Machine learning in prediction of genetic risk of nonsyndromic oral clefts in the Brazilian population

Applications and performance of artificial intelligence models in removable prosthodontics: A literature review

PURPOSE

In this narrative review, we present the current applications and performances of artificial intelligence (AI) models in different phases of the removable prosthodontic workflow and related research topics.

STUDY SELECTION

A literature search was conducted using PubMed, Scopus, Web of Science, and Google Scholar databases between January 2010 and January 2023. Search terms related to AI were combined with terms related to removable prosthodontics. Articles reporting the structure and performance of the developed AI model were selected for this literature review.

RESULTS

A total of 15 articles were relevant to the application of AI in removable prosthodontics, including maxillofacial prosthetics. These applications included the design of removable partial dentures, classification of partially edentulous arches, functional evaluation and outcome prediction in complete denture treatment, early prosthetic management of patients with cleft lip and palate, coloration of maxillofacial prostheses, and prediction of the material properties of denture teeth. Various AI models with reliable prediction accuracy have been developed using supervised learning.

CONCLUSIONS

The current applications of AI in removable prosthodontics exhibit significant potential for improving the prosthodontic workflow, with high accuracy levels reported in most of the reviewed studies. However, the focus has been predominantly on the diagnostic phase, with few studies addressing treatment planning and implementation. Because the number of AI-related studies in removable prosthodontics is limited, more models targeting different prosthodontic disciplines are required.

Updates in Cleft Care

Cleft lip and/or palate is a congenital malformation with a wide range of presentations, and its effective treatment necessitates sustained, comprehensive care across an affected child's life. Early diagnosis, ideally through prenatal imaging or immediately postbirth, is paramount. Access to longitudinal care and long-term follow-up with a multidisciplinary approach, led by the recommendations of the American Cleft Palate Association, is the best way to ensure optimal outcomes. Multiple specialties including plastic surgery, otolaryngology, speech therapy, orthodontists, psychologists, and audiologists all may be indicated in the care of the child. Primary repair of the lip, nose, and palate are generally conducted during infancy. Postoperative care demands meticulous oversight to detect potential complications. If necessary, revisional surgeries should be performed before the child begin primary school. As the child matures, secondary procedures like alveolar bone grafting and orthognathic surgery may be requisite. The landscape of cleft care has undergone significant transformation since early surgical correction, with treatment plans now tailored to the specific type and severity of the cleft. The purpose of this text is to outline the current standards of care in children born with cleft lip and/or palate and to highlight ongoing advancements in the field.

Development of Artificial Neural Network-Based Prediction Model for Evaluation of Maxillary Arch Growth in Children with Complete Unilateral Cleft Lip and Palate

INTRODUCTION

Cleft lip and palate (CLP) are the most common congenital craniofacial deformities that can cause a variety of dental abnormalities in children. The purpose of this study was to predict the maxillary arch growth and to develop a neural network logistic regression model for both UCLP and non-UCLP individuals.

METHODS

This study utilizes a novel method incorporating many approaches, such as the bootstrap method, a multi-layer feed-forward neural network, and ordinal logistic regression. A dataset was created based on the following factors: socio-demographic characteristics such as age and gender, as well as cleft type and category of malocclusion associated with the cleft. Training data were used to create a model, whereas testing data were used to validate it. The study is separated into two phases: phase one involves the use of a multilayer neural network and phase two involves the use of an ordinal logistic regression model to analyze the underlying association between cleft and the factors chosen.

RESULTS

The findings of the hybrid technique using ordinal logistic regression are discussed, where category acts as both a dependent variable and as the study's output. The ordinal logistic regression was used to classify the dependent variables into three categories. The suggested technique performs exceptionally well, as evidenced by a Predicted Mean Square Error (PMSE) of 2.03%.

CONCLUSION

The outcome of the study suggests that there is a strong association between gender, age, and cleft. The difference in width and length of the maxillary arch in UCLP is mainly related to the severity of the cleft and facial growth pattern.

Interactions of SNPs in Folate Metabolism Related Genes on Prostate Cancer Aggressiveness in European Americans and African Americans

Background: Studies showed that folate and related single nucleotide polymorphisms (SNPs) could predict prostate cancer (PCa) risk. However, little is known about the interactions of folate-related SNPs associated with PCa aggressiveness. The study’s objective is to evaluate SNP–SNP interactions among the DHFR 19-bp polymorphism and 10 SNPs in folate metabolism and the one-carbon metabolism pathway associated with PCa aggressiveness. Methods: We evaluated 1294 PCa patients, including 690 European Americans (EAs) and 604 African Americans (AAs). Both individual SNP effects and pairwise SNP–SNP interactions were analyzed. Results: None of the 11 individual polymorphisms were significant for EAs and AAs. Three SNP–SNP interaction pairs can predict PCa aggressiveness with a medium to large effect size. For the EA PCa patients, the interaction between rs1801133 (MTHFR) and rs2236225 (MTHFD1), and rs1801131 (MTHFR) and rs7587117 (SLC4A5) were significantly associated with aggressive PCa. For the AA PCa patients, the interaction of DHFR-19bp polymorphism and rs4652 (LGALS3) was significantly associated with aggressive PCa. Conclusions: These SNP–SNP interactions in the folate metabolism-related genes have a larger impact than SNP individual effects on tumor aggressiveness for EA and AA PCa patients. These findings can provide valuable information for potential biological mechanisms of PCa aggressiveness.

Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models

Recent deep learning algorithms have further improved risk classification capabilities. However, an appropriate feature selection method is required to overcome dimensionality issues in population-based genetic studies. In this Korean case-control study of nonsyndromic cleft lip with or without cleft palate (NSCL/P), we compared the predictive performance of models that were developed by using the genetic-algorithm-optimized neural networks ensemble (GANNE) technique with those models that were generated by eight conventional risk classification methods, including polygenic risk score (PRS), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and deep-learning-based artificial neural network (ANN). GANNE, which is capable of automatic input SNP selection, exhibited the highest predictive power, especially in the 10-SNP model (AUC of 88.2%), thus improving the AUC by 23% and 17% compared to PRS and ANN, respectively. Genes mapped with input SNPs that were selected by using a genetic algorithm (GA) were functionally validated for risks of developing NSCL/P in gene ontology and protein-protein interaction (PPI) network analyses. The IRF6 gene, which is most frequently selected via GA, was also a major hub gene in the PPI network. Genes such as RUNX2, MTHFR, PVRL1, TGFB3, and TBX22 significantly contributed to predicting NSCL/P risk. GANNE is an efficient disease risk classification method using a minimum optimal set of SNPs; however, further validation studies are needed to ensure the clinical utility of the model for predicting NSCL/P risk.

A Comprehensive Investigation on Potential Risk Factors for NSCL/P in a Rural District of Hebei Province, China

Non syndromic cleft lip with or without palate (NSCL/P), one of the most common birth defects, is closely related to various risk factors. However, information regarding risk factors for NSCL/P in rural districts in China is very limited thus far. The objective of this study was designed to identify the potential risk factors for NSCL/P in rural districts.

A comprehensive retrospective investigation including 435 NSCL/P patients and 402 healthy children was carried out in Hebei Province, China. Multiple logistic regression analysis and transmission disequilibrium test (TDT) were respectively used to identify non-genetic and genetic risk factors for NSCL/P, and then PLINK was used to explore the relationship between non-genetic and genetic risk factors.

The results showed that maternal periconceptional exposure to pesticides and herbicides, as well as low parental education level were involved in the increased risk of NSCL/P, whereas maternal folic acid and multivitamin supplementation use during preconception period were associated with the reduced risk of NSCL/P. TDT analysis identified 2 single nucleotide polymorphisms (SNPs) (rs7078160 and rs4752028) in VAX1 and one SNP (rs17563) in BMP4 as the genetic risk factors for NSCL/P. Further analysis showed that the genetic risk factors were closely related with the negative non-genetic risk factors.

Our study identified the potential risk factors for NSCL/P in rural districts, thus providing a theoretical basis for the prevention of NSCL/P occurrence.

Machine Learning in Dentistry: A Scoping Review

Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies.

Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate-A Systematic Review

OBJECTIVE

The objective of this systematic review was (a) to explore the current clinical applications of AI/ML (Artificial intelligence and Machine learning) techniques in diagnosis and treatment prediction in children with CLP (Cleft lip and palate), (b) to create a qualitative summary of results of the studies retrieved.

MATERIALS AND METHODS

An electronic search was carried out using databases such as PubMed, Scopus, and the Web of Science Core Collection. Two reviewers searched the databases separately and concurrently. The initial search was conducted on 6 July 2021. The publishing period was unrestricted; however, the search was limited to articles involving human participants and published in English. Combinations of Medical Subject Headings (MeSH) phrases and free text terms were used as search keywords in each database. The following data was taken from the methods and results sections of the selected papers: The amount of AI training datasets utilized to train the intelligent system, as well as their conditional properties; Unilateral CLP, Bilateral CLP, Unilateral Cleft lip and alveolus, Unilateral cleft lip, Hypernasality, Dental characteristics, and sagittal jaw relationship in children with CLP are among the problems studied.

RESULTS

Based on the predefined search strings with accompanying database keywords, a total of 44 articles were found in Scopus, PubMed, and Web of Science search results. After reading the full articles, 12 papers were included for systematic analysis.

CONCLUSIONS

Artificial intelligence provides an advanced technology that can be employed in AI-enabled computerized programming software for accurate landmark detection, rapid digital cephalometric analysis, clinical decision-making, and treatment prediction. In children with corrected unilateral cleft lip and palate, ML can help detect cephalometric predictors of future need for orthognathic surgery.

Applications of artificial intelligence in dentistry: A comprehensive review

OBJECTIVE

To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research.

MATERIALS AND METHODS

The comprehensive review was conducted in MEDLINE/PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years.

RESULTS

Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling.

CONCLUSIONS

The insight provided by the present work has reported outstanding results in the design of high-performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations.

CLINICAL SIGNIFICANCE

The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.

Current Applications of Artificial Intelligence in Cleft Care: A Scoping Review

Objective: This scoping review aims to identify the various areas and current status of the application of artificial intelligence (AI) for aiding individuals with cleft lip and/or palate.

Introduction: Cleft lip and/or palate contributes significantly toward the global burden on the healthcare system. Artificial intelligence is a technology that can help individuals with cleft lip and/or palate, especially those in areas with limited access to receive adequate care.

Inclusion Criteria: Studies that used artificial intelligence to aid the diagnosis, treatment, or its planning in individuals with cleft lip and/or palate were included.

Methodology: A search of the Pubmed, Embase, and IEEE Xplore databases was conducted using search terms artificial intelligence and cleft lip and/or palate. Gray literature was searched using Google Scholar. The study was conducted according to the PRISMA- ScR guidelines.

Results: The initial search identified 458 results, which were screened based on title and abstracts. After the screening, removal of duplicates, and a full-text reading of selected articles, 26 publications were included. They explored the use of AI in cleft lip and/or palate to aid in decisions regarding diagnosis, treatment, especially speech therapy, and prediction.

Conclusion: There is active interest and immense potential for the use of artificial intelligence in cleft lip and/or palate. Most studies currently focus on speech in cleft palate. Multi-center studies that include different populations, with collaboration amongst academicians and researchers, can further develop the technology.