Presenting a Model for Periodontal Disease Diagnosis Using Two Artificial Neural Network Algorithms

Arbabi, Samin; Jahantigh, Farzad Firouzi; Moghadam, Somaye Ansari

doi:10.5812/jhealthscope.65330

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…They divided 190 patients into two groups (n = 160 for train) and (n = 30 for test) and studied them by examining age, sex, plaque index, probing pocket depth, and clinical attachment loss index as an input variables. Two Levenberg-Marquardet (LM) and Scaled Conjugate Gradient (SCG) algorithms have been used in this work, results showed that a better performance for the LM than SCG [23].…”

Section: Discussionmentioning

confidence: 99%

“…In this cross-sectional study, the records of 300 patients referred to the Periodontics department of Hamadan University of Medical Sciences, west of Iran, between September 2016 and June 2018 were studied. Based on consultation with two experienced professionals of periodontology as well as a review of clinical studies, the desired variables were selected [14,15,21,23]. The corresponding values of the selected variables were extracted and recorded for each patient with full confidentiality principles.…”

Section: Methodsmentioning

confidence: 99%

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A decision support system based on support vector machine for diagnosis of periodontal disease

2020

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Objective: Early diagnosis of many diseases is essential for their treatment. Furthermore, the existence of abundant and unknown variables makes more complicated decision making. For this reason, the diagnosis and classification of diseases using machine learning algorithms have attracted a lot of attention. Therefore, this study aimed to design a support vector machine (SVM) based decision-making support system to diagnosis various periodontal diseases. Data were collected from 300 patients referring to Periodontics department of Hamadan University of Medical Sciences, west of Iran. Among these patients, 160 were Gingivitis, 60 were localized periodontitis and 80 were generalized periodontitis. In the designed classification model, 11 variables such as age, sex, smoking, gingival index, plaque index and so on used as input and output variable show the individual's status as a periodontal disease. Results: Using different kernel functions in the design of the SVM classification model showed that the radial kernel function with an overall correct classification accuracy of 88.7% and the overall hypervolume under the manifold (HUM) value was to 0.912 has the best performance. The results of the present study show that the designed classification model has an acceptable performance in predicting periodontitis.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A decision support system based on support vector machine for diagnosis of periodontal disease

2020

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“…Papantonopoulos et al [136] establish an Ann's modal to distinguish AgP from ChP based on monocytes, eosinophils, neutrophils, and CD4 / CD8 ratio. Arbabi et al [137] established a diagnosis tool based on ANNs, which can accurately diagnose periodontal disease by evaluating age, sex, probing depth, plaque index, and attachment loss index.…”

Section: ) Diagnose Periodontitismentioning

confidence: 99%

Application of Machine Learning to Stomatology: A Comprehensive Review

Sun

Liu

et al. 2020

IEEE Access

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In recent years, machine learning methods has been widely used in various fields, such as finance, spatial sciences, smart grid, intelligent transportation, renewable energy, agriculture, especially medicine. In the era of big medical data, the advantage of machine learning is that it can predict and diagnose through the analysis of a large number of clinical data, and its performance is very close and competitive to or even better than the performance of clinicians. This paper focuses on the application of machine learning techniques in the field of stomatology and detailedly describes application cases involving oral cancer, dental caries, periodontitis, dental pulp diseases, periapical lesions, oral implants, and orthodontics. Finally, the research obstacles and future work are discussed.

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“…LM method has achieved better performance than SCG. [27]The same methodology can be implemented in future with extended and added inputs apart from considered in this paper for more accurate results.…”

Section: Literature Surveymentioning

confidence: 99%

Digitalization in Dental problem diagnosis, Prediction and Analysis: A Machine Learning Perspective of Periodontitis

T.K¹,

Dheeba²

2020

IJRTE

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Artificial Intelligence, Machine learning, deep learning and image processing is becoming popular in medical sciences. The present digitalized world is remodelling each facetadditionally impacting dentistry and medical field from patient record maintenance, data analysisto new diagnostic methods, novel interference waysand totally different treatment choices. Oral health contributes to various diseases and conditions like Endocarditis, Cardio vascular diseases, diabetes, osteoporosis, pregnancy and birth and many more. Bad breathe, tooth decay, periodontitis, oral abscess, tooth erosion, dentinal sensitivity and many more can be even trickier to detect in plain dental radiography. The most prevalent disease periodontitis is a gum disease when left untreated, leads to tooth loss and more hazardous complications. Early Prediction and Proper diagnosis in time will protect our health from the mentioned diseases which can be implemented by making use of emerging technologies to assist and support dentists in predictions and decision making. Hence focusing more on oral health, In the current paper, the most contributing risk factors and parameters like Pocket Depth, Black Triangles, Alveolar Bone Loss, Furcation, Periodontal Abscess, Smoking, Gingivitis, Clinical Attachment Loss, Mobility Etc. that progresses the disease were taken in to consideration and a Python code was implemented which can be used as a Decision making aid to check whether person suffers or likely to suffer in future or not suffering from the disease.In this paper, literature reviews on the various automated computerized methods used to detect and diagnose the disease were discussed and an attempt was made to clearly identify and describe both the clinical and radiological parameters that a dentist/Periodontist use as a metric to grade/assess the periodontitis. The present strategy can be enhanced as a tool and can be used as a decision making aid by dentists’ in the prediction of periodontitis and can also be used for demonstrating fresher’s or upcoming dentists the progress of gum disease, grading the severity of the disease and the associated risk factors considering clinical, radiological findings and adverse habits thereby improving overall time period taken for manual predictions.

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Presenting a Model for Periodontal Disease Diagnosis Using Two Artificial Neural Network Algorithms

Abstract: Background: Artificial neural networks (ANNs) can be used in various medical cases due to their high performance in learning the relationship between variables. Periodontal diseases are common oral infectious diseases that can cause tooth loss, if not treated.

Cited by 4 publications

References 16 publications

A decision support system based on support vector machine for diagnosis of periodontal disease

A decision support system based on support vector machine for diagnosis of periodontal disease

Application of Machine Learning to Stomatology: A Comprehensive Review

Digitalization in Dental problem diagnosis, Prediction and Analysis: A Machine Learning Perspective of Periodontitis

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