Performance of a neural network trained to make third-molar treatment-planning decisions

Applications of artificial intelligence for surgical extraction in stomatology: a systematic review

OBJECTIVES

Artificial intelligence (AI) has been extensively used in the field of stomatology over the past several years. This study aimed to evaluate the effectiveness of AI-based models in the procedure, assessment, and treatment planning of surgical extraction.

STUDY DESIGN

Following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, a comprehensive search was conducted on the Web of Science, PubMed/MEDLINE, Embase, and Scopus databases, covering English publications up to September 2023. Two reviewers performed the study selection and data extraction independently. Only original research studies utilizing AI in surgical extraction of stomatology were included. The Cochrane risk of bias tool for randomized trials (RoB 2) was selected to perform the quality assessment of the selected literature.

RESULTS

From 2,336 retrieved references, 35 studies were deemed eligible. Among them, 28 researchers reported the pioneering role of AI in segmentation, classification, and detection, aligning with clinical needs. In addition, another 7 studies suggested promising results in tooth extraction decision-making, but further model refinement and validation were required.

CONCLUSIONS

Integration of AI in stomatology surgical extraction has significantly progressed, enhancing decision-making accuracy. Combining and comparing algorithmic outcomes across studies is essential for determining optimal clinical applications in the future.

Early Predicting Tribocorrosion Rate of Dental Implant Titanium Materials Using Random Forest Machine Learning Models

Early detection and prediction of bio-tribocorrosion can avert unexpected damage that may lead to secondary revision surgery and associated risks of implantable devices. Therefore, this study sought to develop a state-of-the-art prediction technique leveraging machine learning(ML) models to classify and predict the possibility of mechanical degradation in dental implant materials. Key features considered in the study involving pure titanium and titanium-zirconium (zirconium = 5, 10, and 15 in wt%) alloys include corrosion potential, acoustic emission(AE) absolute energy, hardness, and weight-loss estimates. ML prototype models deployed confirms its suitability in tribocorrosion prediction with an accuracy above 90%. Proposed system can evolve as a continuous structural-health monitoring as well as a reliable predictive modeling technique for dental implant monitoring.

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.

An Application of Machine Learning Techniques to Analyze Patient Information to Improve Oral Health Outcomes

Objective

Various health-related fields have applied Machine learning (ML) techniques such as text mining, topic modeling (TM), and artificial neural networks (ANN) to automate tasks otherwise completed by humans to enhance patient care. However, research in dentistry on the integration of these techniques into the clinic arena has yet to exist. Thus, the purpose of this study was to: introduce a method of automating the reviewing patient chart information using ML, provide a step-by-step description of how it was conducted, and demonstrate this method's potential to identify predictive relationships between patient chart information and important oral health-related contributors.

Methods

A secondary data analysis was conducted to demonstrate the approach on a set of anonymized patient charts collected from a dental clinic. Two ML applications for patient chart review were demonstrated: (1) text mining and Latent Dirichlet Allocation (LDA) were used to preprocess, model, and cluster data in a narrative format and extract common topics for further analysis, (2) Ordinal logistic regression (OLR) and ANN were used to determine predictive relationships between the extracted patient chart data topics and oral health-related contributors. All analysis was conducted in R and SPSS (IBM, SPSS, statistics 22).

Results

Data from 785 patient charts were analyzed. Preprocessing of raw data (data cleaning and categorizing) identified 66 variables, of which 45 were included for analysis. Using LDA, 10 radiographic findings topics and 8 treatment planning topics were extracted from the data. OLR showed that caries risk, occlusal risk, biomechanical risk, gingival recession, periodontitis, gingivitis, assisted mouth opening, and muscle tenderness were highly predictable using the extracted radiographic and treatment planning topics and chart information. Using the statistically significant predictors obtained from OLR, ANN analysis showed that the model can correctly predict >72% of all variables except for bruxism and tooth crowding (63.1 and 68.9%, respectively).

Conclusion

Our study presents a novel approach to address the need for data-enabled innovations in the field of dentistry and creates new areas of research in dental analytics. Utilizing ML methods and its application in dental practice has the potential to improve clinicians' and patients' understanding of the major factors that contribute to oral health diseases/conditions.

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

BACKGROUND AND OBJECTIVE

Streptococcus mutans is the primary initiator and most common organism associated with dental caries. Prediction of post-Streptococcus mutans favours in the selection of appropriate caries excavation method which eventually results in meliorate caries-free cavity preparation for restoration. The objective of this study is to predict the post-Streptococcus mutans prior to dental caries excavation based on pre- Streptococcus mutans using iOS App developed on Artificial Neural Network (ANN) model.

METHODS

For the current research work, children with occlusal dentinal caries lesion were chosen, 45 primary molar teeth cases were studied. Caries excavation was done with carbide bur, polymer bur and spoon excavator. The colony forming units for pre and post-Streptococcus mutans were recorded, data emanating from clinical trials was employed to develop the ANN models. ANN models were trained, validated and tested with the registered clinical data using different ANN architectures.

RESULTS

Feedforward backpropagation ANN model with an architecture of 4-5-1, predicts post-Streptococcus mutans with an efficiency of 0.99033, mean squared error and mean absolute percentage error for testing cases were 0.2341 and 4.967 respectively.

CONCLUSIONS

Caries excavation methods and pre-Streptococcus mutans are feed as inputs, while post-Streptococcus mutans as targets to develop ANN model. Based on the developed ANN model, an ingenious iOS App was developed, the global clinician may utilize the App to meticulously predict post-Streptococcus mutans on iPhone based on pre-Streptococcus mutans, which in turn aids in decision making for the selection of caries excavation method. This study manifests the potential application of iOS App with built-in ANN model in efficiently predicting the post-Streptococcus mutans. Also, the study extends scope for applications of iOS App with built-in ANN models in clinical medicine.

Dental Data Mining: Potential Pitfalls and Practical Issues

Knowledge Discovery and Data Mining (KDD) have become popular buzzwords. But what exactly is data mining? What are its strengths and limitations? Classic regression, artificial neural network (ANN), and classification and regression tree (CART) models are common KDD tools. Some recent reports ( e.g., Kattan et al., 1998 ) show that ANN and CART models can perform better than classic regression models: CART models excel at covariate interactions, while ANN models excel at nonlinear covariates. Model prediction performance is examined with the use of validation procedures and evaluating concordance, sensitivity, specificity, and likelihood ratio. To aid interpretation, various plots of predicted probabilities are utilized, such as lift charts, receiver operating characteristic curves, and cumulative captured-response plots. A dental caries study is used as an illustrative example. This paper compares the performance of logistic regression with KDD methods of CART and ANN in analyzing data from the Rochester caries study. With careful analysis, such as validation with sufficient sample size and the use of proper competitors, problems of naïve KDD analyses ( Schwarzer et al., 2000 ) can be carefully avoided.

Assessment of observers' stability and reliability - a tool for evaluation of intra- and inter-concordance in animal behavioral recordings

Behavior studies on the neurobiological effects of environmental, pharmacological and physiological manipulations in lab animals try to correlate these procedures with specific changes in animal behavior. Parameters such as duration, latency and frequency are assessed from the visually recorded sequences of behaviors, to distinguish changes due to manipulation. Since behavioral recording procedure is intrinsically interpretative, high variability in experimental results is expected and usual, due to observer-related influences such as experience, knowledge, stress, fatigue and personal biases. Here, we present a computer program that supports the assessment of inter- and intra-observer concordance, using statistical indices (e.g., Kappa and Kendal coefficients and concordance index). The software was tested in a case study with 4 different observers, naïve to behavioral recording procedures. On paired analysis, the higher agreement index achieved was 0.76 (concordance index) and 0.47 (Kappa Coefficient, where 0 is no agreement and 1 is total agreement). Observers showed poor concordance indices (lower than 0.7), emphasizing the concern on observer recording stability and on precise morphological definition of the recorded behaviors. These indices can also be used to train observers and to refine the behavioral catalogue definitions, as they are related to different behavioral recording aspects.

An artificial multilayer perceptron neural network for diagnosis of proximal dental caries

OBJECTIVE

To evaluate if the application of an artificial intelligence model, a multilayer perceptron neural network, improves the radiographic diagnosis of proximal caries.

STUDY DESIGN

One hundred sixty radiographic images of proximal surfaces of extracted human teeth were assessed regarding the presence of caries by 25 examiners. Examination of the radiographs was used to feed the neural network, and the corresponding teeth were sectioned and assessed under optical microscope (gold standard). This gold standard served to teach the neural network to diagnose caries on the basis of the radiographic exams. To gauge the network's capacity for generalization, i.e., its performance with new cases, data were divided into 3 subgroups for training, test, and cross-validation. The area under the receiver operating characteristic (ROC) curve allowed comparison of efficacy between network and examiner diagnosis.

RESULTS

For the best of the 25 examiners, the ROC curve area was 0.717, whereas network diagnosis achieved an ROC curve area of 0.884, indicating a sizeable improvement in proximal caries diagnosis.

CONCLUSION

Considering all examiners, the diagnostic improvement using the neural network was 39.4%.

A controlled trial of three referral methods for patients with third molars

AIM

To evaluate the accuracy, sensitivity and specificity of three primary to secondary care referral strategies.

METHOD

Thirty two primary care dental practitioners (GDPs) were randomly allocated one of three referral strategies: current practice (control strategy); a neural network embedded within a computer program and a paper-based clinical algorithm. One hundred and seven patients were assessed for lower third molar treatment: 47, 30 and 30 in each group, respectively. Clinical details were assessed by a panel of experts against a gold standard for third molar removal (the National Institutes of Health criteria). The accuracy, sensitivity, specificity, positive and negative predictive values were calculated for each strategy.

RESULTS

The referral decisions made by the GDPs in the control group displayed greater accuracy and sensitivity but poorer specificity (0.83; 0.97; 0.22) compared with the neural network (0.67; 0.56; 0.79) and clinical algorithm (0.73; 0.56; 0.93).

CONCLUSIONS

It was concluded that incorporation of the clinical algorithm into primary care was the most appropriate option. The computer neural network performed less well than either current practice or the clinical algorithm.

A comparison of human and machine-based predictions of successful weaning from mechanical ventilation

PURPOSE

To evaluate the ability of an appropriately trained neural network to correctly interpret a set of weaning parameters to predict the liberation of a patient from mechanical ventilation, and to contrast these predictions with those of human experts restricted to the same limited set of physiologic data.

METHODS

For each set of weaning parameters, a prediction was made by multiple realizations of a neural network and six expert volunteers.

RESULTS

The percentage of correct predictions made by the neural network when the decision threshold was set to 0.5 (range 0-1) was 83.3 +/- 4.2 (mean +/- SD) and that for the experts was 83.3 +/- 4.7. Predictions by the network when the threshold was 0.5 had a sensitivity of 0.83 and a specificity of 0.84, compared with 0.90 and 0.77, respectively, for the experts. However, sensitivity and specificity comparable to those of the human experts could be obtained by adjusting the decision threshold of the network predictor so that only the most clearly ventilator-dependent patients would not be given a trial of extubation.

CONCLUSION

When both are restricted to the same limited set of patient data, appropriately trained neural networks can be as effective as human experts in predicting whether weaning from mechanical ventilation will be successful.

Third molar management: a case against routine removal in adolescent and young adult orthodontic patients

In the nearly two decades since the National Institutes of Health conference, controversy and uncertainty have continued with respect to the diagnosis and treatment of impacted, nondiseased third molars in adolescents and young adults. Articles published over the past 10 years have studied the issue from the vantage point of risk management. Those who favor prophylactic removal justify this action on three premises: 1. All impacted third molars are potentially pathologic; therefore, prophylactic removal reduces or eliminates risk of future disease. 2. The presence of third molars can cause late crowding. 3. Removal during adolescence and young adulthood reduces risks of operative and postoperative complications compared with older patients. Those who favor conservative management offer three counter arguments: 1. Although impacted third molars do pose a risk of a pathologic condition, the risk is relatively small in comparison with the risks of operative and postoperative complications and the costs of unnecessary removal. 2. Although some investigators have shown a statistical association of third molars and late anterior crowding, the association is not strong enough to allow prediction of patients at risk. This is due principally to the high degree of individual variability, suggesting that many other factors interact in the development of postadolescent crowding. 3. Although studies have shown that morbidity is reduced when impacted, nondiseased third molars are removed during adolescence or young adulthood, the cost-risk-benefit data do not justify routine removal. Proponents of prophylactic removal argue that the benefits outweigh the risks. Proponents of conservative management argue that the scientific evidence is inconclusive in support of prophylactic removal. Unfortunately, much of the clinical research has been flawed. This has led to contradictory interpretations that have not fully clarified the relative risks and benefits of early intervention. Untrustworthy data have served only to fuel the debate and controversy concerning proper protocols. However, careful analyses of the published research show that routine removal of impacted or unerupted, disease-free third molars cannot be justified. A case-by-case management protocol that requires monitoring development represents the consensus of most researchers in this field.

Neural networks: a new technique for development of decision support systems in dentistry

OBJECTIVES

To outline the key concepts of neural network based systems and to evaluate the potential applications of such systems in dentistry.

DATA SOURCES

Published work on neural networks.

CONCLUSIONS

Neural networks may initially seem complex and computer intensive, but actually integrate well with a clinical environment. Neural network expert systems may be trained with only clinical data and as such can be used where 'rule based' decision making is not possible. This is the case in many clinical situations. Neural networks may therefore become important decision making tools within dentistry and have applications both in improving clinical care and in maximizing the cost benefit of care.