Development of artificial neural network model for prediction of post-streptococcus mutans in dental caries

Javed, Syed; Zakirulla, M; Baig, Rahmath Ulla; Asif, Shaik Mohammed; Meer, Allah Baksh

doi:10.1016/j.cmpb.2019.105198

Cited by 45 publications

(37 citation statements)

References 55 publications

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“…However, both proximal and periapical radiographs (Ac = 0.80, Sn = 0.75, Sp = 0.83) [ 31 , 34 ] as well as histologically (Ac = 0.98, Pr = 0.98) trained models [ 2 , 34 ] were able to reliably detect caries as a source for pain. While the aforementioned is considerably more efficient than clinicians (Ac = 0.71, Sn = 0.36, Sp = 0.91), dental specialists play an important role in training the machine from radiographs [ 3 ] or histological data [ 2 ]. Therefore, the prediction of the system may be directly dependent on the experience and agreement of the trainers.…”

Section: Discussionmentioning

confidence: 99%

“…Orofacial pain has multiple origins and varying intensities. The pain may arise from exposed dentin (hypersensitivity pain) [ 2 ] or from carious infection of the dental pulp (pulpitis) [ 3 ]. Untreated dental pulp encourages the infection to spread through the root canals into the periodontal tissue (apical periodontitis) [ 4 , 5 ] and may cause swelling, infection, and bone loss (periapical abscess) [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…Periodontal tissue can also be painfully infected without carious activity (gingivitis and periodontitis) [ 7 ]. Maxillofacial fractures [ 8 ], as well as iatrogenic trauma/infection during dental restorative/endodontic treatment [ 2 ], may elicit varying levels of pain. Bone diseases [ 9 ], temporomandibular joint disorders [ 10 ], space infections [ 11 ], salivary gland disorders [ 12 , 13 ], and sinusitis [ 14 ] elicit pain.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review

Farook

Jamayet

Abdullah

et al. 2021

Pain Research and Management

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Purpose. The study explored the clinical influence, effectiveness, limitations, and human comparison outcomes of machine learning in diagnosing (1) dental diseases, (2) periodontal diseases, (3) trauma and neuralgias, (4) cysts and tumors, (5) glandular disorders, and (6) bone and temporomandibular joint as possible causes of dental and orofacial pain. Method. Scopus, PubMed, and Web of Science (all databases) were searched by 2 reviewers until 29th October 2020. Articles were screened and narratively synthesized according to PRISMA-DTA guidelines based on predefined eligibility criteria. Articles that made direct reference test comparisons to human clinicians were evaluated using the MI-CLAIM checklist. The risk of bias was assessed by JBI-DTA critical appraisal, and certainty of the evidence was evaluated using the GRADE approach. Information regarding the quantification method of dental pain and disease, the conditional characteristics of both training and test data cohort in the machine learning, diagnostic outcomes, and diagnostic test comparisons with clinicians, where applicable, were extracted. Results. 34 eligible articles were found for data synthesis, of which 8 articles made direct reference comparisons to human clinicians. 7 papers scored over 13 (out of the evaluated 15 points) in the MI-CLAIM approach with all papers scoring 5+ (out of 7) in JBI-DTA appraisals. GRADE approach revealed serious risks of bias and inconsistencies with most studies containing more positive cases than their true prevalence in order to facilitate machine learning. Patient-perceived symptoms and clinical history were generally found to be less reliable than radiographs or histology for training accurate machine learning models. A low agreement level between clinicians training the models was suggested to have a negative impact on the prediction accuracy. Reference comparisons found nonspecialized clinicians with less than 3 years of experience to be disadvantaged against trained models. Conclusion. Machine learning in dental and orofacial healthcare has shown respectable results in diagnosing diseases with symptomatic pain and with improved future iterations and can be used as a diagnostic aid in the clinics. The current review did not internally analyze the machine learning models and their respective algorithms, nor consider the confounding variables and factors responsible for shaping the orofacial disorders responsible for eliciting pain.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review

Farook

Jamayet

Abdullah

et al. 2021

Pain Research and Management

View full text Add to dashboard Cite

show abstract

“…Besides MLP, the radial basis function (RBF) is another important type of neural network. A RBF is a three‐layered feed forward network, which applies a RBF as its hidden layer transfer function and the most commonly used RBF in the RBF networks are Gaussian activation functions (Javed, Zakirulla, Baig, Asif, & Meer, 2020).…”

Section: Introductionmentioning

confidence: 99%

A comparative study on the efficiency of two modeling approaches for predicting moisture content of apple slice during drying

Dalvi‐Isfahan

2020

J Food Process Engineering

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The efficiency of two different modeling approaches for predicting moisture content of apple slices during drying were evaluated and compared. The experiments were performed at four air‐drying temperatures and at three levels of air velocity in the convective hot air dryer. Moisture content of apple during drying was predicted using theoretical model and Artificial Neural Networks (ANNs) models. The theoretical model was developed by solving heat and mass transfer equations simultaneously using numerical technique. A multilayer perceptron (MLP) neural network and radial basis function (RBF) network were used to develop neural network modeling. The agreement between the experimental results and the theoretical model predictions was quite good by considering the shrinkage dependent effective diffusivity in the model. Among different ANN structures, the best results were obtained for the MLP network with two hidden layers based on the statistical criteria (the coefficient of determination [R2] and mean squared error [MSE]). Although both two approaches could reasonably forecast the moisture content of apple slice during drying, the ANN model demonstrated better goodness of fit than theoretical model. Practical application Moisture content and water activity are important parameters in production of dried fruit, which have traditionally been measured by destructive and time‐consuming methods. Hence, quick tools are required to evaluate the moisture concentration and monitor changes during drying. Modeling can be considered as a helpful tool for prediction of moisture content as a function of time and space. The results of this study show that the two types of models can successfully predict changes in moisture content during drying. Applying these models at the commercial level can reduce variability in product quality and improve repeatability of the drying process.

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“…The examiners were able to detect proximal caries with 71.7% and NN with 88.4% accuracy. Besides, the ANN model whose input fed with caries excavation methods and pre-streptococcus mutants for the detection of caries was introduced as an iOS application, entitled "Post-streptococcus mutans prediction (PSm)" (Javed et al, 2020). 2014), evaluated the wear factors on dental surfaces of 96 patients using an ANN.…”

Section: Restorative Dentistrymentioning

confidence: 99%

Artificial Intelligence Technologies in Dentistry

Albayrak¹,

Ozdemir²,

Us³

et al. 2021

J Exp Clin Med

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One of the most important actors in the digitization process of our age has been the applications of artificial intelligence (AI). While the weak and strong AI sub-concepts and the different AI models within them are being utilized in many fields such as education, industry and medicine today, the interest of the dentistry field, which has started its integration into the digital world with CAD/CAM technology, in AI is increasing day by day. In different branches of dentistry; AI provides services to clinicians and researchers in many fields such as disease diagnosis, evaluation of the occurrence or recurrence of diseases such as oral cancer, and prediction of success in surgical and prosthetic treatments. In this article, studies in which AI models such as machine learning, convolutional neural network have found research and usage areas on the basis of different branches of dentistry are reviewed.

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Development of artificial neural network model for prediction of post-streptococcus mutans in dental caries

Cited by 45 publications

References 55 publications

Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review

Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review

A comparative study on the efficiency of two modeling approaches for predicting moisture content of apple slice during drying

Artificial Intelligence Technologies in Dentistry

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