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

• URL: http://arxiv.org/abs/2601.11642v1
• Date: Wed, 14 Jan 2026 16:54:20 GMT
• Title: PSSF: Early osteoarthritis detection using physical synthetic knee X-ray scans and AI radiomics models
• Authors: Abbas Alzubaidi, Ali Al-Bayaty,
• Abstract summary: Osteoarthritis is a major cause of disability worldwide.<n>X-ray scans are often difficult to obtain, mainly because of privacy and institutional constraints.<n>This research introduces a physics-based synthetic simulation framework (PSSF) to generate controllable X-ray scans.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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