Poster + Paper
3 April 2023 Integrative risk predictors of temporomandibular joint osteoarthritis progression
Author Affiliations +
Conference Poster
Abstract
In this paper we propose feature selection and machine learning approaches to identify a combination of features for risk prediction of Temporomandibular Joint (TMJ) disease progression. In a sample of 32 TMJ osteoarthritis and 38 controls, feature selection of 5 clinical comorbidities, 43 quantitative imaging, 28 biological features and was performed using Maximum Relevance Minimum Redundancy, Chi-Square and Least Absolute Shrinkage and Selection Operator (LASSO) and Recursive Feature Elimination. We compared the performance of learning using concave and convex kernels (LUCCK), Support Vector Machine (SVM) and Random Forest (RF) approaches to predict disease cure/improvement or persistence/worsening. We show that the SVM model using LASSO achieves area under the curve (AUC), sensitivity and precision of 0.92±0.08, 0.85±0.19 and 0.76 ±0.18, respectively. Baseline levels of headaches, lower back pain, restless sleep, muscle soreness, articular fossa bone surface/bone volume and trabecular separation, condylar High Gray Level Run Emphasis and Short Run High Gray Level Emphasis, saliva levels of 6Ckine, Osteoprotegerin (OPG) and Angiogenin, and serum levels of 6ckine and Brain Derived Neurotrophic Factor (BDNF) were the most frequently occurring features to predict more severe TMJ osteoarthritis prognosis.
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Lingrui Cai, Najla Al Turkestani, Lucia Cevidanes, Jonas Bianchi, Marcela Gurgel, Kayvan Najarian, and Reza Soroushmehr "Integrative risk predictors of temporomandibular joint osteoarthritis progression", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124641N (3 April 2023); https://doi.org/10.1117/12.2651940
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KEYWORDS
Feature selection

Machine learning

Bone

Diseases and disorders

Cross validation

Tunable filters

Visualization

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