Artificial intelligence and infrared thermography as auxiliary tools in the diagnosis of temporomandibular disorder

2021 ◽  
Author(s):  
Elisa Diniz de Lima ◽  
José Alberto Souza Paulino ◽  
Ana Priscila Lira de Farias Freitas ◽  
José Eraldo Viana Ferreira ◽  
Jussara da Silva Barbosa ◽  
...  
Keyword(s):  
Infrared Thermography ◽  
Temporomandibular Disorder ◽  
Extraction Methods ◽  
Pain Scale ◽  
Semantic Features ◽  
Semantic Association ◽  
Significance Level ◽  
Attribute Extraction ◽  
Occurrence Matrix ◽  
Mlp Classifier

Objective: To assess three machine learning (ML) attribute extraction methods: radiomic, semantic and radiomic-semantic association on temporomandibular disorder (TMD) detection using infrared thermography (IT); and to determine which ML classifier, KNN, SVM and MLP, is the most efficient for this purpose. Methods and materials: 78 patients were selected by applying the Fonseca questionnaire and RDC/TMD to categorize control patients (37) and TMD patients (41). IT lateral projections of each patient were acquired. The masseter and temporal muscles were selected as regions of interest (ROI) for attribute extraction. Three methods of extracting attributes were assessed: radiomic, semantic and radiomic-semantic association. For radiomic attribute extraction, 20 texture attributes were assessed using co-occurrence matrix in a standardized angulation of 0°. The semantic features were the ROI mean temperature and pain intensity data. For radiomic-semantic association, a single dataset composed of 28 features was assessed. The classification algorithms assessed were KNN, SVM and MLP. Hopkins’s statistic, Shapiro–Wilk, ANOVA and Tukey tests were used to assess data. The significance level was set at 5% (p < 0.05). Results: Training and testing accuracy values differed statistically for the radiomic-semantic association (p = 0.003). MLP differed from the other classifiers for the radiomic-semantic association (p = 0.004). Accuracy, precision and sensitivity values of semantic and radiomic-semantic association differed statistically from radiomic features (p = 0.008, p = 0.016 and p = 0.013). Conclusion: Semantic and radiomic-semantic-associated ML feature extraction methods and MLP classifier should be chosen for TMD detection using IT images and pain scale data. IT associated with ML presents promising results for TMD detection.

Using Texture Feature in Fruit Classification

2021 ◽  
Vol 39 (1B) ◽  
pp. 67-79
Author(s):  
Mauj H. Abd al kreem ◽  
Abd allameer A. Karim
Keyword(s):  
Fruits And Vegetables ◽  
Real Life ◽  
Texture Feature ◽  
Extraction Methods ◽  
Complex Problem ◽  
Machine Learning Techniques ◽  
Tree Classifier ◽  
Fresh Products ◽  
Occurrence Matrix

Recent advances in computer vision have allowed wide-ranging applications in every area of ​​life. One such area of ​​application is the classification of fresh products, but the classification of fruits and vegetables has proven to be a complex problem and needs further development. In recent years, various machine learning techniques have been exploited with many methods of describing the different features of fruit and vegetable classification in many real-life applications. Classification of fruits and vegetables presents significant challenges due to similarities between layers and irregular characteristics within the class.Hence , in this work, three feature extractor/ descriptor which are local binary pattern (LBP), gray level co-occurrence matrix (GLCM) and, histogram of oriented gradient(HoG) has been proposed to extract fruite features , the  extracted  features have been saved in three feature vectors , then desicion tree classifier has been proposed to classify the fruit types. fruits 360 datasets  is  used  in this work,   where 70% of the dataset were used  in the training phase while 30% of it used in the testing phase. The three proposed feature extruction methods plus the tree  classifier have been used to  classifying  fruits 360 images, results show that the the three feature extraction methods  give a promising results , while the HoG method yielded a poerfull results in which  the accuracy obtained is 96%.

scholarly journals Analysis of the frequency and degree of temporomandibular disorder in patients with head and neck cancer undergoing radiotherapy

2016 ◽  
Vol 29 (2) ◽  
pp. 361-368 ◽  
Author(s):  
Douglas Roberto Pegoraro ◽  
Barbara Zanchet ◽  
Caroline de Oliveira Guariente ◽  
Josemara de Paula Rocha ◽  
Juliana Secchi Batista
Keyword(s):  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Temporomandibular Disorder ◽  
Malignant Neoplasm ◽  
Post Treatment ◽  
Chi Square ◽  
Significance Level ◽  
Chi Square Test ◽  
Quantitative Descriptive

Abstract Introduction: Head and neck cancer is responsible for an increasing incidence of primary malignant neoplasm cases worldwide. Radiotherapy is one of the treatments of choice for this type of cancer, but it can cause adverse effects, such as temporomandibular disorder. The objective of this study was to characterize the degree and frequency of temporomandibular disorder in patients with head and neck cancer undergoing radiotherapy. Method: This research was quantitative, descriptive and exploratory. The sample consisted of 22 patients that answered assessment questions and the Helkimo anamnestic questionnaire, modified by Fonseca (1992). The data were collected from May to October 2014, and statistically analyzed using the Chi-square test, with a significance level of p ≤ 0.05. Results: Of the 22 patients, 86.4 % were male, with a mean age of 58.86 ± 9.41 years. Temporomandibular disorder was present in 31.8% of the subjects, based on the assessment prior to radiotherapy, and in 59.1% in the post-treatment assessment. Among all questions, the most frequent was "Do you use only one side of the mouth to chew?" with 22.7% "yes" answers, both at the first assessment and at the post treatment. Conclusion: According to the results of this study, temporomandibular disorder is a disease that is present with a high prevalence in people diagnosed with head and neck cancer undergoing radiotherapy.

scholarly journals A Novel Approach to Word Sense Disambiguation Based on Topical and Semantic Association

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xin Wang ◽  
Wanli Zuo ◽  
Ying Wang
Keyword(s):  
Language Processing ◽  
Fundamental Problem ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Semantic Features ◽  
Word Sense ◽  
Semantic Association ◽  
Data Set ◽  
Novel Approach ◽  
Sense Disambiguation

Word sense disambiguation (WSD) is a fundamental problem in nature language processing, the objective of which is to identify the most proper sense for an ambiguous word in a given context. Although WSD has been researched over the years, the performance of existing algorithms in terms of accuracy and recall is still unsatisfactory. In this paper, we propose a novel approach to word sense disambiguation based on topical and semantic association. For a given document, supposing that its topic category is accurately discriminated, the correct sense of the ambiguous term is identified through the corresponding topic and semantic contexts. We firstly extract topic discriminative terms from document and construct topical graph based on topic span intervals to implement topic identification. We then exploit syntactic features, topic span features, and semantic features to disambiguate nouns and verbs in the context of ambiguous word. Finally, we conduct experiments on the standard data set SemCor to evaluate the performance of the proposed method, and the results indicate that our approach achieves relatively better performance than existing approaches.

scholarly journals An Approach to Classify Chronic Kidney Diseases using Scintigraphy Images

2019 ◽  
Author(s):  
Pedro Pedrosa Rebouças Filho ◽  
Suane Pires Pinheiro Da Silva ◽  
Jefferson Silva Almeida ◽  
Elene Firmeza Ohata ◽  
Shara Shami Araujo Alves ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Kidney Diseases ◽  
Extraction Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbors ◽  
Chronic Kidney Diseases ◽  
Occurrence Matrix

Chronic kidney diseases cause over a million deaths worldwide every year. One of the techniques used to diagnose the diseases is renal scintigraphy. However, the way that is processed can vary depending on hospitals and doctors, compromising the reproducibility of the method. In this context, we propose an approach to process the exam using computer vision and machine learning to classify the stage of chronic kidney disease. An analysis of different features extraction methods, such as Gray-Level Co-occurrence Matrix, Structural Co-occurrence Matrix, Local Binary Patters (LBP), Hu's Moments and Zernike's Moments in combination with machine learning methods, such as Bayes, Multi-layer Perceptron, k-Nearest Neighbors, Random Forest and Support Vector Machines (SVM), was performed. The best result was obtained by combining LBP feature extractor with SVM classifier. This combination achieved accuracy of 92.00% and F1-score of 91.00%, indicating that the proposed method is adequate to classify chronic kidney disease in two stages, being a high risk of developing end-stage renal failure and other outcomes, and otherwise.

scholarly journals Sociodemographic profile and stages of Parkinson’s disease associated with predictors of Temporomandibular Disorder

Revista CEFAC ◽  
2021 ◽  
Vol 23 (2) ◽  
Author(s):  
Jonatas Silva de Oliveira ◽  
Amanda do Vale Sobral ◽  
Taysa Vannoska de Almeida Silva ◽  
Maria das Graças Wanderley de Sales Coriolano ◽  
Carla Cabral dos Santos Accioly Lins
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Temporomandibular Disorder ◽  
Secondary Data ◽  
Significance Level ◽  
Predictive Variables ◽  
Disease Staging ◽  
Chi Squared ◽  
Sociodemographic Profile ◽  
Stage 1

ABSTRACT Purpose: to analyze the predictors of temporomandibular disorder in people with Parkinson’s disease, verifying their associations with sociodemographic aspects and stages of the disease. Methods: a study based on secondary data from research conducted in 2017 with 110 people with Parkinson’s disease. They were assessed with the Research Diagnostic Criteria for Temporomandibular Disorders and the Parkinson’s disease staging scale. The studied predictive variables for temporomandibular disorder were pain, crepitation, clicking, nighttime and daytime clenching/gnashing, uncomfortable/non-habitual bite, morning rigidity, and tinnitus. The sociodemographic aspects assessed were age, sex, schooling level, marital status, income, and stages 1 to 3 of the disease. The chi-squared odds ratio was used with a 95% confidence interval and significance level at p < 0.05. Results: an association was verified between nighttime clenching/gnashing and income (p = 0.006); tinnitus and income range from ½ to 3 (p = 0.003) and from 4 to 10 minimum wages (p = 0.004); and between tinnitus and stage 1 (p = 0.02). Conclusion: this study verified that the predictors associated with temporomandibular disorder in people with Parkinson’s disease were pain, clicking, crepitation, uncomfortable/non-habitual bite, and morning rigidity. It was verified that income and stage 1 of the disease had an association with nighttime clenching/gnashing and tinnitus.

scholarly journals Murottal Surah Ar-Rahman and Aromatheraphy Lavender (Lavendula Augustfolia) Inhalation to Decrease Labor Pain on First Stage Active Phase

2020 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
Nurul Azizah ◽  
Rafhani Rosyidah ◽  
Evi Destiana
Keyword(s):  
Pain Score ◽  
Design Method ◽  
Active Phase ◽  
Behavioral Observation ◽  
Pain Scale ◽  
Labor Pain ◽  
Control Group ◽  
P Value ◽  
Control Group Design ◽  
Significance Level

Childbirth is a natural process and causes pain, but many mothers can not resist the pain because it is influenced by stress. The study using non-phamacological pain relief therapy with aromatherapy which is believed to reduce pain and the aims to compare between murotal Al-Qur'an Surat Ar-rahman therapy and inhalation of lavender aromatherapy to reduce the intensity of labor pain when first active phase.The research design used Quasi Experimental with Non-equivalent Control Group Design method and using pretest - posttest. The population of the first phase active labor mothers in RB Nuril Masrukha Candi Sidoarjo. The technique sampling used Consecutive sampling. Data collected by observing 2 groups of labor mothers: 30 respondents listened to surah Ar-Rahman murottal and 30 respondents inhaled Lavender Aromatherapy. In both of groups, the pretest was given before treatment, then posttest was done after treatment using observation sheet assessment of pain scale with behavioral observation (FLACC behavioral scale). Data analysis using Independent Sample T-Test with a significance level α = 0.05. The results showed that the difference in pain score reduction in lavender aromatherapy inhalation was 3.26 ± 0.25, whereas in the murottal group of the Ar-Rahman Surah mean decrease in pain score was 2.62 ± 0.057 with P value <0.001, that showed a significant relationship.The conclusion is inhalation of aromatherapy lavender (Lavendula Augustfolia) and murottal surah Ar-Rahman can reduce intensity of labor pain during the first active phase, but inhalation group of aromatherapy lavender has a greater pain reduction score than murottal surah Ar-Rahman group.  

scholarly journals Evaluation of skin sensitivity in individuals presented with temporomandibular disorder

Revista CEFAC ◽  
2020 ◽  
Vol 22 (4) ◽  
Author(s):  
Giovanna Siqueira Faustino da Silva ◽  
Clarissa Evelyn Bandeira Paulino ◽  
Maurício Kosminsky ◽  
Luciana Moraes Studart-Pereira
Keyword(s):  
Temporomandibular Disorder ◽  
Signs And Symptoms ◽  
Age Group ◽  
Skin Sensitivity ◽  
Significance Level ◽  
Regional Block ◽  
The Face ◽  
Sensitivity Change ◽  
And Gender ◽  
Neuromuscular Electrostimulation

ABSTRACT Purpose: to identify the occurrence of a difference in skin sensitivity between analogous points on the face in individuals with temporomandibular disorder. Methods: a total of 60 individuals of both genders, aged 18 to 73 years, participated in the study. People classified with TMD signs and symptoms with the Fonseca Anamnestic Questionnaire were included. The skin sensitivity was evaluated with a Semmes-Weinstein esthesiometer. Sensitivity change was defined in this study as the occurrence of a difference between analogous points on both hemifaces. The localization of the points followed the regional block anatomical description and was confirmed with a neuromuscular electrostimulation device. The collected data were analyzed statistically with a 5% significance level. Results: the occurrence of a difference in sensitivity between the analogous points had a significant association with age group and severity of TMD. However, no association was perceived between sensitivity change and gender. Conclusion: in the population studied, the more severe the temporomandibular disorder, the greater the skin sensitivity change on the face. Also, the older the person, the higher the number of analogous points with a difference in sensitivity.

scholarly journals Is it possible myogenic temporomandibular dysfunctions change the facial thermal imaging?

2019 ◽  
Author(s):  
Denise Sabbagh Haddad ◽  
Beatriz Christine Oliveira ◽  
Marcos Leal Brioschi ◽  
Edgard Michel Crosato ◽  
Ricardo Vardasca ◽  
...  
Keyword(s):  
Infrared Thermography ◽  
Temporomandibular Disorder ◽  
Population Sample ◽  
European Population ◽  
Maximum Temperature ◽  
Asymptomatic Group ◽  
Average Temperature ◽  
Vasomotor Activity ◽  
Lower Maximum ◽  
Thermal Difference

Infrared thermography is a functional examination that can document physiological changes such as abnormal cutaneous vasomotor activity in inflammatory and neurogenic pictures related to nociceptive and neuropathic pain by mapping the thermal distribution on the surface of the skin. Objective: The aim of this study was to verify if there is a facial thermal difference between the symptomatic and asymptomatic group for myogenic TMD according to the Research Diagnostic Criteria for Temporomandibular Disorder (RDC/TMD) in a European population sample. Material and methods: Sixty-one subjects between 20 and 40 years (26.2 ± 7.6 years) of both sexes were divided into two groups. The 28 facial thermo-anatomic points were selected and the values of minimum (Tmin), mean (Tmed) and maximum (Tmax) temperatures, average of hemiface temperatures of whole sample, temperature difference (ΔT(°C)) between groups and from these data an algorithm was formulated to separate the groups with greater accuracy. Results: There was an average difference of 0.3 °C of all points when comparing the two groups. The symptomatic group had lower maximum temperature for frontal and lateral views when compared to the asymptomatic group (p<0.05), and presented lower average temperature in frontal view (p<0.05). Symptomatic individuals for myogenic TMD presented a reduction of facial cutaneous blood flow corresponding to lower maximum temperature by the proposed method of analysis of thermal anatomical points. Conclusions: Infrared thermography showed potential to be a screening and complementary diagnostic examination method for patients with myogenic temporomandibular disorders in the daily clinic just by frontal face image.

HEALTH OF THINGS MODEL FOR CLASSIFYING HUMAN HEART SOUND SIGNALS USING CO-OCCURRENCE MATRIX AND SPECTROGRAM

2020 ◽  
Vol 20 (06) ◽  
pp. 2050040
Author(s):  
VINAY ARORA ◽  
EDDIE YIN-KWEE NG ◽  
ROHAN SINGH LEEKHA ◽  
KARUN VERMA ◽  
TAKSHI GUPTA ◽  
...  
Keyword(s):  
Human Heart ◽  
Local Binary Patterns ◽  
Extraction Methods ◽  
Support Vector ◽  
Medical Specialists ◽  
Machine Learning Model ◽  
The Matrix ◽  
The Status ◽  
Occurrence Matrix ◽  
Pcg Signals

Cardiovascular diseases have become one of the world’s leading causes of death today. Several decision-making systems have been developed with computer-aided support to help the cardiologists in detecting heart disease and thereby minimizing the mortality rate. This paper uses an unexplored sub-domain related to textural features for classifying phonocardiogram (PCG) as normal or abnormal using Grey Level Co-occurrence Matrix (GLCM). The matrix has been applied to extract features from spectrogram of the PCG signals taken from the Physionet 2016 benchmark dataset. Random Forest, Support Vector Machine, Neural Network, and XGBoost have been applied to assess the status of the human heart using PCG signal spectrogram. The result of GLCM is compared with the two other textural feature extraction methods, viz. structural co-occurrence matrix (SCM), and local binary patterns (LBP). Experimental results have proved that applying machine learning model to classify PCG signal on the dataset where GLCM has extracted the feature-set, the accuracy attained is greater as compared to its peer approaches. Thus, this methodology can go a long way to help the medical specialists in precisely and accurately assessing the heart condition of a patient.

IncobotulinumtoxinA Injection for Temporomandibular Joint Disorder

2017 ◽  
Vol 126 (4) ◽  
pp. 328-333 ◽  
Author(s):  
Amit A. Patel ◽  
Michael Z. Lerner ◽  
Andrew Blitzer
Keyword(s):  
Temporomandibular Joint ◽  
Temporomandibular Disorder ◽  
Masticatory Muscles ◽  
Pain Scale ◽  
Pain Medication ◽  
Saline Injection ◽  
Pain Scores ◽  
Patient Reported ◽  
Joint Disorder ◽  
Muscles Of Mastication

Objectives: Temporomandibular disorder (TMD) involves dysfunction of the temporomandibular joint and associated muscles of mastication causing pain with chewing, limitation of jaw movement, and pain. While the exact pathophysiology of TMD is not completely understood, it is thought that hyperfunction of the muscles of mastication places stress on the temporomandibular joint, leading to degeneration of the joint and associated symptoms. We hypothesize that chemodenervation of the muscles of mastication with IncobotulinumtoxinA (Xeomin) will decrease the stress on the temporomandibular joint and improve pain associated with temporomandibular joint and muscle disorder (TMJD). Methods: Twenty patients were randomized to IncobotulinumtoxinA (170 units) or saline injection of the masticatory muscles. Patient-reported pain scale (0-10) was recorded at 4-week intervals following injection for 16 weeks. Patients who received saline injection initially were assessed for reduction in pain at the first 4-week interval and if still had significant pain were rolled over into the IncobotulinumtoxinA arm. Results: Preinjection pain scores were similar between patients. While there was a statistically significant reduction in pain score in the placebo group one month, there was an overall larger drop in average pain scores in those patients injected with IncobotulinumtoxinA initially. All patients initially injected with placebo crossed over into the IncobotulinumtoxinA group. Similar results were seen when examining the composite masticatory muscle tenderness scores. There was no significant change in usage of pain medication. Conclusions: We demonstrate utility of IncobotulinumtoxinA in treating patients with TMD with pain despite pain medication usage and other conventional treatments.

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