PSSF: Early osteoarthritis detection using physical synthetic knee X-ray scans and AI radiomics models

TL;DR

This paper introduces a physics-based synthetic simulation framework (PSSF) to generate controllable knee X-ray images for early osteoarthritis detection, enabling AI radiomics models without patient data.

Contribution

The study presents a novel PSSF that creates synthetic knee X-ray datasets for OA assessment, overcoming privacy and data scarcity issues in medical imaging.

Findings

Generated 180 virtual subjects with multiple imaging protocols.

Achieved accurate OA classification using ML models on synthetic data.

Demonstrated robustness and feature stability across protocols.

Abstract

Knee osteoarthritis (OA) is a major cause of disability worldwide and is still largely assessed using subjective radiographic grading, most commonly the Kellgren-Lawrence (KL) scale. Artificial intelligence (AI) and radiomics offer quantitative tools for OA assessment but depend on large, well-annotated image datasets, mainly X-ray scans, that are often difficult to obtain because of privacy, governance and resourcing constraints. In this research, we introduce a physics-based synthetic simulation framework (PSSF) to fully generate controllable X-ray scans without patients' involvement and violating their privacy and institutional constraints. This PSSF is a 2D X-ray projection simulator of anteroposterior knee radiographs from a parametric anatomical model of the distal femur and proximal tibia. Using PSSF, we create a virtual cohort of 180 subjects (260 knees), each is imaged under three protocols (reference, low-dose, and geometry-shift). Medial joint regions are automatically localized, preprocessed, and processed with the Image Biomarker Standardisation Initiative (IBSI). Practically, three machine learning (ML) models are utilized, logistic regression, random forest, and gradient boosting, to train binary (KL-like "0" vs. "2") and three-class (0-2) prediction radiographic images. Robustness is assessed within IBSI protocol, cross-protocol, and multi-protocol scenarios. Finally, features stability is then evaluated using intraclass correlation coefficients across acquisition changes.