Role of Thermography in Assessment of Mygenous Joint Disorder

Fathima, Nida; Dharman, Sreedevi

doi:10.13005/bpj/1753

Cited by 3 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In accordance with the results of photographs in this patient, it was found that there was a difference of up to 0.5°C, which may be used to identify the disturbance in the TMJ. [ 8 9 10 ]…”

Section: Discussionmentioning

confidence: 99%

Thermal condition of muscle area around the temporomandibular joint in patient with systemic lupus erythematosus using infrared thermography application: A case report

Rahmayani

Yahya

Soraya

et al. 2020

J Int Soc Prevent Communit Dent

View full text Add to dashboard Cite

A BSTRACT Recently, the use of infrared thermography in medical has been increasingly developed and widely used in medical devices to detect diseases, including one used in the field of dentistry, which can be used to detect joint conditions in case of temporomandibular disorder (TMD). Some literature has shown this method of infrared thermography was used to determine the surface temperature of the skin based on the emission of infrared radiation from the body. Thermal measurement is also a noninvasive method that does not provide patient inconvenience, but its application until now has not been so wide. The case study reported on the description of thermal condition of muscle area around temporomandibular joint (TMJ) in a 42-year-old woman with systemic lupus erythematosus (SLE) disease. She had experienced TMD. Infrared thermography is applied to observe the thermal condition of the muscle area around the right and left joints by thermal detection. Thermal measurement was obtained on infrared image capture, and the temperature difference was found to be greater than 0.3°C. Several studies have shown that temperature in the area around TMJ was higher, and thermal asymmetry was greater in individuals with joint disorder/TMD when compared with normal groups.

show abstract

Section: Discussionmentioning

confidence: 99%

Thermal condition of muscle area around the temporomandibular joint in patient with systemic lupus erythematosus using infrared thermography application: A case report

Rahmayani

Yahya

Soraya

et al. 2020

J Int Soc Prevent Communit Dent

View full text Add to dashboard Cite

show abstract

“…Fathima et al highlighted the importance of thermography which is used as a diagnostic tool in dentistry to evaluate the OFP. They also indicated that there is an increased facial temperature found in OFP patients than the asymptomatic patients (Fathima & Dharman, 2019). The validation of thermography in the assessment of facial disorders reported in the literature has motivated us to develop an orofacial assessment tool based on facial thermography.…”

mentioning

confidence: 96%

Automated detection of orofacial pain from thermograms using machine learning and deep learning approaches

Snekhalatha

Krishnan

2021

Expert Systems

View full text Add to dashboard Cite

The main objectives of this study are (i) to perform automated segmentation of facial regions from thermograms using k-means clustering algorithm and to classify the data into normal and orofacial pain (OFP) categories using various machine learning classifiers (ii) to implement the convolutional neural network (CNN) for classification of normal and OFP subjects which involves automated feature extraction and feature selection process. Fifty normal and 50 diseased cases suffering from orofacial pain were included in the study. Facial thermograms were segmented using k-means algorithm, then statistical features were extracted and classified into normal and OFP using various machine learning classifier. Further, the deep learning networks such as VGG-16 and DenseNet-121 were used for automated feature extraction and classification of facial thermograms. The facial temperature variations of 3.46%, 3.4%, and 3.27% were observed in the front, right and left side facial regions respectively between the normal and the OFP subjects. Machine learning classifiers such as support vector machine (SVM) and random forest (RF) classifier provided the highest accuracy of 99%. On the other hand, deep learning models such as modified VGG-16 achieved an average accuracy of 97% compared to modified DenseNet-121 which produced an average accuracy of 68% in classification of normal and OFP thermograms. Thus, computer aided diagnosis of facial thermography could be used as a viable screening device for a reliable identification of tooth pathology before the occurrence of structural changes and complications.

show abstract