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Cao L, He H, Hua F. CURRENT NEURAL NETWORKS DEMONSTRATE POTENTIAL IN AUTOMATED CERVICAL VERTEBRAL MATURATION STAGE CLASSIFICATION BASED ON LATERAL CEPHALOGRAMS. J Evid Based Dent Pract 2024; 24:101928. [PMID: 38448121 DOI: 10.1016/j.jebdp.2023.101928] [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: 03/08/2024]
Abstract
ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION Neural networks for classification of cervical vertebrae maturation: a systematic review. Mathew R, Palatinus S, Padala S, Alshehri A, Awadh W, Bhandi S, Thomas J, Patil S. Angle Orthod. 2022 Nov 1;92(6):796-804. SOURCE OF FUNDING No financial support was reported. TYPE OF STUDY/DESIGN Systematic review.
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Niño-Sandoval TC, Doria-Martinez AM, Escobar RAV, Sánchez EL, Rojas IB, Álvarez LCV, Mc Cann DSF, Támara-Patiño LM. Efficacy of the methods of age determination using artificial intelligence in panoramic radiographs - a systematic review. Int J Legal Med 2024:10.1007/s00414-024-03162-x. [PMID: 38400923 DOI: 10.1007/s00414-024-03162-x] [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: 08/25/2023] [Accepted: 01/08/2024] [Indexed: 02/26/2024]
Abstract
The aim of this systematic review is to analyze the literature to determine whether the methods of artificial intelligence are effective in determining age in panoramic radiographs. Searches without language and year limits were conducted in PubMed/Medline, Embase, Web of Science, and Scopus databases. Hand searches were also performed, and unpublished manuscripts were searched in specialized journals. Thirty-six articles were included in the analysis. Significant differences in terms of root mean square error and mean absolute error were found between manual methods and artificial intelligence techniques, favoring the use of artificial intelligence (p < 0.00001). Few articles compared deep learning methods with machine learning models or manual models. Although there are advantages of machine learning in data processing and deep learning in data collection and analysis, non-comparable data was a limitation of this study. More information is needed on the comparison of these techniques, with particular emphasis on time as a variable.
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Affiliation(s)
- Tania Camila Niño-Sandoval
- Research center of the Institute National of Legal Medicine and Forensic Sciences, Research Institute, Faculty of Medicine, University of Antioquia, Medellin, Colombia
| | | | | | | | - Isabella Bermón Rojas
- Electronic Engineering Faculty, Department of Electronics and Telecommunications, University of Antioquia, Medellin, Colombia
| | - Laura Cristina Vargas Álvarez
- Electronic Engineering Faculty, Department of Electronics and Telecommunications, University of Antioquia, Medellin, Colombia
| | - David Stephen Fernandez Mc Cann
- Electronic Engineering Faculty, Department of Electronics and Telecommunications, University of Antioquia, Medellin, Colombia
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Xu S, Peng H, Yang L, Zhong W, Gao X, Song J. An Automatic Grading System for Orthodontically Induced External Root Resorption Based on Deep Convolutional Neural Network. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01045-6. [PMID: 38393620 DOI: 10.1007/s10278-024-01045-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/09/2024] [Accepted: 02/09/2024] [Indexed: 02/25/2024]
Abstract
Orthodontically induced external root resorption (OIERR) is a common complication of orthodontic treatments. Accurate OIERR grading is crucial for clinical intervention. This study aimed to evaluate six deep convolutional neural networks (CNNs) for performing OIERR grading on tooth slices to construct an automatic grading system for OIERR. A total of 2146 tooth slices of different OIERR grades were collected and preprocessed. Six pre-trained CNNs (EfficientNet-B1, EfficientNet-B2, EfficientNet-B3, EfficientNet-B4, EfficientNet-B5, and MobileNet-V3) were trained and validated on the pre-processed images based on four different cross-validation methods. The performances of the CNNs on a test set were evaluated and compared with those of orthodontists. The gradient-weighted class activation mapping (Grad-CAM) technique was used to explore the area of maximum impact on the model decisions in the tooth slices. The six CNN models performed remarkably well in OIERR grading, with a mean accuracy of 0.92, surpassing that of the orthodontists (mean accuracy of 0.82). EfficientNet-B4 trained with fivefold cross-validation emerged as the final OIERR grading system, with a high accuracy of 0.94. Grad-CAM revealed that the apical region had the greatest effect on the OIERR grading system. The six CNNs demonstrated excellent OIERR grading and outperformed orthodontists. The proposed OIERR grading system holds potential as a reliable diagnostic support for orthodontists in clinical practice.
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Affiliation(s)
- Shuxi Xu
- College of Stomatology, Chongqing Medical University, Chongqing, 401147, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, 401147, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, 401147, China
| | - Houli Peng
- College of Stomatology, Chongqing Medical University, Chongqing, 401147, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, 401147, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, 401147, China
| | - Lanxin Yang
- College of Stomatology, Chongqing Medical University, Chongqing, 401147, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, 401147, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, 401147, China
| | - Wenjie Zhong
- College of Stomatology, Chongqing Medical University, Chongqing, 401147, China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, 401147, China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, 401147, China
| | - Xiang Gao
- College of Stomatology, Chongqing Medical University, Chongqing, 401147, China.
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, 401147, China.
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, 401147, China.
| | - Jinlin Song
- College of Stomatology, Chongqing Medical University, Chongqing, 401147, China.
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing, 401147, China.
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, 401147, China.
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Zhu M, Yang P, Bian C, Zuo F, Guo Z, Wang Y, Wang Y, Bai Y, Zhang N. Convolutional neural network-assisted diagnosis of midpalatal suture maturation stage in cone-beam computed tomography. J Dent 2024; 141:104808. [PMID: 38101505 DOI: 10.1016/j.jdent.2023.104808] [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: 09/09/2023] [Revised: 12/09/2023] [Accepted: 12/12/2023] [Indexed: 12/17/2023] Open
Abstract
OBJECTIVES The selection of treatment for maxillary expansion is closely related to the calcification degree of the midpalatal suture. A classification method for individual assessment of the morphology of midpalatal suture in cone-beam computed tomography (CBCT) is useful for evaluating the calcification degree. Currently, convolutional neural networks (CNNs) have been introduced into the field of oral and maxillofacial imaging diagnosis. This study validated the ability of CNN models in assessing the maturation stage of the midpalatal suture. METHODS The existing CNN model ResNet50 was trained to locate the CBCT transverse plane which contained a complete midpalatal suture. ResNet18, ResNet50, RessNet101, Inception-v3, and Efficientnetv2-s models were trained to evaluate the midpalatal suture maturation stage. Multi-class classification metrics, accuracy, recall, precision, F1-score, and area under the curve values from the receiver operating characteristic curve were used to evaluate the performance of the models, and gradient-weighted class activation map technology was utilised to visualise five midpalatal suture maturation stages for each model. RESULTS Resnet50 demonstrated an accuracy of 99.74 % in identifying the transverse plane that contained the complete midpalatal suture. The highest accuracies achieved on the two-stage, three-stage, and five-stage maturation classification tests were 95.15, 88.06, and 75.37 %, all of which exceeded the average accuracy of three experienced orthodontists. CONCLUSIONS The CNN model can locate the plane of the midpalatal suture in CBCT images and can assist clinicians in assessing the maturation stage of the midpalatal suture to select the means of maxillary expansion. CLINICAL SIGNIFICANCE The application of artificial intelligence on CBCT midpalatal suture plane localisation and maturation stage evaluation enhances diagnostic and treatment efficiency and accuracy of individual assessment of midpalatal suture calcification degree. Additionally, it assists the clinical palatal expansion technique in achieving ideal results.
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Affiliation(s)
- Mengyao Zhu
- Department of Orthodontics, School of Stomatology, Capital Medical University, Beijing 100050, China
| | - Pan Yang
- Department of Oral and Maxillofacial Radiology, School of Stomatology, Capital Medical University, Beijing 100050, China
| | - Ce Bian
- Department of Orthodontics, School of Stomatology, Capital Medical University, Beijing 100050, China
| | - Feifei Zuo
- LargeV Instrument Corp., Ltd., Beijing 100084, China
| | - Zhongmin Guo
- LargeV Instrument Corp., Ltd., Beijing 100084, China
| | - Yufeng Wang
- LargeV Instrument Corp., Ltd., Beijing 100084, China
| | - Yajie Wang
- Department of Engineering Physics, Tsinghua University, Beijing 100084, China; LargeV Instrument Corp., Ltd., Beijing 100084, China
| | - Yuxing Bai
- Department of Orthodontics, School of Stomatology, Capital Medical University, Beijing 100050, China
| | - Ning Zhang
- Department of Orthodontics, School of Stomatology, Capital Medical University, Beijing 100050, China.
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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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Hartoonian S, Hosseini M, Yousefi I, Mahdian M, Ghazizadeh Ahsaie M. Applications of artificial intelligence in dentomaxillofacial imaging-a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol 2024:S2212-4403(23)01566-3. [PMID: 38637235 DOI: 10.1016/j.oooo.2023.12.790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/02/2023] [Accepted: 12/22/2023] [Indexed: 04/20/2024]
Abstract
BACKGROUND Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities. STUDY DESIGN A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another. RESULTS The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection. CONCLUSION Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.
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Affiliation(s)
- Serlie Hartoonian
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Matine Hosseini
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Iman Yousefi
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mina Mahdian
- Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Mitra Ghazizadeh Ahsaie
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Dipalma G, Inchingolo AD, Inchingolo AM, Piras F, Carpentiere V, Garofoli G, Azzollini D, Campanelli M, Paduanelli G, Palermo A, Inchingolo F. Artificial Intelligence and Its Clinical Applications in Orthodontics: A Systematic Review. Diagnostics (Basel) 2023; 13:3677. [PMID: 38132261 PMCID: PMC10743240 DOI: 10.3390/diagnostics13243677] [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: 11/15/2023] [Revised: 12/05/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
This review aims to analyze different strategies that make use of artificial intelligence to enhance diagnosis, treatment planning, and monitoring in orthodontics. Orthodontics has seen significant technological advancements with the introduction of digital equipment, including cone beam computed tomography, intraoral scanners, and software coupled to these devices. The use of deep learning in software has sped up image processing processes. Deep learning is an artificial intelligence technology that trains computers to analyze data like the human brain does. Deep learning models are capable of recognizing complex patterns in photos, text, audio, and other data to generate accurate information and predictions. MATERIALS AND METHODS Pubmed, Scopus, and Web of Science were used to discover publications from 1 January 2013 to 18 October 2023 that matched our topic. A comparison of various artificial intelligence applications in orthodontics was generated. RESULTS A final number of 33 studies were included in the review for qualitative analysis. CONCLUSIONS These studies demonstrate the effectiveness of AI in enhancing orthodontic diagnosis, treatment planning, and assessment. A lot of articles emphasize the integration of artificial intelligence into orthodontics and its potential to revolutionize treatment monitoring, evaluation, and patient outcomes.
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Affiliation(s)
- Gianna Dipalma
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Alessio Danilo Inchingolo
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Angelo Michele Inchingolo
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Fabio Piras
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Vincenzo Carpentiere
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Grazia Garofoli
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Daniela Azzollini
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Merigrazia Campanelli
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Gregorio Paduanelli
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Andrea Palermo
- Implant Dentistry College of Medicine and Dentistry Birmingham, University of Birmingham, Birmingham B46BN, UK;
| | - Francesco Inchingolo
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
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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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Jiang C, Jiang F, Xie Z, Sun J, Sun Y, Zhang M, Zhou J, Feng Q, Zhang G, Xing K, Mei H, Li J. Evaluation of automated detection of head position on lateral cephalometric radiographs based on deep learning techniques. Ann Anat 2023; 250:152114. [PMID: 37302431 DOI: 10.1016/j.aanat.2023.152114] [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: 03/14/2023] [Revised: 05/13/2023] [Accepted: 05/20/2023] [Indexed: 06/13/2023]
Abstract
BACKGROUND Lateral cephalometric radiograph (LCR) is crucial to diagnosis and treatment planning of maxillofacial diseases, but inappropriate head position, which reduces the accuracy of cephalometric measurements, can be challenging to detect for clinicians. This non-interventional retrospective study aims to develop two deep learning (DL) systems to efficiently, accurately, and instantly detect the head position on LCRs. METHODS LCRs from 13 centers were reviewed and a total of 3000 radiographs were collected and divided into 2400 cases (80.0 %) in the training set and 600 cases (20.0 %) in the validation set. Another 300 cases were selected independently as the test set. All the images were evaluated and landmarked by two board-certified orthodontists as references. The head position of the LCR was classified by the angle between the Frankfort Horizontal (FH) plane and the true horizontal (HOR) plane, and a value within - 3°- 3° was considered normal. The YOLOv3 model based on the traditional fixed-point method and the modified ResNet50 model featuring a non-linear mapping residual network were constructed and evaluated. Heatmap was generated to visualize the performances. RESULTS The modified ResNet50 model showed a superior classification accuracy of 96.0 %, higher than 93.5 % of the YOLOv3 model. The sensitivity&recall and specificity of the modified ResNet50 model were 0.959, 0.969, and those of the YOLOv3 model were 0.846, 0.916. The area under the curve (AUC) values of the modified ResNet50 and the YOLOv3 model were 0.985 ± 0.04 and 0.942 ± 0.042, respectively. Saliency maps demonstrated that the modified ResNet50 model considered the alignment of cervical vertebras, not just the periorbital and perinasal areas, as the YOLOv3 model did. CONCLUSIONS The modified ResNet50 model outperformed the YOLOv3 model in classifying head position on LCRs and showed promising potential in facilitating making accurate diagnoses and optimal treatment plans.
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Affiliation(s)
- Chen Jiang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Fulin Jiang
- Chongqing University Three Gorges Hospital, Chongqing 404031, China
| | - Zhuokai Xie
- University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jikui Sun
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Yan Sun
- University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Mei Zhang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Jiawei Zhou
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Qingchen Feng
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Guanning Zhang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Ke Xing
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Hongxiang Mei
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Juan Li
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China.
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12
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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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13
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Li H, Li H, Yuan L, Liu C, Xiao S, Liu Z, Zhou G, Dong T, Ouyang N, Liu L, Ma C, Feng Y, Zheng Y, Xia L, Fang B. The psc-CVM assessment system: A three-stage type system for CVM assessment based on deep learning. BMC Oral Health 2023; 23:557. [PMID: 37573308 PMCID: PMC10422791 DOI: 10.1186/s12903-023-03266-7] [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: 12/09/2022] [Accepted: 07/29/2023] [Indexed: 08/14/2023] Open
Abstract
BACKGROUND Many scholars have proven cervical vertebral maturation (CVM) method can predict the growth and development and assist in choosing the best time for treatment. However, assessing CVM is a complex process. The experience and seniority of the clinicians have an enormous impact on judgment. This study aims to establish a fully automated, high-accuracy CVM assessment system called the psc-CVM assessment system, based on deep learning, to provide valuable reference information for the growth period determination. METHODS This study used 10,200 lateral cephalograms as the data set (7111 in train set, 1544 in validation set and 1545 in test set) to train the system. The psc-CVM assessment system is designed as three parts with different roles, each operating in a specific order. 1) Position Network for locating the position of cervical vertebrae; 2) Shape Recognition Network for recognizing and extracting the shapes of cervical vertebrae; and 3) CVM Assessment Network for assessing CVM according to the shapes of cervical vertebrae. Statistical analysis was conducted to detect the performance of the system and the agreement of CVM assessment between the system and the expert panel. Heat maps were analyzed to understand better what the system had learned. The area of the third (C3), fourth (C4) cervical vertebrae and the lower edge of second (C2) cervical vertebrae were activated when the system was assessing the images. RESULTS The system has achieved good performance for CVM assessment with an average AUC (the area under the curve) of 0.94 and total accuracy of 70.42%, as evaluated on the test set. The Cohen's Kappa between the system and the expert panel is 0.645. The weighted Kappa between the system and the expert panel is 0.844. The overall ICC between the psc-CVM assessment system and the expert panel was 0.946. The F1 score rank for the psc-CVM assessment system was: CVS (cervical vertebral maturation stage) 6 > CVS1 > CVS4 > CVS5 > CVS3 > CVS2. CONCLUSIONS The results showed that the psc-CVM assessment system achieved high accuracy in CVM assessment. The system in this study was significantly consistent with expert panels in CVM assessment, indicating that the system can be used as an efficient, accurate, and stable diagnostic aid to provide a clinical aid for determining growth and developmental stages by CVM.
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Affiliation(s)
- Hairui Li
- Department of Orthodontics, Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Haizhen Li
- Department of Orthodontics, Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Lingjun Yuan
- Department of Orthodontics, Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Chao Liu
- Department of Orthodontics, Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Shengzhao Xiao
- Department of Orthodontics, Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Zhen Liu
- Department of Orthodontics, Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Guoli Zhou
- Department of Orthodontics, Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Ting Dong
- Department of Orthodontics, Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Ningjuan Ouyang
- Department of Orthodontics, Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Lu Liu
- Department of Orthodontics, Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | | | - Yang Feng
- Translational Medicine Research Platform of Oral Biomechanics and Artificial Intelligence, Department of Orthodontics, Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Youyi Zheng
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China.
| | - Lunguo Xia
- Department of Orthodontics, Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
| | - Bing Fang
- Department of Orthodontics, Shanghai Ninth People's Hospital affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
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14
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Popova T, Stocker T, Khazaei Y, Malenova Y, Wichelhaus A, Sabbagh H. Influence of growth structures and fixed appliances on automated cephalometric landmark recognition with a customized convolutional neural network. BMC Oral Health 2023; 23:274. [PMID: 37165409 PMCID: PMC10173502 DOI: 10.1186/s12903-023-02984-2] [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: 11/27/2022] [Accepted: 04/20/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND One of the main uses of artificial intelligence in the field of orthodontics is automated cephalometric analysis. Aim of the present study was to evaluate whether developmental stages of a dentition, fixed orthodontic appliances or other dental appliances may affect detection of cephalometric landmarks. METHODS For the purposes of this study a Convolutional Neural Network (CNN) for automated detection of cephalometric landmarks was developed. The model was trained on 430 cephalometric radiographs and its performance was then tested on 460 new radiographs. The accuracy of landmark detection in patients with permanent dentition was compared with that in patients with mixed dentition. Furthermore, the influence of fixed orthodontic appliances and orthodontic brackets and/or bands was investigated only in patients with permanent dentition. A t-test was performed to evaluate the mean radial errors (MREs) against the corresponding SDs for each landmark in the two categories, of which the significance was set at p < 0.05. RESULTS The study showed significant differences in the recognition accuracy of the Ap-Inferior point and the Is-Superior point between patients with permanent dentition and mixed dentition, and no significant differences in the recognition process between patients without fixed orthodontic appliances and patients with orthodontic brackets and/or bands and other fixed orthodontic appliances. CONCLUSIONS The results indicated that growth structures and developmental stages of a dentition had an impact on the performance of the customized CNN model by dental cephalometric landmarks. Fixed orthodontic appliances such as brackets, bands, and other fixed orthodontic appliances, had no significant effect on the performance of the CNN model.
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Affiliation(s)
- Teodora Popova
- Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany
| | - Thomas Stocker
- Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany
| | - Yeganeh Khazaei
- Department of Statistics, Statistical Consultation Unit, StaBLab, LMU Munich, Akademiestr. 1, 80799, Munich, Germany
| | - Yoana Malenova
- Department of Oral and Maxillofacial Surgery, University Hospital, LMU Munich, Lindwurmstrasse 2a, 80337, Munich, Germany
| | - Andrea Wichelhaus
- Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany
| | - Hisham Sabbagh
- Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany.
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15
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Liao N, Dai J, Tang Y, Zhong Q, Mo S. iCVM: An Interpretable Deep Learning Model for CVM Assessment under Label Uncertainty. IEEE J Biomed Health Inform 2022; 26:4325-4334. [PMID: 35653451 DOI: 10.1109/jbhi.2022.3179619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The Cervical Vertebral Maturation (CVM) method aims to determine the craniofacial skeletal maturational stage, which is crucial for orthodontic and orthopedic treatment. In this paper, we explore the potential of deep learning for automatic CVM assessment. In particular, we propose a convolutional neural network named iCVM. Based on the residual network, it is specialized for the challenges unique to the task of CVM assessment. 1) To combat overfitting due to limited data size, multiple dropout layers are utilized. 2) To address the inevitable label ambiguity between adjacent maturational stages, we introduce the concept of label distribution learning in the loss function. Besides, we attempt to analyze the regions important for the prediction of the model by using the Grad-CAM technique. The learned strategy shows surprisingly high consistency with the clinical criteria. This indicates that the decisions made by our model are well interpretable, which is critical in evaluation of growth and development in orthodontics. Moreover, to drive future research in the field, we release a new dataset named CVM-900 along with the paper. It contains the cervical part of 900 lateral cephalograms collected from orthodontic patients of different ages and genders. Experimental results show that the proposed approach achieves superior performance on CVM-900 in terms of various evaluation metrics.
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16
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Automatic Classification for Sagittal Craniofacial Patterns Based on Different Convolutional Neural Networks. Diagnostics (Basel) 2022; 12:diagnostics12061359. [PMID: 35741169 PMCID: PMC9221941 DOI: 10.3390/diagnostics12061359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/21/2022] [Accepted: 05/27/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: The present study aims to evaluate and compare the model performances of different convolutional neural networks (CNNs) used for classifying sagittal skeletal patterns. (2) Methods: A total of 2432 lateral cephalometric radiographs were collected. They were labeled as Class I, Class II, and Class III patterns, according to their ANB angles and Wits values. The radiographs were randomly divided into the training, validation, and test sets in the ratio of 70%:15%:15%. Four different CNNs, namely VGG16, GoogLeNet, ResNet152, and DenseNet161, were trained, and their model performances were compared. (3) Results: The accuracy of the four CNNs was ranked as follows: DenseNet161 > ResNet152 > VGG16 > GoogLeNet. DenseNet161 had the highest accuracy, while GoogLeNet possessed the smallest model size and fastest inference speed. The CNNs showed better capabilities for identifying Class III patterns, followed by Classes II and I. Most of the samples that were misclassified by the CNNs were boundary cases. The activation area confirmed the CNNs without overfitting and indicated that artificial intelligence could recognize the compensatory dental features in the anterior region of the jaws and lips. (4) Conclusions: CNNs can quickly and effectively assist orthodontists in the diagnosis of sagittal skeletal classification patterns.
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Kim EG, Oh IS, So JE, Kang J, Le VNT, Tak MK, Lee DW. Estimating Cervical Vertebral Maturation with a Lateral Cephalogram Using the Convolutional Neural Network. J Clin Med 2021; 10:jcm10225400. [PMID: 34830682 PMCID: PMC8620598 DOI: 10.3390/jcm10225400] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 11/15/2021] [Accepted: 11/16/2021] [Indexed: 11/16/2022] Open
Abstract
Recently, the estimation of bone maturation using deep learning has been actively conducted. However, many studies have considered hand–wrist radiographs, while a few studies have focused on estimating cervical vertebral maturation (CVM) using lateral cephalograms. This study proposes the use of deep learning models for estimating CVM from lateral cephalograms. As the second, third, and fourth cervical vertebral regions (denoted as C2, C3, and C4, respectively) are considerably smaller than the whole image, we propose a stepwise segmentation-based model that focuses on the C2–C4 regions. We propose three convolutional neural network-based classification models: a one-step model with only CVM classification, a two-step model with region of interest (ROI) detection and CVM classification, and a three-step model with ROI detection, cervical segmentation, and CVM classification. Our dataset contains 600 lateral cephalogram images, comprising six classes with 100 images each. The three-step segmentation-based model produced the best accuracy (62.5%) compared to the models that were not segmentation-based.
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Affiliation(s)
- Eun-Gyeong Kim
- Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54907, Korea; (E.-G.K.); (I.-S.O.); (J.-E.S.); (J.K.)
| | - Il-Seok Oh
- Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54907, Korea; (E.-G.K.); (I.-S.O.); (J.-E.S.); (J.K.)
| | - Jeong-Eun So
- Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54907, Korea; (E.-G.K.); (I.-S.O.); (J.-E.S.); (J.K.)
| | - Junhyeok Kang
- Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54907, Korea; (E.-G.K.); (I.-S.O.); (J.-E.S.); (J.K.)
| | - Van Nhat Thang Le
- Department of Pediatric Dentistry, Research Institute of Clinical Medicine, Jeonbuk National University, Jeonju 54907, Korea; (V.N.T.L.); (M.-K.T.)
- Biomedical Research Institute, Jeonbuk National University Hospital, Jeonju 54907, Korea
- Faculty of Odonto-Stomatology, Hue University of Medicine and Pharmacy, Hue University, Hue 49120, Vietnam
| | - Min-Kyung Tak
- Department of Pediatric Dentistry, Research Institute of Clinical Medicine, Jeonbuk National University, Jeonju 54907, Korea; (V.N.T.L.); (M.-K.T.)
- Biomedical Research Institute, Jeonbuk National University Hospital, Jeonju 54907, Korea
| | - Dae-Woo Lee
- Department of Pediatric Dentistry, Research Institute of Clinical Medicine, Jeonbuk National University, Jeonju 54907, Korea; (V.N.T.L.); (M.-K.T.)
- Biomedical Research Institute, Jeonbuk National University Hospital, Jeonju 54907, Korea
- Correspondence:
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