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

arXiv: https://arxiv.org/abs/2601.11642 · HTML: https://mlsanity.com/paper/2601.11642/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 14 Jan 2026.

## Builds on (most cited first, 9 of 9)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Confounding variables can degrade generalization performance of radiological deep learning models](https://mlsanity.com/paper/1807.00431/) (2018)
- [Generative Adversarial Network in Medical Imaging: A Review](https://mlsanity.com/paper/1809.07294/) (2018)
- [Image biomarker standardisation initiative](https://mlsanity.com/paper/1612.07003/) (2016)
- [Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach](https://mlsanity.com/paper/1710.10589/) (2017)
- [Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data](https://mlsanity.com/paper/1904.06236/) (2019)
- [Automatic Grading of Individual Knee Osteoarthritis Features in Plain Radiographs using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1907.08020/) (2019)
- [Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists](https://mlsanity.com/paper/2207.12521/) (2022)
- [Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis](https://mlsanity.com/paper/2311.06118/) (2023)

## Abstract (arXiv)

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.