Abstract
Osteoarthritis is a chronic disease that affects the temporomandibular joint (TMJ), causing chronic pain and disability. To diagnose patients suffering from this disease before advanced degradation of the bone, we developed a diagnostic tool called TMJOAI. This machine learning based algorithm is capable of classifying the health status TMJ in of patients using 52 clinical, biological and jaw condyle radiomic markers. The TMJOAI includes three parts. the feature preparation, selection and model evaluation. Feature generation includes the choice of radiomic features (condylar trabecular bone or mandibular fossa), the histogram matching of the images prior to the extraction of the radiomic markers, the generation of feature pairwise interaction, etc.; the feature selection are based on the p-values or AUCs of single features using the training data; the model evaluation compares multiple machine learning algorithms (e.g. regression-based, tree-based and boosting algorithms) from 10 times 5-fold cross validation. The best performance was achieved with averaging the predictions of XGBoost and LightGBM models; and the inclusion of 32 additional markers from the mandibular fossa of the joint improved the AUC prediction performance from 0.83 to 0.88. After cross-validation and testing, the tools presented here have been deployed on an open-source, web-based system, making it accessible to clinicians. TMJOAI allows users to add data and automatically train and update the machine learning models, and therefore improve their performance.
Supported by NIDCR DE024550 and AAOF Dewel Biomedical research Award.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Appel, R., Fuchs, T., Dollár, P., Perona, P.: Quickly boosting decision trees-pruning underachieving features early. In: International Conference on Machine Learning, pp. 594–602. PMLR (2013)
Bianchi, J., et al.: Osteoarthritis of the temporomandibular joint can be diagnosed earlier using biomarkers and machine learning. Sci. Rep. 10(1), 1–14 (2020)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Brosset, S., et al.: Web infrastructure for data management, storage and computation. In: Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging, vol. 11600, p. 116001N. International Society for Optics and Photonics (2021)
Center for disease control and prevention. data and statistics. https://www.cdc.gov/arthritis/data_statistics/index.htm. Accessed July 2021
Cevidanes, L.H., et al.: 3D osteoarthritic changes in TMJ condylar morphology correlates with specific systemic and local biomarkers of disease. Osteoarthr. Cartil. 22(10), 1657–1667 (2014)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM, New York (2016). 10(2939672.2939785)
Chen, T., Li, H., Yang, Q., Yu, Y.: General functional matrix factorization using gradient boosting. In: International Conference on Machine Learning, pp. 436–444. PMLR (2013)
Cosma, G., Brown, D., Archer, M., Khan, M., Pockley, A.G.: A survey on computational intelligence approaches for predictive modeling in prostate cancer. Expert Syst. Appl. 70, 1–19 (2017)
Ebrahim, F.H., et al.: Accuracy of biomarkers obtained from cone beam computed tomography in assessing the internal trabecular structure of the mandibular condyle. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. 124(6), 588–599 (2017)
Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)
Heard, B.J., Rosvold, J.M., Fritzler, M.J., El-Gabalawy, H., Wiley, J.P., Krawetz, R.J.: A computational method to differentiate normal individuals, osteoarthritis and rheumatoid arthritis patients using serum biomarkers. J. R. Soc. Interface 11(97), 20140428 (2014)
Jamshidi, A., Pelletier, J.P., Martel-Pelletier, J.: Machine-learning-based patient-specific prediction models for knee osteoarthritis. Nat. Rev. Rheumatol. 15(1), 49–60 (2019)
Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30, 3146–3154 (2017)
Kuo, D.E., et al.: Gradient boosted decision tree classification of endophthalmitis versus uveitis and lymphoma from aqueous and vitreous IL-6 and IL-10 levels. J. Ocul. Pharmacol. Ther. 33(4), 319–324 (2017)
Kuo, D.E., et al.: Logistic regression classification of primary vitreoretinal lymphoma versus uveitis by interleukin 6 and interleukin 10 levels. Ophthalmology 127(7), 956–962 (2020)
Lazzarini, N., et al.: A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women. Osteoarthr. Cartil. 25(12), 2014–2021 (2017)
Li, P., Wu, Q., Burges, C.: McRank: learning to rank using multiple classification and gradient boosting. Adv. Neural Inf. Process. Syst. 20, 897–904 (2007)
Liu, Y., et al.: Multiple treatment meta-analysis of intra-articular injection for temporomandibular osteoarthritis. J. Oral Maxillofac. Surg. 78(3), 373-e1 (2020)
National institute of dental and craniofacial research. facial pain. https://www.nidcr.nih.gov/research/data-statistics/facial-pain. Accessed July 2021
Oguz, B.U., Shinohara, R.T., Yushkevich, P.A., Oguz, I.: Gradient boosted trees for corrective learning. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 203–211. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67389-9_24
Paniagua, B., et al.: Validation of CBCT for the computation of textural biomarkers. In: Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, vol. 9417, p. 94171B. International Society for Optics and Photonics (2015)
Wang, X., Zhang, J., Gan, Y., Zhou, Y.: Current understanding of pathogenesis and treatment of TMJ osteoarthritis. J. Dent. Res. 94(5), 666–673 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Le, C. et al. (2021). TMJOAI: An Artificial Web-Based Intelligence Tool for Early Diagnosis of the Temporomandibular Joint Osteoarthritis. In: Oyarzun Laura, C., et al. Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning. DCL PPML LL-COVID19 CLIP 2021 2021 2021 2021. Lecture Notes in Computer Science(), vol 12969. Springer, Cham. https://doi.org/10.1007/978-3-030-90874-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-90874-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90873-7
Online ISBN: 978-3-030-90874-4
eBook Packages: Computer ScienceComputer Science (R0)