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Kanchanapiboon P, Tunksook P, Tunksook P, Ritthipravat P, Boonpratham S, Satravaha Y, Chaweewannakorn C, Peanchitlertkajorn S. Classification of cervical vertebral maturation stages with machine learning models: leveraging datasets with high inter- and intra-observer agreement. Prog Orthod 2024; 25:35. [PMID: 39279025 PMCID: PMC11402886 DOI: 10.1186/s40510-024-00535-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 07/22/2024] [Indexed: 09/18/2024] Open
Abstract
OBJECTIVES This study aimed to assess the accuracy of machine learning (ML) models with feature selection technique in classifying cervical vertebral maturation stages (CVMS). Consensus-based datasets were used for models training and evaluation for their model generalization capabilities on unseen datasets. METHODS Three clinicians independently rated CVMS on 1380 lateral cephalograms, resulting in the creation of five datasets: two consensus-based datasets (Complete Agreement and Majority Voting), and three datasets based on a single rater's evaluations. Additionally, landmarks annotation of the second to fourth cervical vertebrae and patients' information underwent a feature selection process. These datasets were used to train various ML models and identify the top-performing model for each dataset. These models were subsequently tested on their generalization capabilities. RESULTS Features that considered significant in the consensus-based datasets were consistent with a CVMS guideline. The Support Vector Machine model on the Complete Agreement dataset achieved the highest accuracy (77.4%), followed by the Multi-Layer Perceptron model on the Majority Voting dataset (69.6%). Models from individual ratings showed lower accuracies (60.4-67.9%). The consensus-based training models also exhibited lower coefficient of variation (CV), indicating superior generalization capability compared to models from single raters. CONCLUSION ML models trained on consensus-based datasets for CVMS classification exhibited the highest accuracy, with significant features consistent with the original CVMS guidelines. These models also showed robust generalization capabilities, underscoring the importance of dataset quality.
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Affiliation(s)
- Potjanee Kanchanapiboon
- Division of Nuclear Medicine, Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, 2 Wang Lang Rd, Siriraj, Bangkok Noi, Bangkok, 10700, Thailand
| | - Pitipat Tunksook
- Department of Orthodontics, Faculty of Dentistry, Mahidol University, 6 Yothi Rd, Thung Phaya Thai, Ratchathewi, Bangkok, 10400, Thailand
| | | | - Panrasee Ritthipravat
- Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, 999 Phutthamonthon 4 Rd, Salaya, Nakhon Pathom, 73170, Thailand
| | - Supatchai Boonpratham
- Department of Orthodontics, Faculty of Dentistry, Mahidol University, 6 Yothi Rd, Thung Phaya Thai, Ratchathewi, Bangkok, 10400, Thailand
| | - Yodhathai Satravaha
- Department of Orthodontics, Faculty of Dentistry, Mahidol University, 6 Yothi Rd, Thung Phaya Thai, Ratchathewi, Bangkok, 10400, Thailand
| | - Chaiyapol Chaweewannakorn
- Department of Orthodontics, Faculty of Dentistry, Mahidol University, 6 Yothi Rd, Thung Phaya Thai, Ratchathewi, Bangkok, 10400, Thailand
| | - Supakit Peanchitlertkajorn
- Department of Orthodontics, Faculty of Dentistry, Mahidol University, 6 Yothi Rd, Thung Phaya Thai, Ratchathewi, Bangkok, 10400, Thailand.
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Kazimierczak W, Jedliński M, Issa J, Kazimierczak N, Janiszewska-Olszowska J, Dyszkiewicz-Konwińska M, Różyło-Kalinowska I, Serafin Z, Orhan K. Accuracy of Artificial Intelligence for Cervical Vertebral Maturation Assessment-A Systematic Review. J Clin Med 2024; 13:4047. [PMID: 39064087 PMCID: PMC11277636 DOI: 10.3390/jcm13144047] [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: 06/04/2024] [Revised: 07/03/2024] [Accepted: 07/08/2024] [Indexed: 07/28/2024] Open
Abstract
Background/Objectives: To systematically review and summarize the existing scientific evidence on the diagnostic performance of artificial intelligence (AI) in assessing cervical vertebral maturation (CVM). This review aimed to evaluate the accuracy and reliability of AI algorithms in comparison to those of experienced clinicians. Methods: Comprehensive searches were conducted across multiple databases, including PubMed, Scopus, Web of Science, and Embase, using a combination of Boolean operators and MeSH terms. The inclusion criteria were cross-sectional studies with neural network research, reporting diagnostic accuracy, and involving human subjects. Data extraction and quality assessment were performed independently by two reviewers, with a third reviewer resolving any disagreements. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool was used for bias assessment. Results: Eighteen studies met the inclusion criteria, predominantly employing supervised learning techniques, especially convolutional neural networks (CNNs). The diagnostic accuracy of AI models for CVM assessment varied widely, ranging from 57% to 95%. The factors influencing accuracy included the type of AI model, training data, and study methods. Geographic concentration and variability in the experience of radiograph readers also impacted the results. Conclusions: AI has considerable potential for enhancing the accuracy and reliability of CVM assessments in orthodontics. However, the variability in AI performance and the limited number of high-quality studies suggest the need for further research.
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Affiliation(s)
- Wojciech Kazimierczak
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Maciej Jedliński
- Department of Interdisciplinary Dentistry, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
| | - Julien Issa
- Chair of Practical Clinical Dentistry, Department of Diagnostics, Poznań University of Medical Sciences, 61-701 Poznań, Poland
| | - Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | | | - Marta Dyszkiewicz-Konwińska
- Chair of Practical Clinical Dentistry, Department of Diagnostics, Poznań University of Medical Sciences, 61-701 Poznań, Poland
| | - Ingrid Różyło-Kalinowska
- Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, 20-093 Lublin, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Kaan Orhan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06500, Turkey
- Medical Design Application and Research Center (MEDITAM), Ankara University, Ankara 06500, Turkey
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, 1088 Budapest, Hungary
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La Rosa S, Quinzi V, Palazzo G, Ronsivalle V, Lo Giudice A. The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence-Based Literature. Healthcare (Basel) 2024; 12:1311. [PMID: 38998846 PMCID: PMC11240988 DOI: 10.3390/healthcare12131311] [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: 05/24/2024] [Revised: 06/19/2024] [Accepted: 06/29/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has emerged as a revolutionary technology with several applications across different dental fields, including pedodontics. This systematic review has the objective to catalog and explore the various uses of artificial intelligence in pediatric dentistry. METHODS A thorough exploration of scientific databases was carried out to identify studies addressing the usage of AI in pediatric dentistry until December 2023 in the Embase, Scopus, PubMed, and Web of Science databases by two researchers, S.L.R. and A.L.G. RESULTS From a pool of 1301 articles, only 64 met the predefined criteria and were considered for inclusion in this review. From the data retrieved, it was possible to provide a narrative discussion of the potential implications of AI in the specialized area of pediatric dentistry. The use of AI algorithms and machine learning techniques has shown promising results in several applications of daily dental pediatric practice, including the following: (1) assisting the diagnostic and recognizing processes of early signs of dental pathologies, (2) enhancing orthodontic diagnosis by automating cephalometric tracing and estimating growth and development, (3) assisting and educating children to develop appropriate behavior for dental hygiene. CONCLUSION AI holds significant potential in transforming clinical practice, improving patient outcomes, and elevating the standards of care in pediatric patients. Future directions may involve developing cloud-based platforms for data integration and sharing, leveraging large datasets for improved predictive results, and expanding AI applications for the pediatric population.
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Affiliation(s)
- Salvatore La Rosa
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
| | - Vincenzo Quinzi
- Department of Life, Health & Environmental Sciences, Postgraduate School of Orthodontics, University of L’Aquila, 67100 L’Aquila, Italy
| | - Giuseppe Palazzo
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
| | - Vincenzo Ronsivalle
- Section of Oral Surgery, Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, Policlinico Universitario “Gaspare Rodolico—San Marco”, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy;
| | - Antonino Lo Giudice
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
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Bajjad AA, Gupta S, Agarwal S, Pawar RA, Kothawade MU, Singh G. Use of artificial intelligence in determination of bone age of the healthy individuals: A scoping review. J World Fed Orthod 2024; 13:95-102. [PMID: 37968159 DOI: 10.1016/j.ejwf.2023.10.001] [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: 08/24/2023] [Revised: 09/25/2023] [Accepted: 10/10/2023] [Indexed: 11/17/2023]
Abstract
BACKGROUND Bone age assessment, as an indicator of biological age, is widely used in orthodontics and pediatric endocrinology. Owing to significant subject variations in the manual method of assessment, artificial intelligence (AI), machine learning (ML), and deep learning (DL) play a significant role in this aspect. A scoping review was conducted to search the existing literature on the role of AI, ML, and DL in skeletal age or bone age assessment in healthy individuals. METHODS A literature search was conducted in PubMed, Scopus, and Web of Science from January 2012 to December 2022 using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Extension for Scoping Reviews (PRISMA-ScR) and Joanna Briggs Institute guidelines. Grey literature was searched using Google Scholar and OpenGrey. Hand-searching of the articles in all the reputed orthodontic journals and the references of the included articles were also searched for relevant articles for the present scoping review. RESULTS Nineteen articles that fulfilled the inclusion criteria were included. Ten studies used skeletal maturity indicators based on hand and wrist radiographs, two studies used magnetic resonance imaging and seven studies used cervical vertebrae maturity indicators based on lateral cephalograms to assess the skeletal age of the individuals. Most of these studies were published in non-orthodontic medical journals. BoneXpert automated software was the most commonly used software, followed by DL models, and ML models in the studies for assessment of bone age. The automated method was found to be as reliable as the manual method for assessment. CONCLUSIONS This scoping review validated the use of AI, ML, or DL in bone age assessment of individuals. A more uniform distribution of sufficient samples in different stages of maturation, use of three-dimensional inputs such as magnetic resonance imaging, and cone beam computed tomography is required for better training of the models to generalize the outputs for use in the target population.
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Affiliation(s)
- Adeel Ahmed Bajjad
- Department of Orthodontics, Kothiwal Dental College and Research Centre, Moradabad, India
| | - Seema Gupta
- Department of Orthodontics, Kothiwal Dental College and Research Centre, Moradabad, India.
| | - Soumitra Agarwal
- Department of Orthodontics, Kothiwal Dental College and Research Centre, Moradabad, India
| | - Rakesh A Pawar
- Department of Orthodontics, JMF ACPM Dental College, Dhule, India
| | | | - Gul Singh
- Department of Orthodontics, Kothiwal Dental College and Research Centre, Moradabad, India
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Kazimierczak N, Kazimierczak W, Serafin Z, Nowicki P, Nożewski J, Janiszewska-Olszowska J. AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review. J Clin Med 2024; 13:344. [PMID: 38256478 PMCID: PMC10816993 DOI: 10.3390/jcm13020344] [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: 11/19/2023] [Revised: 12/29/2023] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
The advent of artificial intelligence (AI) in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. Its usage in orthodontic practices worldwide has increased with the availability of various AI applications and tools. This review explores the principles of AI, its applications in orthodontics, and its implementation in clinical practice. A comprehensive literature review was conducted, focusing on AI applications in dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision making, and patient telemonitoring. Due to study heterogeneity, no meta-analysis was possible. AI has demonstrated high efficacy in all these areas, but variations in performance and the need for manual supervision suggest caution in clinical settings. The complexity and unpredictability of AI algorithms call for cautious implementation and regular manual validation. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for successful integration into orthodontic practice.
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Affiliation(s)
- Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Wojciech Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Paweł Nowicki
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Jakub Nożewski
- Department of Emeregncy Medicine, University Hospital No 2 in Bydgoszcz, Ujejskiego 75, 85-168 Bydgoszcz, Poland
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Atici SF, Ansari R, Allareddy V, Suhaym O, Cetin AE, Elnagar MH. AggregateNet: A deep learning model for automated classification of cervical vertebrae maturation stages. Orthod Craniofac Res 2023; 26 Suppl 1:111-117. [PMID: 36855827 DOI: 10.1111/ocr.12644] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 03/02/2023]
Abstract
OBJECTIVE A study of supervised automated classification of the cervical vertebrae maturation (CVM) stages using deep learning (DL) network is presented. A parallel structured deep convolutional neural network (CNN) with a pre-processing layer that takes X-ray images and the age as the input is proposed. METHODS A total of 1018 cephalometric radiographs were labelled and classified according to the CVM stages. The images were separated according to gender for better model-fitting. The images were cropped to extract the cervical vertebrae automatically using an object detector. The resulting images and the age inputs were used to train the proposed DL model: AggregateNet with a set of tunable directional edge enhancers. After the features of the images were extracted, the age input was concatenated to the output feature vector. To have the parallel network not overfit, data augmentation was used. The performance of our CNN model was compared with other DL models, ResNet20, Xception, MobileNetV2 and custom-designed CNN model with the directional filters. RESULTS The proposed innovative model that uses a parallel structured network preceded with a pre-processing layer of edge enhancement filters achieved a validation accuracy of 82.35% in CVM stage classification on female subjects, 75.0% in CVM stage classification on male subjects, exceeding the accuracy achieved with the other DL models investigated. The effectiveness of the directional filters is reflected in the improved performance attained in the results. If AggregateNet is used without directional filters, the test accuracy decreases to 80.0% on female subjects and to 74.03% on male subjects. CONCLUSION AggregateNet together with the tunable directional edge filters is observed to produce higher accuracy than the other models that we investigated in the fully automated determination of the CVM stages.
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Affiliation(s)
- Salih Furkan Atici
- Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, Illinois, USA
| | - Rashid Ansari
- Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, Illinois, USA
| | - Veerasathpurush Allareddy
- Department of Orthodontics, College of Dentistry, University of Illinois Chicago, Chicago, Illinois, USA
| | - Omar Suhaym
- Department of Oral and Maxillofacial Surgery, College of Dentistry, University of Illinois Chicago, Chicago, Illinois, USA
- King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Ahmet Enis Cetin
- Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, Illinois, USA
| | - Mohammed H Elnagar
- Department of Orthodontics, College of Dentistry, University of Illinois Chicago, Chicago, Illinois, USA
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Liu J, Zhang C, Shan Z. Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives. Healthcare (Basel) 2023; 11:2760. [PMID: 37893833 PMCID: PMC10606213 DOI: 10.3390/healthcare11202760] [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: 08/24/2023] [Revised: 10/11/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
In recent years, there has been the notable emergency of artificial intelligence (AI) as a transformative force in multiple domains, including orthodontics. This review aims to provide a comprehensive overview of the present state of AI applications in orthodontics, which can be categorized into the following domains: (1) diagnosis, including cephalometric analysis, dental analysis, facial analysis, skeletal-maturation-stage determination and upper-airway obstruction assessment; (2) treatment planning, including decision making for extractions and orthognathic surgery, and treatment outcome prediction; and (3) clinical practice, including practice guidance, remote care, and clinical documentation. We have witnessed a broadening of the application of AI in orthodontics, accompanied by advancements in its performance. Additionally, this review outlines the existing limitations within the field and offers future perspectives.
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Affiliation(s)
- Junqi Liu
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Chengfei Zhang
- Division of Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Zhiyi Shan
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
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Akay G, Akcayol MA, Özdem K, Güngör K. Deep convolutional neural network-the evaluation of cervical vertebrae maturation. Oral Radiol 2023; 39:629-638. [PMID: 36894716 DOI: 10.1007/s11282-023-00678-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 02/19/2023] [Indexed: 03/11/2023]
Abstract
OBJECTIVES This study aimed to automatically determine the cervical vertebral maturation (CVM) processes on lateral cephalometric radiograph images using a proposed deep learning-based convolutional neural network (CNN) model and to test the success rate of this CNN model in detecting CVM stages using precision, recall, and F1-score. METHODS A total of 588 digital lateral cephalometric radiographs of patients with a chronological age between 8 and 22 years were included in this study. CVM evaluation was carried out by two dentomaxillofacial radiologists. CVM stages in the images were divided into 6 subgroups according to the growth process. A convolutional neural network (CNN) model was developed in this study. Experimental studies for the developed model were carried out in the Jupyter Notebook environment using the Python programming language, the Keras, and TensorFlow libraries. RESULTS As a result of the training that lasted 40 epochs, 58% training and 57% test accuracy were obtained. The model obtained results that were very close to the training on the test data. On the other hand, it was determined that the model showed the highest success in terms of precision and F1-score in the CVM Stage 1 and the highest success in the recall value in the CVM Stage 2. CONCLUSION The experimental results have shown that the developed model achieved moderate success and it reached a classification accuracy of 58.66% in CVM stage classification.
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Affiliation(s)
- Gülsün Akay
- Department of Dentomaxillofacial Radiology, Gazi University Faculty of Dentistry, Emek, Ankara, Turkey.
| | - M Ali Akcayol
- Department of Computer Engineering, Gazi University Faculty of Engineering, Ankara, Turkey
| | - Kevser Özdem
- Department of Computer Engineering, Gazi University Faculty of Engineering, Ankara, Turkey
| | - Kahraman Güngör
- Department of Dentomaxillofacial Radiology, Gazi University Faculty of Dentistry, Emek, Ankara, Turkey
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Rana SS, Nath B, Chaudhari PK, Vichare S. Cervical Vertebral Maturation Assessment using various Machine Learning techniques on Lateral cephalogram: A systematic literature review. J Oral Biol Craniofac Res 2023; 13:642-651. [PMID: 37663368 PMCID: PMC10470275 DOI: 10.1016/j.jobcr.2023.08.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 05/12/2023] [Accepted: 08/16/2023] [Indexed: 09/05/2023] Open
Abstract
Importance For the assessment of optimum treatment timing in dentofacial orthopedics, understanding the growth process is of paramount importance. The evaluation of skeletal maturity based on study of the morphology of the cervical vertebrae has been devised to minimize radiation exposure of a patient due to hand wrist radiography. Cervical vertebral maturation assessment (CVMA) predictions have been examined in the state-of-the-art machine learning techniques in the recent past which require more attention and validation by clinicians and practitioners. Objectives This paper aimed to answer the question "How are machine learning techniques being employed in studies concerning cervical vertebral maturation assessment using lateral cephalograms?" Method A systematic search through the available literature was performed for this work based upon the Population, Intervention, Comparison and Outcome (PICO) framework. Data sources study selection data extraction and synthesis The searches were performed in Ovid Medline, Embase, PubMed and Cochrane Central Register of Controlled Trials (CENTRAL) and Cochrane Database of Systematic Reviews (CDSR). A search of the grey literature was also performed in Google Scholar and OpenGrey. We also did a hand-searching in the Angle Orthodontist, Journal of Orthodontics and Craniofacial Research, Progress in Orthodontics, and the American Journal of Orthodontics and Dentofacial Orthopedics. References from the included articles were also searched. Main outcomes and measures results A total of 25 papers which were assessed for full text, and 13 papers were included for the systematic review. The machine learning methods used were scrutinized according to their performance and comparison to human observers/experts. The accuracy of the models ranged between 60 and 90% or above, and satisfactory agreement and correlation with the human observers. Conclusions and relevance Machine learning models can be used for detection and classification of the cervical vertebrae maturation. In this systematic review (SR), the studies were summarized in terms of ML techniques applied, sample data, age range of sample and conventional method for CVMA, which showed that further studies with a uniform distribution of samples equally in stages of maturation and according to the gender is required for better training of the models in order to generalize the outputs for prolific use to target population.
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Affiliation(s)
- Shailendra Singh Rana
- Department of Dentistry, All India Institute of Medical Sciences, Bhatinda, Punjab, India
| | - Bhola Nath
- Department of Community Medicine, All India Institute of Medical Sciences, Bhatinda, Punjab, India
| | - Prabhat Kumar Chaudhari
- Division of Orthodontics and Dentofacial Deformities, Centre for Dental Education and Research, All India Institute of Medical Sciences, New Delhi, 110029, India
| | - Sharvari Vichare
- Department of Dentistry, All India Institute of Medical Sciences, Bhatinda, Punjab, India
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Pirayesh Z, Hassanzadeh-Samani S, Farzan A, Rohban MH, Ghorbanimehr MS, Mohammad-Rahimi H, Motamedian SR. A deep learning framework to scale linear facial measurements to actual size using horizontal visible iris diameter: a study on an Iranian population. Sci Rep 2023; 13:13755. [PMID: 37612309 PMCID: PMC10447546 DOI: 10.1038/s41598-023-40839-6] [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: 04/29/2023] [Accepted: 08/17/2023] [Indexed: 08/25/2023] Open
Abstract
Digital images allow for the objective evaluation of facial appearance and abnormalities as well as treatment outcomes and stability. With the advancement of technology, manual clinical measurements can be replaced with fully automatic photographic assessments. However, obtaining millimetric measurements on photographs does not provide clinicians with their actual value due to different image magnification ratios. A deep learning tool was developed to estimate linear measurements on images with unknown magnification using the iris diameter. A framework was designed to segment the eyes' iris and calculate the horizontal visible iris diameter (HVID) in pixels. A constant value of 12.2 mm was assigned as the HVID value in all the photographs. A vertical and a horizontal distance were measured in pixels on photographs of 94 subjects and were estimated in millimeters by calculating the magnification ratio using HVID. Manual measurement of the distances was conducted on the subjects and the actual and estimated amounts were compared using Bland-Altman analysis. The obtained error was calculated as mean absolute percentage error (MAPE) of 2.9% and 4.3% in horizontal and vertical measurements. Our study shows that due to the consistent size and narrow range of HVID values, the iris diameter can be used as a reliable scale to calibrate the magnification of the images to obtain precise measurements in further research.
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Affiliation(s)
- Zeynab Pirayesh
- Department of Orthodontics and Dentofacial Orthopedics, School of Dentistry, Zanjan University of Medical Sciences, Zanjan, Iran
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Sahel Hassanzadeh-Samani
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Arash Farzan
- Department of Orthodontics and Dentofacial Orthopedics, School of Dentistry, Zanjan University of Medical Sciences, Zanjan, Iran
| | | | | | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
- Postdoc Research Fellow, Division of Artificial Intelligence Imaging Research, University of Maryland School of Dentistry, Baltimore, Maryland, USA
| | - Saeed Reza Motamedian
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Zakhar G, Hazime S, Eckert G, Wong A, Badirli S, Turkkahraman H. Prediction of Pubertal Mandibular Growth in Males with Class II Malocclusion by Utilizing Machine Learning. Diagnostics (Basel) 2023; 13:2713. [PMID: 37627972 PMCID: PMC10453460 DOI: 10.3390/diagnostics13162713] [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: 07/03/2023] [Revised: 08/11/2023] [Accepted: 08/17/2023] [Indexed: 08/27/2023] Open
Abstract
The goal of this study was to create a novel machine learning (ML) model that can predict the magnitude and direction of pubertal mandibular growth in males with Class II malocclusion. Lateral cephalometric radiographs of 123 males at three time points (T1: 12; T2: 14; T3: 16 years old) were collected from an online database of longitudinal growth studies. Each radiograph was traced, and seven different ML models were trained using 38 data points obtained from 92 subjects. Thirty-one subjects were used as the test group to predict the post-pubertal mandibular length and y-axis, using input data from T1 and T2 combined (2 year prediction), and T1 alone (4 year prediction). Mean absolute errors (MAEs) were used to evaluate the accuracy of each model. For all ML methods tested using the 2 year prediction, the MAEs for post-pubertal mandibular length ranged from 2.11-6.07 mm to 0.85-2.74° for the y-axis. For all ML methods tested with 4 year prediction, the MAEs for post-pubertal mandibular length ranged from 2.32-5.28 mm to 1.25-1.72° for the y-axis. Besides its initial length, the most predictive factors for mandibular length were found to be chronological age, upper and lower face heights, upper and lower incisor positions, and inclinations. For the y-axis, the most predictive factors were found to be y-axis at earlier time points, SN-MP, SN-Pog, SNB, and SNA. Although the potential of ML techniques to accurately forecast future mandibular growth in Class II cases is promising, a requirement for more substantial sample sizes exists to further enhance the precision of these predictions.
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Affiliation(s)
- Grant Zakhar
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (G.Z.); (A.W.)
| | - Samir Hazime
- Indiana University School of Dentistry, Indianapolis, IN 46202, USA;
| | - George Eckert
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA;
| | - Ariel Wong
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (G.Z.); (A.W.)
| | | | - Hakan Turkkahraman
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN 46202, USA; (G.Z.); (A.W.)
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Automatic determination of pubertal growth spurts based on the cervical vertebral maturation staging using deep convolutional neural networks. J World Fed Orthod 2023; 12:56-63. [PMID: 36890034 DOI: 10.1016/j.ejwf.2023.02.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 02/15/2023] [Accepted: 02/15/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND This study aimed to develop a deep convolutional neural network (CNN) for automatic classification of pubertal growth spurts using cervical vertebral maturation (CVM) staging based on the lateral cephalograms of an Iranian subpopulation. MATERIAL AND METHODS Cephalometric radiographs were collected from 1846 eligible patients (aged 5-18 years) referred to the orthodontic department of Hamadan University of Medical Sciences. These images were labeled by two experienced orthodontists. Two scenarios, including two- and three-class (pubertal growth spurts using CVM), were considered as the output for the classification task. The cropped image of the second to fourth cervical vertebrae was used as input to the network. After the preprocessing, the augmentation step, and hyperparameter tuning, the networks were trained with initial random weighting and transfer learning. Finally, the best architecture among the different architectures was determined based on the accuracy and F-score criteria. RESULTS The CNN based on the ConvNeXtBase-296 architecture had the highest accuracy for automatically assessing pubertal growth spurts based on CVM staging in both three-class (82% accuracy) and two-class (93% accuracy) scenarios. Given the limited amount of data available for training the target networks for most of the architectures in use, transfer learning improves predictive performance. CONCLUSIONS The results of this study confirm the potential of CNNs as an auxiliary diagnostic tool for intelligent assessment of skeletal maturation staging with high accuracy even with a relatively small number of images. Considering the development of orthodontic science toward digitalization, the development of such intelligent decision systems is proposed.
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13
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Shahnavazi M, Mohamadrahimi H. The application of artificial neural networks in the detection of mandibular fractures using panoramic radiography. Dent Res J (Isfahan) 2023; 20:27. [PMID: 36960025 PMCID: PMC10028573 DOI: 10.4103/1735-3327.369629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/06/2022] [Accepted: 12/20/2022] [Indexed: 03/25/2023] Open
Abstract
Background Panoramic radiography is a standard diagnostic imaging method for dentists. However, it is challenging to detect mandibular trauma and fractures in panoramic radiographs due to the superimposed facial skeleton structures. The objective of this study was to develop a deep learning algorithm that is capable of detecting mandibular fractures and trauma automatically and compare its performance with general dentists. Materials and Methods This is a retrospective diagnostic test accuracy study. This study used a two-stage deep learning framework. To train the model, 190 panoramic images were collected from four different sources. The mandible was first segmented using a U-net model. Then, to detect fractures, a model named Faster region-based convolutional neural network was applied. In the end, a comparison was made between the accuracy, specificity, and sensitivity of artificial intelligence and general dentists in trauma diagnosis. Results The mAP50 and mAP75 for object detection were 98.66% and 57.90%, respectively. The classification accuracy of the model was 91.67%. The sensitivity and specificity of the model were 100% and 83.33%, respectively. On the other hand, human-level diagnostic accuracy, sensitivity, and specificity were 87.22 ± 8.91, 82.22 ± 16.39, and 92.22 ± 6.33, respectively. Conclusion Our framework can provide a level of performance better than general dentists when it comes to diagnosing trauma or fractures.
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Affiliation(s)
- Maryam Shahnavazi
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Aja University of Medical Sciences, Tehran, Iran
- Address for correspondence: Dr. Maryam Shahnavazi, School of Dentistry, Aja University of Medical Sciences, Misaq Complex, 13th East Street, Ajoudanieh, Tehran, Iran. E-mail:
| | - Hosein Mohamadrahimi
- Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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14
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Dumbryte I, Narbutis D, Vailionis A, Juodkazis S, Malinauskas M. Revelation of microcracks as tooth structural element by X-ray tomography and machine learning. Sci Rep 2022; 12:22489. [PMID: 36577779 PMCID: PMC9797571 DOI: 10.1038/s41598-022-27062-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/23/2022] [Indexed: 12/30/2022] Open
Abstract
Although teeth microcracks (MCs) have long been considered more of an aesthetic problem, their exact role in the structure of a tooth and impact on its functionality is still unknown. The aim of this study was to reveal the possibilities of an X-ray micro-computed tomography ([Formula: see text]CT) in combination with convolutional neural network (CNN) assisted voxel classification and volume segmentation for three-dimensional (3D) qualitative analysis of tooth microstructure and verify this approach with four extracted human premolars. Samples were scanned using a [Formula: see text]CT instrument (Xradia 520 Versa; ZEISS) and segmented with CNN to identify enamel, dentin, and cracks. A new CNN image segmentation model was trained based on "Multiclass semantic segmentation using DeepLabV3+" example and was implemented with "TensorFlow". The technique which was used allowed 3D characterization of all MCs of a tooth, regardless of the volume of the tooth in which they begin and extend, and the evaluation of the arrangement of cracks and their structural features. The proposed method revealed an intricate star-shaped network of MCs covering most of the inner tooth, and the main crack planes in all samples were arranged radially in two almost perpendicular directions, suggesting that the cracks could be considered as a planar structure.
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Affiliation(s)
- Irma Dumbryte
- grid.6441.70000 0001 2243 2806Institute of Odontology, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Donatas Narbutis
- grid.6441.70000 0001 2243 2806Institute of Theoretical Physics and Astronomy, Faculty of Physics, Vilnius University, Vilnius, Lithuania
| | - Arturas Vailionis
- grid.168010.e0000000419368956Stanford Nano Shared Facilities, Stanford University, Stanford, USA ,grid.6901.e0000 0001 1091 4533Department of Physics, Kaunas University of Technology, Kaunas, Lithuania
| | - Saulius Juodkazis
- grid.1027.40000 0004 0409 2862Optical Sciences Centre and ARC Training Centre in Surface Engineering for Advanced Materials (SEAM), School of Science, Swinburne University of Technology, Hawthorn, Australia ,grid.32197.3e0000 0001 2179 2105WRH Program International Research Frontiers Initiative (IRFI) Tokyo Institute of Technology, Nagatsuta-cho, Midori-ku, Yokohama, Japan
| | - Mangirdas Malinauskas
- grid.6441.70000 0001 2243 2806Laser Research Center, Faculty of Physics, Vilnius University, Vilnius, Lithuania
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