lateral structural discrepancies; (2) a Diagnostic Memory Bank that maintains class-wise prototypes to stabilize feature representations; and (3) an Evidential Ordinal Head based on the Normal-Inverse-Gamma (NIG) distribution to jointly estimate continuous KL grades and epistemic uncertainty. Extensive experiments demonstrate that ClinNet achieves a Quadratic Weighted Kappa of 0.892 and Accuracy of 0.768, statistically outperforming state-of-the-art baselines (p<0.001). Crucially, we demonstrate that the model's uncertainty estimates successfully flag out-of-distribution samples and potential misdiagnoses, paving the way for safe clinical deployment.

Problem

• knee osteoarthritis grading
• annotation uncertainty
• ordinal regression
• radiographic images
• disease progression

Innovation

• evidential ordinal regression
• bilateral asymmetry
• prototype memory
• epistemic uncertainty
• knee osteoarthritis grading