Automated Segmentation of Knee Joint Cartilage: A Clinical Applicability Study of pyKNEEr
Λέξεις-κλειδιά:
cartilage segmentation, Hyaline cartilage, Arthroscopic findings, Pykneer, WORMS scaleΠερίληψη
Objective: This study aims to evaluate the feasibility and accuracy of the Python Knee Cartilage Image Analysis Workflow (pyKNEEr), open-source tool for automated segmentation of standard sagittal magnetic resonance imaging (MRI) in assessing femoral knee joint cartilage tissue changes, in comparison with the established Whole-Organ Magnetic Resonance Imaging Score (WORMS) and actual arthroscopic findings.
Materials and methods: This study, conducted from January to October 2022, involved a cohort of 10 patients with varying degrees of femoral bone cartilage changes. The patients underwent knee arthroscopy for internal meniscal damage. Sagittal MRI tomograms were analyzed using pyKNEEr v0.0.5 for cartilage tissue measurements, and manual assessment was performed using the WORMS scale. Statistical data processing was performed using Pingouin 0.5.3 and Numerical Python (NumPy) 1.24.2 for Python 3.9 (Python Software Foundation, Delaware, USA).
Results: The pyKNEEr analysis showed an average total cartilage thickness of 2.26 ± 0.21 mm, (2.33 ± 0.26 mm for men, 2.22 ± 0.19 mm for women), and an average total cartilage volume of 10242.2 ± 1860.75 mm³, (10,380.25 ± 2,654.41 mm³ for men, 10,150.17 ± 1,406.89 mm³ for women).
A statistically significant strong inverse correlation was found between cartilage thickness and WORMS score (r=-0.813, 95% CI -0.95 to -0.38, p=0.025). Additionally, a moderate inverse correlation was observed between cartilage volume and WORMS score (r=-0.777, 95% CI -0.94 to -0.29, p=0.049). No statistically significant correlations were identified by using the ICRS scale.
Furthermore, there was no significant association between cartilage thickness and volume as determined using pyKNEEr.
Conclusion: pyKNEEr for automated segmentation of standard sagittal MRI images, demonstrates alignment with the WORMS scale, but neither pyKNEEr’s automated segmentation nor the WORMS scale showed a statistically significant correlation with the arthroscopic depiction of cartilage defects.
