Alomar X, Medrano J, Cabratosa J, Clavero J, Lorente M, Serra I, Monill J and Salvador A: Anatomy of the temporomandibular joint. Seminars in Ultrasound, CT and MRI. Elsevier, 28:170–183, 2007
Ingawale S and Goswami T: Temporomandibular joint: disorders, treatments, and biomechanics. Ann Biomed Eng, 37:976-996, 2009
Mallya S and Lam E: White and Pharoah’s oral radiology: principles and interpretation, Elsevier Health Sciences, 2018
Al-Ani Z and Gray RJ: Temporomandibular disorders: a problem-based approach. John Wiley & Sons, 2021
Tanaka E and Van Eijden T: Biomechanical behavior of the temporomandibular joint disc. Crit Rev Oral Biol Med, 14:138-150, 2023
Tanaka E and Koolstra J: Biomechanics of the temporomandibular joint. J Dent Res, 87:989-991, 2008
Drace JE and Enzmann DR: Defining the normal temporomandibular joint: closed-, partially open-, and open-mouth MR imaging of asymptomatic subjects. Radiology, 177:67-71, 1990
Young AL: Internal derangements of the temporomandibular joint: A review of the anatomy, diagnosis, and management. J Indian Prosthodont Soc, 15:2-7, 2015
PubMed PubMed Central Google Scholar
Wilkes CH: Internal derangements of the temporomandibular joint: pathological variations. Arch Otolaryngol Head Neck Surg, 115:469-477, 1989
Som PM and Curtin HD: Head and neck imaging: expert consult-online and print. Elsevier Health Sciences, 1547–1553, 2011
Dias IM, Coelho PR, Assis NMSP, Leite FPP and Devito KL: Evaluation of the correlation between disc displacements and degenerative bone changes of the temporomandibular joint by means of magnetic resonance images. Int J Oral Maxaxillofac Surg, 41:1051-1057, 2012
Roh H-S, Kim W, Kim Y-K and Lee J-Y: Relationships between disk displacement, joint effusion, and degenerative changes of the TMJ in TMD patients based on MRI findings. J Craniomaxillofac Surg, 40:283-286, 2012
Perschbacher S: Interpretation of panoramic radiographs. Aust Dent J, 57:40-45, 2012
Honda K, Larheim T, Maruhashi K, Matsumoto K and Iwai K: Osseous abnormalities of the mandibular condyle: diagnostic reliability of cone beam computed tomography compared with helical computed tomography based on an autopsy material. Dentomaxillofac Radiol, 35:152-157, 2006
Katakami K, Shimoda S, Kobayashi K and Kawasaki K: Histological investigation of osseous changes of mandibular condyles with backscattered electron images. Dentomaxillofac Radiol, 37:330-339, 2008
Sano T and Westesson P-L: Magnetic resonance imaging of the temporomandibular joint: increased T2 signal in the retrodiskal tissue of painful joints. Oral Surg Oral Med Oral Pathol Oral Radiol Endod, 79:511-516, 1995
Emshoff R, Rudisch A, Innerhofer K, Brandlmaier I, Moschen I and Bertram S: Magnetic resonance imaging findings of internal derangement in temporomandibular joints without a clinical diagnosis of temporomandibular disorder. J Oral Rehabil, 29:516-522, 2002
Larheim TA: Role of magnetic resonance imaging in the clinical diagnosis of the temporomandibular joint. Cells Tissues Organs, 180:6-21, 2005
Katzberg RW, Bessette RW, Tallents RH, Plewes D, Manzione J, Schenck J, Foster TH and Hart H: Normal and abnormal temporomandibular joint: MR imaging with surface coil. Radiology, 158:183-189, 1986
Brooks SL and Westesson P-L: Temporomandibular joint: value of coronal MR images. Radiology, 188:317-321, 1993
Choi H-M and Park M-S: Cone-Beam Computed Tomographic Assessment of Temporomandibular Joint Morphology in Patients with Temporomandibular Joint Disc Displacement and in Healthy Subjects: A Pilot Study. J Oral Med Pain, 41:41-47, 2016
Chen B and Li C: The relationship between the articular disc in magnetic resonance imaging and the condyle in cone beam computed tomography: a retrospective study. J Stomatol Oral Maxillofac Surg, 125:101940, 2024
Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, Van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R and Dekker A: Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 48:441-446, 2012
PubMed PubMed Central Google Scholar
Jia T-Y, Xiong J-F, Li X-Y, Yu W, Xu Z-Y, Cai X-W, Ma J-C, Ren Y-C, Larsson R and Zhang J: Identifying EGFR mutations in lung adenocarcinoma by noninvasive imaging using radiomics features and random forest modeling. Eur Radiol, 29:4742-4750, 2019
Mayerhoefer ME, Materka A, Langs G, Häggström I, Szczypiński P, Gibbs P and Cook G: Introduction to radiomics. J Nucl Med, 61:488-495, 2020
CAS PubMed PubMed Central Google Scholar
Rastegar S, Vaziri M, Qasempour Y, Akhash M, Abdalvand N, Shiri I, Abdollahi H and Zaidi H: Radiomics for classification of bone mineral loss: a machine learning study. Diagn Interv Imaging, 101:599-610, 2020
Tomaszewski MR and Gillies RJ: The biological meaning of radiomic features. Radiology, 298:505-516, 2021
Linning E, Lu L, Li L, Yang H, Schwartz LH and Zhao B: Radiomics for classification of lung cancer histological subtypes based on nonenhanced computed tomography. Acad Radiol, 26:1245-1252, 2019
He B, Ji T, Zhang H, Zhu Y, Shu R, Zhao W and Wang K: MRI‐based radiomics signature for tumor grading of rectal carcinoma using random forest model. J Cell Physiol, 234:20501-20509, 2019
Jiang Y-W, Xu X-J, Wang R and Chen C-M: Radiomics analysis based on lumbar spine CT to detect osteoporosis. Eur Radiol, 32:8019-8026, 2022
PubMed PubMed Central Google Scholar
Fruehwald-Pallamar J, Hesselink J, Mafee M, Holzer-Fruehwald L, Czerny C and Mayerhoefer M: Texture-based analysis of 100 MR examinations of head and neck tumors–is it possible to discriminate between benign and malignant masses in a multicenter trial?. RöFo-Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren. © Georg Thieme Verlag KG, 188:195–202, 2016
De Araujo Faria V, Azimbagirad M, Viani Arruda G, Fernandes Pavoni J, Cezar Felipe J, dos Santos EMCMF and Murta Junior LO: Prediction of radiation-related dental caries through pyradiomics features and artificial neural network on panoramic radiography. J Digit Imaging, 34:1237-1248, 2021
Jeon KJ, Kim YH, Choi H, Ha E-G, Jeong H and Han S-S: Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study. PLoS One, 18:e0280523, 2023
CAS PubMed PubMed Central Google Scholar
Haghnegahdar, A. A., Kolahi, S., Khojastepour, L., and Tajeripour, F. Diagnosis of tempromandibular disorders using local binary patterns. J Biomed Phys Eng, 8:87-96, 2018
CAS PubMed PubMed Central Google Scholar
Bianchi, J., de Oliveira Ruellas, A. C., Gonçalves, J. R., Paniagua, B., Prieto, J. C., Styner, M., ... & Cevidanes, L. H. S: Osteoarthritis of the temporomandibular joint can be diagnosed earlier using biomarkers and machine learning. Sci Rep, 10:8012, 2020
Orhan, K., Driesen, L., Shujaat, S., Jacobs, R., and Chai, X., Development and validation of a magnetic resonance imaging‐based machine learning model for TMJ pathologies. Biomed Res Int, 2021:6656773, 2021
PubMed PubMed Central Google Scholar
Breiman L: Random forests. Mach Learn, 45:5-32, 2001
Chen T and Guestrin C: Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016
Comments (0)