Clinical decision support systems in orthodontics: A narrative review of data science approaches

Digitization and validation of the open bite checklist manifesto: a step toward artificial intelligence

OBJECTIVES

To introduce and validate newly designed computer software to aid in the diagnosis of anterior open bite (AOB).

MATERIALS AND METHODS

The software was constructed based on the algorithm of a standardized open bite checklist, which considered skeletal, dental, and soft tissue components, as well as smile characteristics. Feeding the software with this input yielded a digital form output (DFO) in the guise of a diagnostic report characterizing the AOB phenotype, contributing components, severity, associated problems, and functional factors. For validation, DFO was compared to a conventional form output (CFO), created in a standardized manner according to expert opinions. Agreement between the DFO and CFO in terms of AOB phenotype was the primary outcome, while the secondary outcome was the number of missing diagnostic components in either method.

RESULTS

Percentage of agreement between CFO and DFO was 82.2%, with a kappa coefficient of 0.78, which is considered a good level of agreement. There was a statistically significant relationship between the number of missing diagnostic components in CFO and level of disagreement, which rendered the DFO more reliable.

CONCLUSIONS

Newly constructed software represents an efficient and valid diagnostic tool for AOB and its contributing components. There was good agreement between CFO and DFO, with the latter being more comprehensive and reliable. The algorithm built in the software can be used as the basis for a future artificial intelligence model to aid in the diagnosis of AOB.

Novel Machine Learning Algorithms for Prediction of Treatment Decisions in Adult Patients With Class III Malocclusion

BACKGROUND

Management of Class III (Cl III) dentoskeletal phenotype is often expert-driven.

PURPOSE

The aim is to identify critical morphological features in postcircumpubertal Cl III treatment and appraise the predictive ability of innovative machine learning (ML) algorithms for adult Cl III malocclusion treatment planning.

STUDY DESIGN

The Orthodontics Department at the University of Illinois Chicago undertook a retrospective cross-sectional study analyzing Cl III malocclusion cases (2003-2020) through dental records and pretreatment lateral cephalograms.

PREDICTOR

Forty features were identified through a literature review and gathered from pretreatment records, serving as ML model inputs. Eight ML models were trained to predict the best treatment for adult Cl III malocclusion.

OUTCOME VARIABLE

Predictive accuracy, sensitivity, and specificity of the models, along with the highest-contributing features, were evaluated for performance assessment.

COVARIATES

Demographic covariates, including age, gender, race, and ethnicity, were assessed. Inclusion criteria targeted patients with cervical vertebral maturation stage 4 or above. Operative covariates such as tooth extraction and types of orthognathic surgical maneuvers were also analyzed.

ANALYSES

Demographic characteristics of the camouflage and surgical study groups were described statistically. Shapiro-Wilk Normality test was employed to check data distribution. Differences in means between groups were evaluated using parametric and nonparametric independent sample tests, with statistical significance set at <0.05.

RESULTS

The study involved 182 participants; 65 underwent camouflage mechanotherapy, and 117 received orthognathic surgery. No statistical differences were found in demographic characteristics between the two groups (P > .05). Extreme values of pretreatment parameters suggested a surgical approach. Artificial neural network algorithms predicted treatment approach with 91% accuracy, while the Extreme Gradient Boosting model achieved 93% accuracy after recursive feature elimination optimization. The Extreme Gradient Boosting model highlighted Wit's appraisal, anterior overjet, and Mx/Md ratio as key predictors.

CONCLUSIONS

The research identified significant cephalometric differences between Cl III adults requiring orthodontic camouflage or surgery. A 93% accurate artificial intelligence model was formulated based on these insights, highlighting the potential role of artificial intelligence and ML as adjunct tools in orthodontic diagnosis and treatment planning. This may assist in minimizing clinician subjectivity in borderline cases.

Osteoarthritis Diagnosis Integrating Whole Joint Radiomics and Clinical Features for Robust Learning Models Using Biological Privileged Information

This paper proposes a machine learning model using privileged information (LUPI) and normalized mutual information feature selection method (NMIFS) to build a robust and accurate framework to diagnose patients with Temporomandibular Joint Osteoarthritis (TMJ OA). To build such a model, we employ clinical, quantitative imaging and additional biological markers as privileged information. We show that clinical features play a leading role in the TMJ OA diagnosis and quantitative imaging features, extracted from cone-beam computerized tomography (CBCT) scans, improve the model performance. As the proposed LUPI model employs biological data in the training phase (which boosted the model performance), this data is unnecessary for the testing stage, indicating the model can be widely used even when only clinical and imaging data are collected. The model was validated using 5-fold stratified cross-validation with hyperparameter tuning to avoid the bias of data splitting. Our method achieved an AUC, specificity and precision of 0.81, 0.79 and 0.77, respectively.

Interpretable artificial intelligence for classification of alveolar bone defect in patients with cleft lip and palate

Cleft lip and/or palate (CLP) is the most common congenital craniofacial anomaly and requires bone grafting of the alveolar cleft. This study aimed to develop a novel classification algorithm to assess the severity of alveolar bone defects in patients with CLP using three-dimensional (3D) surface models and to demonstrate through an interpretable artificial intelligence (AI)-based algorithm the decisions provided by the classifier. Cone-beam computed tomography scans of 194 patients with CLP were used to train and test the performance of an automatic classification of the severity of alveolar bone defect. The shape, height, and width of the alveolar bone defect were assessed in automatically segmented maxillary 3D surface models to determine the ground truth classification index of its severity. The novel classifier algorithm renders the 3D surface models from different viewpoints and captures 2D image snapshots fed into a 2D Convolutional Neural Network. An interpretable AI algorithm was developed that uses features from each view and aggregated via Attention Layers to explain the classification. The precision, recall and F-1 score were 0.823, 0.816, and 0.817, respectively, with agreement ranging from 97.4 to 100% on the severity index within 1 group difference. The new classifier and interpretable AI algorithm presented satisfactory accuracy to classify the severity of alveolar bone defect morphology using 3D surface models of patients with CLP and graphically displaying the features that were considered during the deep learning model's classification decision.

A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases

Today, with rapid advances in technology, computer-based studies and Artificial Intelligence (AI) approaches are finding their place in every field, especially in the medical sector, where they attract great attention. The Temporomandibular Joint (TMJ) stands as the most intricate joint within the human body, and diseases related to this joint are quite common. In this paper, we reviewed studies that utilize AI-based algorithms and computer-aided programs for investigating TMJ and TMJ-related diseases. We conducted a literature search on Google Scholar, Web of Science, and PubMed without any time constraints and exclusively selected English articles. Moreover, we examined the references to papers directly related to the topic matter. As a consequence of the survey, a total of 66 articles within the defined scope were assessed. These selected papers were distributed across various areas, with 11 focusing on segmentation, 3 on Juvenile Idiopathic Arthritis (JIA), 10 on TMJ Osteoarthritis (OA), 21 on Temporomandibular Joint Disorders (TMD), 6 on decision support systems, 10 reviews, and 5 on sound studies. The observed trend indicates a growing interest in artificial intelligence algorithms, suggesting that the number of studies in this field will likely continue to expand in the future.

Artificial Intelligence Systems Assisting in the Assessment of the Course and Retention of Orthodontic Treatment

This scoping review examines the contemporary applications of advanced artificial intelligence (AI) software in orthodontics, focusing on its potential to improve daily working protocols, but also highlighting its limitations. The aim of the review was to evaluate the accuracy and efficiency of current AI-based systems compared to conventional methods in diagnosing, assessing the progress of patients' treatment and follow-up stability. The researchers used various online databases and identified diagnostic software and dental monitoring software as the most studied software in contemporary orthodontics. The former can accurately identify anatomical landmarks used for cephalometric analysis, while the latter enables orthodontists to thoroughly monitor each patient, determine specific desired outcomes, track progress, and warn of potential changes in pre-existing pathology. However, there is limited evidence to assess the stability of treatment outcomes and relapse detection. The study concludes that AI is an effective tool for managing orthodontic treatment from diagnosis to retention, benefiting both patients and clinicians. Patients find the software easy to use and feel better cared for, while clinicians can make diagnoses more easily and assess compliance and damage to braces or aligners more quickly and frequently.

Decoding Deep Learning applications for diagnosis and treatment planning

INTRODUCTION

Artificial Intelligence (AI), Machine Learning and Deep Learning are playing an increasingly significant role in the medical field in the 21st century. These recent technologies are based on the concept of creating machines that have the potential to function as a human brain. It necessitates the gathering of large quantity of data to be processed. Once processed with AI machines, these data have the potential to streamline and improve the capabilities of the medical field in diagnosis and treatment planning, as well as in the prediction and recognition of diseases. These concepts are new to Orthodontics and are currently limited to image processing and pattern recognition.

OBJECTIVE

This article exposes and describes the different methods by which orthodontics may benefit from a more widespread adoption of these technologies.

Machine Learning Predictive Model as Clinical Decision Support System in Orthodontic Treatment Planning

Diagnosis and treatment planning forms the crux of orthodontics, which orthodontists gain with years of expertise. Machine Learning (ML), having the ability to learn by pattern recognition, can gain this expertise in a very short duration, ensuring reduced error, inter-intra clinician variability and good accuracy. Thus, the aim of this study was to construct an ML predictive model to predict a broader outline of the orthodontic diagnosis and treatment plan. The sample consisted of 700 case records of orthodontically treated patients in the past ten years. The data were split into a training and a test set. There were 33 input variables and 11 output variables. Four ML predictive model layers with seven algorithms were created. The test set was used to check the efficacy of the ML-predicted treatment plan and compared with that of the decision made by the expert orthodontists. The model showed an overall average accuracy of 84%, with the Decision Tree, Random Forest and XGB classifier algorithms showing the highest accuracy ranging from 87-93%. Yet in their infancy stages, Machine Learning models could become a valuable Clinical Decision Support System in orthodontic diagnosis and treatment planning in the future.

Three-dimensional digital applications for implant space planning in orthodontics: A narrative review

In the digital dentistry era, new tools, algorithms, data science approaches, and computer applications are available to researchers and clinicians. However, there is also a strong need for better knowledge and understanding of multisource data applications, including three-dimensional imaging information such as cone-beam computed tomography images and digital dental models for multidisciplinary cases. In addition, artificial intelligence models and automated clinical decision systems are rising. The clinician needs to plan the treatment based on state-of-the-art diagnosis for better and more personalized treatment. This article aimed to review basic concepts and the current panorama of digital implant planning in orthodontics, with open-source and closed-source tools for assessing cone-beam computed images and digital dental models. The visualization and processing of the three-dimensional data allow better implant planning based on bone conditions, adjacent teeth and root positions, and the prognosis of the case. We showed that many tools for assessment, segmentation, and visualization of cone-beam computed tomographic images and digital dental models could facilitate the treatment planning of patients needing implants or space closure. The tools and approaches presented are toward personalized treatment and better prognosis, following the path to a more automated clinical decision system based on multisource three-dimensional data, artificial intelligence models, and digital planning. In summary, the orthodontist needs to analyze each patient individually and use different software or tools that better fit their practice, allowing efficient treatment planning and satisfactory results with an adequate prognosis.

Accuracy of artificial intelligence for tooth extraction decision-making in orthodontics: a systematic review and meta-analysis

OBJECTIVE

This study aimed to analyze the accuracy of artificial intelligence (AI) for orthodontic tooth extraction decision-making.

MATERIALS AND METHODS

PubMed/MEDLINE, EMBASE, LILACS, Web of Science, Scopus, LIVIVO, Computers & Applied Science, ACM Digital Library, Compendex, and gray literature (OpenGrey, ProQuest, and Google Scholar) were electronically searched. Three independent reviewers selected the studies and extracted and analyzed the data. Risk of bias, methodological quality, and certainty of evidence were assessed by QUADAS-2, checklist for AI research, and GRADE, respectively.

RESULTS

The search identified 1810 studies. After 2 phases of selection, six studies were included, showing an unclear risk of bias of patient selection. Two studies showed a high risk of bias in the index test, while two others presented an unclear risk of bias in the diagnostic test. Data were pooled in a random model and yielded an accuracy value of 0.87 (95% CI = 0.75-0.96) for all studies, 0.89 (95% CI = 0.70-1.00) for multilayer perceptron, and 0.88 (95% CI = 0.73-0.98) for back propagation models. Sensitivity, specificity, and area under the curve of the multilayer perceptron model yielded 0.84 (95% CI = 0.58-1.00), 0.89 (95% CI = 0.74-0.98), and 0.92 (95% CI = 0.72-1.00) scores, respectively. Sagittal discrepancy, upper crowding, and protrusion showed the highest ranks weighted in the models.

CONCLUSIONS

Orthodontic tooth extraction decision-making using AI presented promising accuracy but should be considered with caution due to the very low certainty of evidence.

CLINICAL RELEVANCE

AI models for tooth extraction decision in orthodontics cannot yet be considered a substitute for a final human decision.

The Forefront of Dentistry-Promising Tech-Innovations and New Treatments

KNOWLEDGE TRANSFER STATEMENT

This article discusses innovations in technology and treatments that have enormous potential to revolutionize our dental care, including novel concepts in electronic health records, communication between dentists and patients, biologics around diagnosis and treatment, digital dentistry, and, finally, the real-time optimization of information technology. The early implementation and validation of these innovations can drive down their costs and provide better dental and medical services to all members of our society.

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 &gt;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.

Complexity and data mining in dental research: A network medicine perspective on interceptive orthodontics

Procedures and models of computerized data analysis are becoming researchers' and practitioners' thinking partners by transforming the reasoning underlying biomedicine. Complexity theory, Network analysis and Artificial Intelligence are already approaching this discipline, intending to provide support for patient's diagnosis, prognosis and treatments. At the same time, due to the sparsity, noisiness and time-dependency of medical data, such procedures are raising many unprecedented problems related to the mismatch between the human mind's reasoning and the outputs of computational models. Thanks to these computational, non-anthropocentric models, a patient's clinical situation can be elucidated in the orthodontic discipline, and the growth outcome can be approximated. However, to have confidence in these procedures, orthodontists should be warned of the related benefits and risks. Here we want to present how these innovative approaches can derive better patients' characterization, also offering a different point of view about patient's classification, prognosis and treatment.