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Ali IE, Tanikawa C, Chikai M, Ino S, Sumita Y, Wakabayashi N. Applications and performance of artificial intelligence models in removable prosthodontics: A literature review. J Prosthodont Res 2024; 68:358-367. [PMID: 37793819 DOI: 10.2186/jpr.jpr_d_23_00073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023]
Abstract
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.
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Affiliation(s)
- Islam E Ali
- Department of Advanced Prosthodontics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
- Department of Prosthodontics, Faculty of Dentistry, Mansoura University, Mansoura, Egypt
| | - Chihiro Tanikawa
- Department of Orthodontics and Dentofacial Orthopedics, Graduate School of Dentistry, Osaka University, Suita, Japan
| | - Manabu Chikai
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
| | - Shuichi Ino
- Department of Mechanical Engineering, Graduate School of Engineering, Osaka University, Suita, Japan
| | - Yuka Sumita
- Department of Partial and Complete Denture, School of Life Dentistry at Tokyo, The Nippon Dental University, Tokyo, Japan
- Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Noriyuki Wakabayashi
- Department of Advanced Prosthodontics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
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Maj C, Eberts A, Schumacher J, Dasmeh P. Single-cell analysis reveals the spatial-temporal expression of genes associated with esophageal malformations. Sci Rep 2024; 14:3752. [PMID: 38355689 PMCID: PMC10866870 DOI: 10.1038/s41598-024-53098-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 01/27/2024] [Indexed: 02/16/2024] Open
Abstract
Understanding the molecular mechanisms of congenital diseases is challenging due to their occurrence within specific developmental stages. Esophageal malformations are examples of such conditions, characterized by abnormalities in the development of esophagus during embryogenesis. These developmental malformations encompass a range of anomalies, including esophageal atresia, and tracheoesophageal fistula. Here, we investigated the preferential expression of 29 genes that are implicated in such malformations and their immediate interactome (a total of 67 genes). We conducted our analyses across several single-cell atlases of embryonic development, encompassing approximately 150,000 cells from the mouse foregut, 180,000 cells from human embryos, and 500,000 cells from 24 human organs. Our study, spanning diverse mesodermal and endodermal cell populations and early developmental stages, shows that the genes associated with esophageal malformations show their highest cell-type specific expression in lateral plate mesoderm cells and at the developmental stage of E8.75-E9.0 days. In human embryos, these genes show a significant cell-type specific expression among subpopulations of epithelial cells, fibroblasts and progenitor cells including basal cells. Notably, members of the forkhead-box family of transcription factors, namely FOXF1, FOXC1, and FOXD1, as well as the SRY-box transcription factor, SOX2, demonstrate the most significant preferential expression in both mouse and human embryos. Overall, our findings provide insights into the temporal and cellular contexts contributing to esophageal malformations.
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Affiliation(s)
- Carlo Maj
- Center for Human Genetics, Marburg University and Marburg University Hospital, Marburg, Germany.
| | - Antonia Eberts
- Center for Human Genetics, Marburg University and Marburg University Hospital, Marburg, Germany
| | - Johannes Schumacher
- Center for Human Genetics, Marburg University and Marburg University Hospital, Marburg, Germany.
| | - Pouria Dasmeh
- Center for Human Genetics, Marburg University and Marburg University Hospital, Marburg, Germany.
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, USA.
- Institute for Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland.
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Lagoumintzis G, Patrinos GP. Triangulating nutrigenomics, metabolomics and microbiomics toward personalized nutrition and healthy living. Hum Genomics 2023; 17:109. [PMID: 38062537 PMCID: PMC10704648 DOI: 10.1186/s40246-023-00561-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/02/2023] [Indexed: 12/18/2023] Open
Abstract
The unique physiological and genetic characteristics of individuals influence their reactions to different dietary constituents and nutrients. This notion is the foundation of personalized nutrition. The field of nutrigenetics has witnessed significant progress in understanding the impact of genetic variants on macronutrient and micronutrient levels and the individual's responsiveness to dietary intake. These variants hold significant value in facilitating the development of personalized nutritional interventions, thereby enabling the effective translation from conventional dietary guidelines to genome-guided nutrition. Nevertheless, certain obstacles could impede the extensive implementation of individualized nutrition, which is still in its infancy, such as the polygenic nature of nutrition-related pathologies. Consequently, many disorders are susceptible to the collective influence of multiple genes and environmental interplay, wherein each gene exerts a moderate to modest effect. Furthermore, it is widely accepted that diseases emerge because of the intricate interplay between genetic predisposition and external environmental influences. In the context of this specific paradigm, the utilization of advanced "omic" technologies, including epigenomics, transcriptomics, proteomics, metabolomics, and microbiome analysis, in conjunction with comprehensive phenotyping, has the potential to unveil hitherto undisclosed hereditary elements and interactions between genes and the environment. This review aims to provide up-to-date information regarding the fundamentals of personalized nutrition, specifically emphasizing the complex triangulation interplay among microbiota, dietary metabolites, and genes. Furthermore, it highlights the intestinal microbiota's unique makeup, its influence on nutrigenomics, and the tailoring of dietary suggestions. Finally, this article provides an overview of genotyping versus microbiomics, focusing on investigating the potential applications of this knowledge in the context of tailored dietary plans that aim to improve human well-being and overall health.
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Affiliation(s)
- George Lagoumintzis
- Division of Pharmacology and Biosciences, Department of Pharmacy, School of Health Sciences, University of Patras, 26504, Patras, Greece.
| | - George P Patrinos
- Division of Pharmacology and Biosciences, Department of Pharmacy, School of Health Sciences, University of Patras, 26504, Patras, Greece.
- Department of Genetics and Genomics, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, Abu Dhabi, UAE.
- Zayed Center for Health Sciences, United Arab Emirates University, Al-Ain, Abu Dhabi, UAE.
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Parham MJ, Simpson AE, Moreno TA, Maricevich RS. Updates in Cleft Care. Semin Plast Surg 2023; 37:240-252. [PMID: 38098682 PMCID: PMC10718659 DOI: 10.1055/s-0043-1776733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
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.
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Affiliation(s)
- Matthew J. Parham
- Division of Plastic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas
- Division of Plastic Surgery, Texas Children's Hospital, Houston, Texas
| | - Arren E. Simpson
- Division of Plastic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas
- Division of Plastic Surgery, Texas Children's Hospital, Houston, Texas
| | - Tanir A. Moreno
- Division of Plastic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas
- Division of Plastic Surgery, Texas Children's Hospital, Houston, Texas
| | - Renata S. Maricevich
- Division of Plastic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas
- Division of Plastic Surgery, Texas Children's Hospital, Houston, Texas
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5
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Yin C, Yan B. Machine learning in basic scientific research on oral diseases. DIGITAL MEDICINE 2023; 9. [DOI: 10.1097/dm-2023-00001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Kang G, Baek SH, Kim YH, Kim DH, Park JW. Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models. Int J Mol Sci 2023; 24:ijms24054557. [PMID: 36901988 PMCID: PMC10003462 DOI: 10.3390/ijms24054557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 02/13/2023] [Accepted: 02/20/2023] [Indexed: 03/02/2023] Open
Abstract
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.
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Affiliation(s)
- Geon Kang
- Department of Medical Genetics, College of Medicine, Hallym University, Chuncheon 24252, Republic of Korea
| | - Seung-Hak Baek
- Department of Orthodontics, School of Dentistry, Seoul National University, Seoul 03080, Republic of Korea
| | - Young Ho Kim
- Department of Orthodontics, The Institute of Oral Health Science, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul 06351, Republic of Korea
| | - Dong-Hyun Kim
- Department of Social and Preventive Medicine, College of Medicine, Hallym University, Chuncheon 24252, Republic of Korea
| | - Ji Wan Park
- Department of Medical Genetics, College of Medicine, Hallym University, Chuncheon 24252, Republic of Korea
- Correspondence:
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Fitriasari S, Trainor PA. Gene-environment interactions in the pathogenesis of common craniofacial anomalies. Curr Top Dev Biol 2022; 152:139-168. [PMID: 36707210 DOI: 10.1016/bs.ctdb.2022.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Craniofacial anomalies often exhibit phenotype variability and non-mendelian inheritance due to their multifactorial origin, involving both genetic and environmental factors. A combination of epidemiologic studies, genome-wide association, and analysis of animal models have provided insight into the effects of gene-environment interactions on craniofacial and brain development and the pathogenesis of congenital disorders. In this chapter, we briefly summarize the etiology and pathogenesis of common craniofacial anomalies, focusing on orofacial clefts, hemifacial microsomia, and microcephaly. We then discuss how environmental risk factors interact with genes to modulate the incidence and phenotype severity of craniofacial anomalies. Identifying environmental risk factors and dissecting their interaction with different genes and modifiers is central to improved strategies for preventing craniofacial anomalies.
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Affiliation(s)
| | - Paul A Trainor
- Stowers Institute for Medical Research, Kansas City, MO, United States; Department of Anatomy and Cell Biology, University of Kansas Medical Center, Kansas City, KS, United States.
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Abstract
This chapter reviews the evidence of gene×environment interactions (G×E) in the etiology of orofacial cleft birth defects (OFCs), specifically cleft lip (CL), cleft palate (CP), and cleft lip with or without cleft palate (CL/P). We summarize the current state of our understanding of the genetic architecture of nonsyndromic OFCs and the evidence that maternal exposures during pregnancy influence risk of OFCs. Further, we present possible candidate gene pathways for these exposures including metabolism of folates, metabolism of retinoids, retinoic acid receptor signaling, aryl hydrocarbon receptor signaling, glucocorticoid receptor signaling, and biotransformation and transport. We review genes in these pathways with prior evidence of association with OFCs, genes with evidence from prior candidate gene G×E studies, and genes identified from genome-wide searches specifically for identifying G×E. Finally, we suggest future directions for G×E research in OFCs.
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Affiliation(s)
- Mary L Marazita
- Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, United States; Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States; Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States; Clinical and Translational Science Institute, Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
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9
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Wang F, Zheng J, Cheng J, Zou H, Li M, Deng B, Luo R, Wang F, Huang D, Li G, Zhang R, Ding X, Li Y, Du J, Yang Y, Kan J. Personalized nutrition: A review of genotype-based nutritional supplementation. Front Nutr 2022; 9:992986. [PMID: 36159456 PMCID: PMC9500586 DOI: 10.3389/fnut.2022.992986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 08/17/2022] [Indexed: 11/13/2022] Open
Abstract
Nutritional disorders have become a major public health issue, requiring increased targeted approaches. Personalized nutrition adapted to individual needs has garnered dramatic attention as an effective way to improve nutritional balance and maintain health. With the rapidly evolving fields of genomics and nutrigenetics, accumulation of genetic variants has been indicated to alter the effects of nutritional supplementation, suggesting its indispensable role in the genotype-based personalized nutrition. Additionally, the metabolism of nutrients, such as lipids, especially omega-3 polyunsaturated fatty acids, glucose, vitamin A, folic acid, vitamin D, iron, and calcium could be effectively improved with related genetic variants. This review focuses on existing literatures linking critical genetic variants to the nutrient and the ways in which these variants influence the outcomes of certain nutritional supplementations. Although further studies are required in this direction, such evidence provides valuable insights for the guidance of appropriate interventions using genetic information, thus paving the way for the smooth transition of conventional generic approach to genotype-based personalized nutrition.
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Affiliation(s)
| | | | - Junrui Cheng
- Department of Molecular and Structural Biochemistry, North Carolina State University, Kannapolis, NC, United States
| | - Hong Zou
- Sequanta Technologies Co., Ltd, Shanghai, China
| | | | - Bin Deng
- Nutrilite Health Institute, Guangzhou, China
| | - Rong Luo
- Nutrilite Health Institute, Guangzhou, China
| | - Feng Wang
- Nutrilite Health Institute, Guangzhou, China
| | | | - Gang Li
- Nutrilite Health Institute, Shanghai, China
| | - Rao Zhang
- School of Public Health, Institute of Nutrition and Health, Qingdao University, Qingdao, China
| | - Xin Ding
- School of Public Health, Institute of Nutrition and Health, Qingdao University, Qingdao, China
| | - Yuan Li
- Sequanta Technologies Co., Ltd, Shanghai, China
| | - Jun Du
- Nutrilite Health Institute, Shanghai, China
- Jun Du
| | - Yuexin Yang
- Chinese Center for Disease Control and Prevention, National Institute for Nutrition and Health, Beijing, China
- Yuexin Yang
| | - Juntao Kan
- Nutrilite Health Institute, Shanghai, China
- *Correspondence: Juntao Kan
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Huqh MZU, Abdullah JY, Wong LS, Jamayet NB, Alam MK, Rashid QF, Husein A, Ahmad WMAW, Eusufzai SZ, Prasadh S, Subramaniyan V, Fuloria NK, Fuloria S, Sekar M, Selvaraj S. Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate-A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191710860. [PMID: 36078576 PMCID: PMC9518587 DOI: 10.3390/ijerph191710860] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 08/22/2022] [Indexed: 05/03/2023]
Abstract
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.
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Affiliation(s)
- Mohamed Zahoor Ul Huqh
- Orthodontic Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia
| | - Johari Yap Abdullah
- Craniofacial Imaging Lab, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia
- Correspondence: (J.Y.A.); (L.S.W.); (S.S.)
| | - Ling Shing Wong
- Faculty of Health and Life Sciences, INTI International University, Nilai 71800, Malaysia
- Correspondence: (J.Y.A.); (L.S.W.); (S.S.)
| | - Nafij Bin Jamayet
- Division of Clinical Dentistry (Prosthodontics), School of Dentistry, International Medical University, Bukit Jalil, Kuala Lumpur 57000, Malaysia
| | - Mohammad Khursheed Alam
- Orthodontic Division, Preventive Dentistry Department, College of Dentistry, Jouf University, Sakaka 72345, Saudi Arabia
| | - Qazi Farah Rashid
- Prosthodontic Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia
| | - Adam Husein
- Prosthodontic Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia
| | - Wan Muhamad Amir W. Ahmad
- Department of Biostatistics, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia
| | - Sumaiya Zabin Eusufzai
- Department of Biostatistics, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia
| | - Somasundaram Prasadh
- National Dental Center Singapore, 5 Second Hospital Avenue, Singapore 168938, Singapore
| | | | | | | | - Mahendran Sekar
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy and Health Sciences, Royal College of Medicine Perak, Universiti Kuala Lumpur, Ipoh 30450, Malaysia
| | - Siddharthan Selvaraj
- Faculty of Dentistry, AIMST University, Bedong 08100, Malaysia
- Correspondence: (J.Y.A.); (L.S.W.); (S.S.)
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Risk score prediction model based on single nucleotide polymorphism for predicting malaria: a machine learning approach. BMC Bioinformatics 2022; 23:325. [PMID: 35934714 PMCID: PMC9358850 DOI: 10.1186/s12859-022-04870-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 08/01/2022] [Indexed: 11/25/2022] Open
Abstract
Background The malaria risk prediction is currently limited to using advanced statistical methods, such as time series and cluster analysis on epidemiological data. Nevertheless, machine learning models have been explored to study the complexity of malaria through blood smear images and environmental data. However, to the best of our knowledge, no study analyses the contribution of Single Nucleotide Polymorphisms (SNPs) to malaria using a machine learning model. More specifically, this study aims to quantify an individual's susceptibility to the development of malaria by using risk scores obtained from the cumulative effects of SNPs, known as weighted genetic risk scores (wGRS).
Results We proposed an SNP-based feature extraction algorithm that incorporates the susceptibility information of an individual to malaria to generate the feature set. However, it can become computationally expensive for a machine learning model to learn from many SNPs. Therefore, we reduced the feature set by employing the Logistic Regression and Recursive Feature Elimination (LR-RFE) method to select SNPs that improve the efficacy of our model. Next, we calculated the wGRS of the selected feature set, which is used as the model's target variables. Moreover, to compare the performance of the wGRS-only model, we calculated and evaluated the combination of wGRS with genotype frequency (wGRS + GF). Finally, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and Ridge regression algorithms are utilized to establish the machine learning models for malaria risk prediction. Conclusions Our proposed approach identified SNP rs334 as the most contributing feature with an importance score of 6.224 compared to the baseline, with an importance score of 1.1314. This is an important result as prior studies have proven that rs334 is a major genetic risk factor for malaria. The analysis and comparison of the three machine learning models demonstrated that LightGBM achieves the highest model performance with a Mean Absolute Error (MAE) score of 0.0373. Furthermore, based on wGRS + GF, all models performed significantly better than wGRS alone, in which LightGBM obtained the best performance (0.0033 MAE score). Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04870-0.
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Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis. Healthcare (Basel) 2022; 10:healthcare10071269. [PMID: 35885796 PMCID: PMC9320442 DOI: 10.3390/healthcare10071269] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/25/2022] [Accepted: 06/30/2022] [Indexed: 12/29/2022] Open
Abstract
This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.
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Marya A, Venugopal A, Karobari MI, Chaudhari PK, Heboyan A, Rokaya D. The Contemporary Management of Cleft Lip and Palate and the Role of Artificial Intelligence: A Review. Open Dent J 2022. [DOI: 10.2174/18742106-v16-e2202240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Introduction:
Cleft management is an exhaustive process for the patient, the orthodontist, and the caregiver. In recent decades, a wide number of challenges have been addressed with the inclusion of various dental specialties for the detection, diagnosis, and treatment of orofacial clefts. The orthodontist plays a very pivotal role during the overall management of children with cleft lip and palate as they need to make critical decisions for when to intervene orthodontically and at what stage to set priorities for individual treatment goals.
Objectives:
The objectives of this study were to provide an in-depth review of the evolving role of various disciplines focusing on orthodontics in the management of cleft cases.
Methods:
A general search was carried out to identify the published data on cleft lip and cleft palate management on PubMed and Scopus until the 1st of June 2021 using keywords such as cleft lip, cleft palate, cleft orthodontics, naso-alveolar molding, and surgical cleft orthodontics. The related literature was then reviewed and analyzed.
Results:
With improvements in 3D modeling, CT scans of patients can be used to construct precise 3D models, and these can be utilized to demonstrate various clinical issues related to clefts. The orthodontist has a major role in the various stages and steps, follow-up, treatment care, and outcome assessment. With the advent of technological advancements and artificial intelligence, the role is only going to evolve and expand further in the management of the cleft lip and palate. Diagnostic techniques utilizing artificial intelligence to detect cleft during the prenatal period have also been tested and have been shown to have a high rate of accuracy. The evolution of distraction osteogenesis came into the limelight as a revolutionary modality for cleft treatment. Computer-assisted orthognathic surgery is a widely used modality for reshaping the osseous defects of the maxilla in patients with congenital clefts. With the development of additional modalities such as aligners, patients that need to undergo complex orthognathic surgeries can also be treated with aligners without compromising the outcomes.
Conclusion:
The cleft lip and palate can be managed by a multi-disciplinary team. Orthodontics has an important role in the overall management of a cleft affected individual as they must make critical decisions regarding orthodontic interventions as well as set priorities for each treatment goal. With the advent of technological advancements and artificial intelligence, the diagnosis and management of the cleft lip and palate have become simplified.
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14
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Kondody RT, Patil A, Devika G, Jose A, Kumar A, Nair S. Introduction to artificial intelligence and machine learning into orthodontics: A review. APOS TRENDS IN ORTHODONTICS 2022. [DOI: 10.25259/apos_60_2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Over the past few years, artificial intelligence (AI) and machine learning (ML) have revolutionized different healthcare branches, including dentistry. AI in a wider aspect means computers that mimic or behave like human intelligence whereas ML forms a part of AI and enables machines to increase their capabilities by the process of self-adapting algorithms. AI models’ basic principles or fundamentals are purely based on artificial neural networks or convolutional neural networks. This review focuses on giving a comprehensive and detailed explanation about AI and ML technology and their wide range of applications in various sections of orthodontic practice.
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Affiliation(s)
- Rony T. Kondody
- Department of Orthodontics, Sri Rajiv Gandhi College of Dental Science and Hospital, Bengaluru, India,
| | - Aishwarya Patil
- Department of Oral Pathology and microbiology, HKES’s S. Nijalingappa Dental College and Hospital, Gulbarga, India,
| | - G. Devika
- Department of Periodontics, Oxford Dental College and Hospital, Bengaluru, India,
| | - Angeline Jose
- Department of Conservative Dentistry and Endodontics, ESIC Govt. Dental College and Hospital, Gulbarga, Karnataka, India,
| | - Ashwath Kumar
- Department of Conservative Dentistry and Endodontics, ESIC Govt. Dental College and Hospital, Gulbarga, Karnataka, India,
| | - Saumya Nair
- Department of Conservative Dentistry and Endodontics, Annoor Dental College and Hospital, Muvattupuzha, Kerala, India,
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15
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Linkage and association of PAX7 polymorphisms (rs742071, rs766325, and rs4920520) with the risk of non-syndromic cleft lip with/without cleft palate: A systematic review and meta-analysis. Meta Gene 2022. [DOI: 10.1016/j.mgene.2021.101007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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16
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Dhillon H, Chaudhari PK, Dhingra K, Kuo RF, Sokhi RK, Alam MK, Ahmad S. Current Applications of Artificial Intelligence in Cleft Care: A Scoping Review. Front Med (Lausanne) 2021; 8:676490. [PMID: 34395471 PMCID: PMC8355556 DOI: 10.3389/fmed.2021.676490] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 06/30/2021] [Indexed: 01/30/2023] Open
Abstract
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.
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Affiliation(s)
- Harnoor Dhillon
- Centre for Dental Education and Research, All India Institute of Medical Sciences, New Delhi, India
| | - Prabhat Kumar Chaudhari
- Centre for Dental Education and Research, All India Institute of Medical Sciences, New Delhi, India
| | - Kunaal Dhingra
- Centre for Dental Education and Research, All India Institute of Medical Sciences, New Delhi, India
| | - Rong-Fu Kuo
- Medical Device Innovation Centre, National Cheng Kung University, Tainan, Taiwan
| | - Ramandeep Kaur Sokhi
- Centre for Dental Education and Research, All India Institute of Medical Sciences, New Delhi, India
| | | | - Shandar Ahmad
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
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17
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Bichu YM, Hansa I, Bichu AY, Premjani P, Flores-Mir C, Vaid NR. Applications of artificial intelligence and machine learning in orthodontics: a scoping review. Prog Orthod 2021; 22:18. [PMID: 34219198 PMCID: PMC8255249 DOI: 10.1186/s40510-021-00361-9] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/12/2021] [Indexed: 12/15/2022] Open
Abstract
Introduction This scoping review aims to provide an overview of the existing evidence on the use of artificial intelligence (AI) and machine learning (ML) in orthodontics, its translation into clinical practice, and what limitations do exist that have precluded their envisioned application. Methods A scoping review of the literature was carried out following the PRISMA-ScR guidelines. PubMed was searched until July 2020. Results Sixty-two articles fulfilled the inclusion criteria. A total of 43 out of the 62 studies (69.35%) were published this last decade. The majority of these studies were from the USA (11), followed by South Korea (9) and China (7). The number of studies published in non-orthodontic journals (36) was more extensive than in orthodontic journals (26). Artificial Neural Networks (ANNs) were found to be the most commonly utilized AI/ML algorithm (13 studies), followed by Convolutional Neural Networks (CNNs), Support Vector Machine (SVM) (9 studies each), and regression (8 studies). The most commonly studied domains were diagnosis and treatment planning—either broad-based or specific (33), automated anatomic landmark detection and/or analyses (19), assessment of growth and development (4), and evaluation of treatment outcomes (2). The different characteristics and distribution of these studies have been displayed and elucidated upon therein. Conclusion This scoping review suggests that there has been an exponential increase in the number of studies involving various orthodontic applications of AI and ML. The most commonly studied domains were diagnosis and treatment planning, automated anatomic landmark detection and/or analyses, and growth and development assessment. Supplementary Information The online version contains supplementary material available at 10.1186/s40510-021-00361-9.
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Affiliation(s)
| | | | | | | | - Carlos Flores-Mir
- Department of Orthodontics, University of Alberta, Edmonton, Alberta, Canada
| | - Nikhilesh R Vaid
- Department of Orthodontics, European University College, Dubai, United Arab Emirates
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18
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Neela PK, Gosla SR, Husain A, Mohan V, Thumoju S, Rajeshwari BV. Association of Nucleotide Variants of GRHL3, IRF6, NAT2, SDC2, BCL3, and PVRL1 Genes with Nonsyndromic Cleft Lip With/Without Cleft Palate in Multigenerational Families: A Retrospective Study. Contemp Clin Dent 2021; 12:138-142. [PMID: 34220153 PMCID: PMC8237814 DOI: 10.4103/ccd.ccd_329_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 05/21/2020] [Accepted: 07/03/2020] [Indexed: 11/05/2022] Open
Abstract
Background: Several genes are associated with the etiology of cleft lip and palate (CLP) in different populations. Many nucleotide variants on genes such as GRHL3, IRF6, NAT2, SDC2, BCL3, and PVRL1 were reported in different populations, but not studied in multigenerational cases in the Indian population. Aim and Objective: The aim of this study is to evaluate whether nucleotide variants rs41268753, rs861020, rs1041983, rs1042381, rs2965169, and rs10790332 are involved in the etiology of nonsyndromic CLP (NSCLP) in multigenerational Indian families. Study Design: Retrospective genetic study. Materials and Methods: Based on inclusion and exclusion criteria, 20 multigenerational families with nonsyndromic cleft lip with or without cleft palate (NSCL/P) were selected. Blood samples from both affected and unaffected participants were collected as a source of genomic DNA. Six nucleotide variants on these genes were genotyped to test for the association with NSCL/P. Genotyping was performed with the MassArray method. Genotype distribution was used to calculate the Hardy–Weinberg equilibrium using PLINK, a whole-genome association analysis toolset. The allelic association was compared among cases and controls using Chi-square test as implemented in PLINK. P ≤ 0.05 indicates statistical differences between groups. Results: No significant associations were found between individual single-nucleotide polymorphisms and NSCL/P. The odds ratio was 1.531, 1.198, 0.8082, 1.418, 1, and 0.5929 for polymorphisms rs41268753, rs861020, rs1041983, rs1042381, rs2965169, and rs10790332, respectively. Conclusion: Our findings suggest that among the multigenerational families in our population, the high-risk nucleotide variants GRHL3 rs41268753, IRF6 rs861020, NAT2 rs1041983, SDC2 rs1042381, BCL3 rs2965169, and PVRL1 rs10790332 are not associated with increased risk of NSCL/P.
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Affiliation(s)
- Praveen Kumar Neela
- GSR Institute of Craniomaxillofacial and Facial Plastic Surgery, Hyderabad, India.,Department of Orthodontics, Kamineni Institute of Dental Sciences, Nalgonda, Telangana, India
| | - Srinivas Reddy Gosla
- GSR Institute of Craniomaxillofacial and Facial Plastic Surgery, Hyderabad, India.,Department of Cranio Maxillofacial Surgery, AIIMS, Rishikesh, India
| | - Akhter Husain
- Department of Orthodontics, Yenepoya Dental College, Yenepoya University, Mangalore, India
| | - Vasavi Mohan
- Department of Genetics and Molecular Medicine, Vasavi Medical and Research Centre, Hyderabad, India
| | - Sravya Thumoju
- Department of Genetics and Molecular Medicine, Vasavi Medical and Research Centre, Hyderabad, India
| | - B V Rajeshwari
- Department of Genetics and Molecular Medicine, Vasavi Medical and Research Centre, Hyderabad, India.,Department of OBG, Surabhi Institute of Medical Sciences, Telangana, India
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19
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Mohammad-Rahimi H, Nadimi M, Rohban MH, Shamsoddin E, Lee VY, Motamedian SR. Machine learning and orthodontics, current trends and the future opportunities: A scoping review. Am J Orthod Dentofacial Orthop 2021; 160:170-192.e4. [PMID: 34103190 DOI: 10.1016/j.ajodo.2021.02.013] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 01/01/2021] [Accepted: 02/01/2021] [Indexed: 12/29/2022]
Abstract
INTRODUCTION In recent years, artificial intelligence (AI) has been applied in various ways in medicine and dentistry. Advancements in AI technology show promising results in the practice of orthodontics. This scoping review aimed to investigate the effectiveness of AI-based models employed in orthodontic landmark detection, diagnosis, and treatment planning. METHODS A precise search of electronic databases was conducted, including PubMed, Google Scholar, Scopus, and Embase (English publications from January 2010 to July 2020). Quality Assessment and Diagnostic Accuracy Tool 2 (QUADAS-2) was used to assess the quality of the articles included in this review. RESULTS After applying inclusion and exclusion criteria, 49 articles were included in the final review. AI technology has achieved state-of-the-art results in various orthodontic applications, including automated landmark detection on lateral cephalograms and photography images, cervical vertebra maturation degree determination, skeletal classification, orthodontic tooth extraction decisions, predicting the need for orthodontic treatment or orthognathic surgery, and facial attractiveness. Most of the AI models used in these applications are based on artificial neural networks. CONCLUSIONS AI can help orthodontists save time and provide accuracy comparable to the trained dentists in diagnostic assessments and prognostic predictions. These systems aim to boost performance and enhance the quality of care in orthodontics. However, based on current studies, the most promising application was cephalometry landmark detection, skeletal classification, and decision making on tooth extractions.
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Affiliation(s)
| | - Mohadeseh Nadimi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Erfan Shamsoddin
- National Institute for Medical Research Development, Tehran, Iran
| | | | - Saeed Reza Motamedian
- Department of Orthodontics, School of Dentistry, & Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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20
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Nasreddine G, El Hajj J, Ghassibe-Sabbagh M. Orofacial clefts embryology, classification, epidemiology, and genetics. MUTATION RESEARCH-REVIEWS IN MUTATION RESEARCH 2021; 787:108373. [PMID: 34083042 DOI: 10.1016/j.mrrev.2021.108373] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 02/21/2021] [Accepted: 02/23/2021] [Indexed: 01/14/2023]
Abstract
Orofacial clefts (OFCs) rank as the second most common congenital birth defect in the United States after Down syndrome and are the most common head and neck congenital malformations. They are classified as cleft lip with or without cleft palate (CL/P) and cleft palate only (CPO). OFCs have significant psychological and socio-economic impact on patients and their families and require a multidisciplinary approach for management and counseling. A complex interaction between genetic and environmental factors contributes to the incidence and clinical presentation of OFCs. In this comprehensive review, the embryology, classification, epidemiology and etiology of clefts are thoroughly discussed and a "state-of-the-art" snapshot of the recent advances in the genetics of OFCs is presented.
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Affiliation(s)
- Ghenwa Nasreddine
- Department of Natural Sciences, School of Arts and Sciences, Lebanese American University, P.O. Box: 13-5053, Chouran, 1102 2801, Beirut, Lebanon.
| | - Joelle El Hajj
- Department of Natural Sciences, School of Arts and Sciences, Lebanese American University, P.O. Box: 13-5053, Chouran, 1102 2801, Beirut, Lebanon.
| | - Michella Ghassibe-Sabbagh
- Department of Natural Sciences, School of Arts and Sciences, Lebanese American University, P.O. Box: 13-5053, Chouran, 1102 2801, Beirut, Lebanon.
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21
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Association of MTHFR 1298A > C Polymorphism with Susceptibility to Non-Syndromic Cleft Lip with or without Palate: A Case-Control Study and Meta-Analysis. Fetal Pediatr Pathol 2021; 40:1-17. [PMID: 31682771 DOI: 10.1080/15513815.2019.1683918] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Several studies have evaluated association of the 5,10-methylenetetrahydrofolate reductase (MTHFR) gene 1298A > C polymorphism with non-syndromic cleft lip with or without palate (NSCL ± P) susceptibility, however the results are inconsistent. MATERIALS AND METHODS To address this issue, we performed a case-control study to evaluate the association of MTHFR 1298A > C polymorphism with NSCL ± P risk, followed by a meta-analysis. RESULTS Including our study, a total of 22 case-control studies with 2,814 cases and 4,199 controls were selected. The results suggested that there was no significant association between MTHFR 1298A > C polymorphism and NSCL ± P risk overall. The subgroup analysis demonstrated that the polymorphism was significantly associated with NSCL ± P risk in Asians and Iranian populations, but not in Caucasians, mixed and Chinese populations. CONCLUSION This meta-analysis indicates that MTHFR 1298A > C polymorphism may not contribute to NSCL ± P risk in overall. However, the MTHFR 1298A > C polymorphism was significantly associated with an increased risk of NSCL ± P in Asians and Iranian populations.
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22
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Rostam Niakan Kalhori S, Tanhapour M, Gholamzadeh M. Enhanced childhood diseases treatment using computational models: Systematic review of intelligent experiments heading to precision medicine. J Biomed Inform 2021; 115:103687. [PMID: 33497811 DOI: 10.1016/j.jbi.2021.103687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 12/05/2020] [Accepted: 01/18/2021] [Indexed: 10/22/2022]
Abstract
INTRODUCTION Precision or personalized Medicine (PM) is used for the prevention and treatment of diseases by considering a huge amount of information about individuals variables. Due to high volume of information, AI-based computational models are required. A large set of studies conducted to examine the PM approach to improve childhood clinical outcomes. Thus, the main goal of this study was to review the application of health information technology and especially artificial intelligence (AI) methods for the treatment of childhood disease using PM. METHODS PubMed, Scopus, Web of Science, and EMBASE databases were searched up to December 18, 2019. Articles that focused on informatics applications for childhood disease PM included in this study. Included papers were classified for qualitative analysis and interpreting results. The results were analyzed using Microsoft Excel 2019. RESULTS From 341 citations, 62 papers met our inclusion criteria. The number of published papers that used AI methods to apply for PM in childhood diseases increased from 2010 to 2019. Our results showed that most applied methods were related to machine learning discipline. In terms of clinical scope, the largest number of clinical articles are devoted to oncology. Besides, the analysis showed that genomics was the most PM approach used regarding childhood disease. CONCLUSION This systematic review examined papers that used AI methods for applying PM approaches in childhood diseases from medical informatics perspectives. Thus, it provided new insight to researchers who are interested in knowing research needs in this field.
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Affiliation(s)
- Sharareh Rostam Niakan Kalhori
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Mozhgan Tanhapour
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Marsa Gholamzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
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23
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24
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Garland MA, Reynolds K, Zhou CJ. Environmental mechanisms of orofacial clefts. Birth Defects Res 2020; 112:1660-1698. [PMID: 33125192 PMCID: PMC7902093 DOI: 10.1002/bdr2.1830] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2020] [Revised: 10/07/2020] [Accepted: 10/13/2020] [Indexed: 12/11/2022]
Abstract
Orofacial clefts (OFCs) are among the most common birth defects and impart a significant burden on afflicted individuals and their families. It is increasingly understood that many nonsyndromic OFCs are a consequence of extrinsic factors, genetic susceptibilities, and interactions of the two. Therefore, understanding the environmental mechanisms of OFCs is important in the prevention of future cases. This review examines the molecular mechanisms associated with environmental factors that either protect against or increase the risk of OFCs. We focus on essential metabolic pathways, environmental signaling mechanisms, detoxification pathways, behavioral risk factors, and biological hazards that may disrupt orofacial development.
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Affiliation(s)
- Michael A. Garland
- Department of Biochemistry and Molecular Medicine, University of California at Davis, School of Medicine, Sacramento, CA 95817
- Institute for Pediatric Regenerative Medicine of Shriners Hospitals for Children, University of California at Davis, School of Medicine, Sacramento, CA 95817
| | - Kurt Reynolds
- Department of Biochemistry and Molecular Medicine, University of California at Davis, School of Medicine, Sacramento, CA 95817
- Institute for Pediatric Regenerative Medicine of Shriners Hospitals for Children, University of California at Davis, School of Medicine, Sacramento, CA 95817
- Biochemistry, Molecular, Cellular, and Developmental Biology (BMCDB) graduate group, University of California, Davis, CA 95616
| | - Chengji J. Zhou
- Department of Biochemistry and Molecular Medicine, University of California at Davis, School of Medicine, Sacramento, CA 95817
- Institute for Pediatric Regenerative Medicine of Shriners Hospitals for Children, University of California at Davis, School of Medicine, Sacramento, CA 95817
- Biochemistry, Molecular, Cellular, and Developmental Biology (BMCDB) graduate group, University of California, Davis, CA 95616
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25
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Abstract
Machine learning (ML) revolves around the concept of using experience to teach computer-based programs to reliably perform specific tasks. Healthcare setting is an ideal environment for adaptation of ML applications given the multiple specific tasks that could be allocated to computer programs to perform. There have been several scoping reviews published in literature looking at the general acceptance and adaptability of surgical specialities to ML applications, but very few focusing on the application towards craniofacial surgery. This study aims to present a detailed scoping review regarding the use of ML applications in craniofacial surgery.
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26
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Leite AF, Vasconcelos KDF, Willems H, Jacobs R. Radiomics and Machine Learning in Oral Healthcare. Proteomics Clin Appl 2020; 14:e1900040. [PMID: 31950592 DOI: 10.1002/prca.201900040] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 12/09/2019] [Indexed: 12/12/2022]
Abstract
The increasing storage of information, data, and forms of knowledge has led to the development of new technologies that can help to accomplish complex tasks in different areas, such as in dentistry. In this context, the role of computational methods, such as radiomics and Artificial Intelligence (AI) applications, has been progressing remarkably for dentomaxillofacial radiology (DMFR). These tools bring new perspectives for diagnosis, classification, and prediction of oral diseases, treatment planning, and for the evaluation and prediction of outcomes, minimizing the possibilities of human errors. A comprehensive review of the state-of-the-art of using radiomics and machine learning (ML) for imaging in oral healthcare is presented in this paper. Although the number of published studies is still relatively low, the preliminary results are very promising and in a near future, an augmented dentomaxillofacial radiology (ADMFR) will combine the use of radiomics-based and AI-based analyses with the radiologist's evaluation. In addition to the opportunities and possibilities, some challenges and limitations have also been discussed for further investigations.
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Affiliation(s)
- André Ferreira Leite
- Department of Dentistry, Faculty of Health Sciences, University of Brasília, Brasília, 70910-900, Brazil.,Omfsimpath Research Group, Department of Imaging and Pathology, Biomedical Sciences, KU Leuven and Dentomaxillofacial Imaging Department, University Hospitals Leuven, Leuven, 3000, Belgium
| | - Karla de Faria Vasconcelos
- Omfsimpath Research Group, Department of Imaging and Pathology, Biomedical Sciences, KU Leuven and Dentomaxillofacial Imaging Department, University Hospitals Leuven, Leuven, 3000, Belgium
| | - Holger Willems
- Relu, Innovatie-en incubatiecentrum KU Leuven, Leuven, 3000, Belgium
| | - Reinhilde Jacobs
- Omfsimpath Research Group, Department of Imaging and Pathology, Biomedical Sciences, KU Leuven and Dentomaxillofacial Imaging Department, University Hospitals Leuven, Leuven, 3000, Belgium.,Department of Dental Medicine, Karolinska Institutet, Huddinge, 17177, Sweden
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