A comprehensive patient-specific prediction model for temporomandibular joint osteoarthritis progression

Authors

• Najla Al Turkestani, Faculty of Dentistry, King Abdulaziz University
• Tengfei Li, UNC School of Medicine
• Jonas Bianchi, University of the Pacific Arthur A. Dugoni School of DentistryFollow
• Marcela Gurgel, University of Michigan School of Dentistry
• Juan Prieto, UNC School of Medicine
• Hina Shah, UNC School of Medicine
• Erika Benavides, University of Michigan School of Dentistry
• Fabiana Soki, University of Michigan School of Dentistry
• Yuji Mishina, University of Michigan School of Dentistry
• Margherita Fontana, University of Michigan School of Dentistry
• Arvind Rao, University of Michigan Medical School
• Hongtu Zhu, UNC School of Medicine
• Lucia Cevidanes, University of Michigan School of Dentistry

Document Type

Article

Publication Title

Proceedings of the National Academy of Sciences of the United States of America

Department

Orthodontics

ISSN

10916490

Volume

121

Issue

8

DOI

10.1073/pnas.2306132121

First Page

1

Last Page

12

Publication Date

2-20-2024

Abstract

Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative disease characterized by chronic pain and impaired jaw function. The complexity of TMJ OA has hindered the development of prognostic tools, posing a significant challenge in timely, patient-specific management. Addressing this gap, our research employs a comprehensive, multidimensional approach to advance TMJ OA prognostication. We conducted a prospective study with 106 subjects, 74 of whom were followed up after 2 to 3 y of conservative treatment. Central to our methodology is the development of an innovative, open-source predictive modeling framework, the Ensemble via Hierarchical Predictions through Nested cross-validation tool (EHPN). This framework synergistically integrates 18 feature selection, statistical, and machine learning methods to yield an accuracy of 0.87, with an area under the ROC curve of 0.72 and an F1 score of 0.82. Our study, beyond technical advancements, emphasizes the global impact of TMJ OA, recognizing its unique demographic occurrence. We highlight key factors influencing TMJ OA progression. Using SHAP analysis, we identified personalized prognostic predictors: lower values of headache, lower back pain, restless sleep, condyle high gray level-GL-run emphasis, articular fossa GL nonuniformity, and long-run low GL emphasis; and higher values of superior joint space, mouth opening, saliva Vascular-endothelium-growth-factor, Matrix-metalloproteinase-7, serum Epithelial-neutrophil-activating-peptide, and age indicate recovery likelihood. Our multidimensional and multimodal EHPN tool enhances clinicians' decision-making, offering a transformative translational infrastructure. The EHPN model stands as a significant contribution to precision medicine, offering a paradigm shift in the management of temporomandibular disorders and potentially influencing broader applications in personalized healthcare.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Recommended Citation

Al Turkestani, Najla; Li, Tengfei; Bianchi, Jonas; Gurgel, Marcela; Prieto, Juan; Shah, Hina; Benavides, Erika; Soki, Fabiana; Mishina, Yuji; Fontana, Margherita; Rao, Arvind; Zhu, Hongtu; and Cevidanes, Lucia, "A comprehensive patient-specific prediction model for temporomandibular joint osteoarthritis progression" (2024). Pacific Faculty Work. 44.
https://scholarlycommons.pacific.edu/all-faculty/44