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Wiechens B, Backhaus SJ, Oestreicher D, Beutner D, Schliephake H, Meyer-Marcotty P, Brockmeyer P. Assessment of oropharyngeal respiratory volume and sleep apnoea scores using peripheral arterial tonometry may improve diagnosis and treatment planning of combined dysgnathia therapy approaches. J Oral Rehabil 2024; 51:733-742. [PMID: 38100245 DOI: 10.1111/joor.13641] [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: 07/28/2023] [Revised: 11/16/2023] [Accepted: 12/03/2023] [Indexed: 03/02/2024]
Abstract
BACKGROUND Increased daytime sleepiness is a frequently reported symptom in patients with pronounced dysgnathia. OBJECTIVES This study investigated possible correlations using home peripheral arterial tonometry (PAT) and oropharyngeal airway volume determination in patients with dysgnathia and daytime sleepiness. METHODS Twenty patients (13 male, median age 27.6 ± 6.8 years) with abnormal sleep history and 10 skeletal neutral configured controls (6 male, median age 29.5 ± 4.2 years) with normal sleep history were examined. Patients and controls were evaluated for apnoea-hypopnoea index (AHI), respiratory disturbance index (RDI), oxygen desaturation index (ODI), snoring volume (dB), total sleep time (TST) and REM-percentage (REM). Airway volumetry was measured via CBCT. Individual user experience for PAT was assessed using the User Experience Questionnaire (UEQ). RESULTS Patients had significantly higher respiratory scores than controls. AHI increased 4.6-fold (p = .006), RDI 2.5-fold (p = .008) and ODI 6.4-fold (p < .001). Oropharyngeal volumes showed a 30% decrease (p = .003). dB, TST and REM showed no significant differences. AHI (r = -.51; p = .005), ODI (r = -.60; p < .001) and RDI (r = -.45; p = .016) correlated negatively with pharyngeal volume. Wits appraisal correlated negatively with oropharyngeal volume (r = -.47; p = .010) and positively with AHI (r = .41; p = .03) and ODI (r = .49; p = .007). dB and TST (r = -.49; p = .008) and REM and RDI (r = -.43; p = .02) correlated negatively. UEQ-KPI (2.17 ± 0.24) confirmed excellent usability of PAT. CONCLUSION Patients with mandibular retrognathia and abnormal sleep history showed significantly higher respiratory indices and smaller oropharyngeal volumes than neutrally configured controls. The dygnathia severity directly influenced the risk of obstructive sleep apnoea.
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Affiliation(s)
- Bernhard Wiechens
- Department of Orthodontics, University Medical Center Goettingen, Goettingen, Germany
| | - Sören J Backhaus
- Department of Cardiology and Pneumology University Medical Center Goettingen, Goettingen, Germany
- German Center for Cardiovascular Research (DZHK), Partner Site Goettingen, Goettingen, Germany
| | - David Oestreicher
- Department of Otorhinolaryngology, University Medical Center Goettingen, Goettingen, Germany
| | - Dirk Beutner
- Department of Otorhinolaryngology, University Medical Center Goettingen, Goettingen, Germany
| | - Henning Schliephake
- Department of Oral and Maxillofacial Surgery, University Medical Center Goettingen, Goettingen, Germany
| | | | - Phillipp Brockmeyer
- Department of Oral and Maxillofacial Surgery, University Medical Center Goettingen, Goettingen, Germany
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Rodrigues J, Evangelopoulos E, Anagnostopoulos I, Sachdev N, Ismail A, Samsudin R, Khalaf K, Pattanaik S, Shetty SR. Impact of class II and class III skeletal malocclusion on pharyngeal airway dimensions: A systematic literature review and meta-analysis. Heliyon 2024; 10:e27284. [PMID: 38501020 PMCID: PMC10945137 DOI: 10.1016/j.heliyon.2024.e27284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 02/24/2024] [Accepted: 02/27/2024] [Indexed: 03/20/2024] Open
Abstract
Background This study is a pioneer systematic review and meta-analysis aimed at comparing the influence of Class II and Class III skeletal malocclusions on pharyngeal airway dimensions. It stands as the inaugural comprehensive assessment to collate and analyze the disparate findings from previously published articles on this topic. The objective of this study was to identify published articles that compare the effects of class II and class III skeletal malocclusion on the pharyngeal airway dimensions. Methods An all-inclusive search for existing published studies was done to identify peer-reviewed scholarly articles that compared the influence of class II and class III skeletal malocclusion on pharyngeal airway dimensions. The search was done via five electronic databases: Cochrane Library, EMBASE, Scopus, Web of Science, and PubMed. Screening of the articles was done and the eligible studies were critically assessed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist. Results The initial search yielded 476 potential articles of which, nine were finally included in this study for a total of 866 patients. Three studies were of cross-sectional design and six were of retrospective study design. Following a critical analysis and review of the studies, class III skeletal malocclusion had significantly larger volume and area measurements compared to class II skeletal malocclusion. Conclusion Research in the field of literature has established that variations in skeletal classifications have a discernible effect on the size of the pharyngeal airways. With the advancement of skeletal malocclusions to a class III, there is an observed increase in both the volume and cross-sectional area of the airways.
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Affiliation(s)
- Jensyll Rodrigues
- College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | | | | | | | - Ahmad Ismail
- College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Rani Samsudin
- College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Khaled Khalaf
- Institute of Dentistry, University of Aberdeen, United Kingdom
| | - Snigdha Pattanaik
- College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Shishir Ram Shetty
- College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
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Hadzic A, Urschler M, Press JNA, Riedl R, Rugani P, Štern D, Kirnbauer B. Evaluating a Periapical Lesion Detection CNN on a Clinically Representative CBCT Dataset-A Validation Study. J Clin Med 2023; 13:197. [PMID: 38202204 PMCID: PMC10779652 DOI: 10.3390/jcm13010197] [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: 11/20/2023] [Revised: 12/20/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024] Open
Abstract
The aim of this validation study was to comprehensively evaluate the performance and generalization capability of a deep learning-based periapical lesion detection algorithm on a clinically representative cone-beam computed tomography (CBCT) dataset and test for non-inferiority. The evaluation involved 195 CBCT images of adult upper and lower jaws, where sensitivity and specificity metrics were calculated for all teeth, stratified by jaw, and stratified by tooth type. Furthermore, each lesion was assigned a periapical index score based on its size to enable a score-based evaluation. Non-inferiority tests were conducted with proportions of 90% for sensitivity and 82% for specificity. The algorithm achieved an overall sensitivity of 86.7% and a specificity of 84.3%. The non-inferiority test indicated the rejection of the null hypothesis for specificity but not for sensitivity. However, when excluding lesions with a periapical index score of one (i.e., very small lesions), the sensitivity improved to 90.4%. Despite the challenges posed by the dataset, the algorithm demonstrated promising results. Nevertheless, further improvements are needed to enhance the algorithm's robustness, particularly in detecting very small lesions and the handling of artifacts and outliers commonly encountered in real-world clinical scenarios.
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Affiliation(s)
- Arnela Hadzic
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036 Graz, Austria; (A.H.); (R.R.)
| | - Martin Urschler
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036 Graz, Austria; (A.H.); (R.R.)
| | - Jan-Niclas Aaron Press
- Division of Oral Surgery and Orthodontics, Medical University of Graz, 8010 Graz, Austria (P.R.); (B.K.)
| | - Regina Riedl
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036 Graz, Austria; (A.H.); (R.R.)
| | - Petra Rugani
- Division of Oral Surgery and Orthodontics, Medical University of Graz, 8010 Graz, Austria (P.R.); (B.K.)
| | - Darko Štern
- Institute of Computer Graphics and Vision, Graz University of Technology, 8010 Graz, Austria
| | - Barbara Kirnbauer
- Division of Oral Surgery and Orthodontics, Medical University of Graz, 8010 Graz, Austria (P.R.); (B.K.)
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Amasya H, Alkhader M, Serindere G, Futyma-Gąbka K, Aktuna Belgin C, Gusarev M, Ezhov M, Różyło-Kalinowska I, Önder M, Sanders A, Costa ALF, de Castro Lopes SLP, Orhan K. Evaluation of a Decision Support System Developed with Deep Learning Approach for Detecting Dental Caries with Cone-Beam Computed Tomography Imaging. Diagnostics (Basel) 2023; 13:3471. [PMID: 37998607 PMCID: PMC10669958 DOI: 10.3390/diagnostics13223471] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/12/2023] [Accepted: 11/16/2023] [Indexed: 11/25/2023] Open
Abstract
This study aims to investigate the effect of using an artificial intelligence (AI) system (Diagnocat, Inc., San Francisco, CA, USA) for caries detection by comparing cone-beam computed tomography (CBCT) evaluation results with and without the software. 500 CBCT volumes are scored by three dentomaxillofacial radiologists for the presence of caries separately on a five-point confidence scale without and with the aid of the AI system. After visual evaluation, the deep convolutional neural network (CNN) model generated a radiological report and observers scored again using AI interface. The ground truth was determined by a hybrid approach. Intra- and inter-observer agreements are evaluated with sensitivity, specificity, accuracy, and kappa statistics. A total of 6008 surfaces are determined as 'presence of caries' and 13,928 surfaces are determined as 'absence of caries' for ground truth. The area under the ROC curve of observer 1, 2, and 3 are found to be 0.855/0.920, 0.863/0.917, and 0.747/0.903, respectively (unaided/aided). Fleiss Kappa coefficients are changed from 0.325 to 0.468, and the best accuracy (0.939) is achieved with the aided results. The radiographic evaluations performed with aid of the AI system are found to be more compatible and accurate than unaided evaluations in the detection of dental caries with CBCT images.
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Affiliation(s)
- Hakan Amasya
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Istanbul University-Cerrahpaşa, Istanbul 34320, Türkiye;
- CAST (Cerrahpasa Research, Simulation and Design Laboratory), Istanbul University-Cerrahpaşa, Istanbul 34320, Türkiye
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul 34220, Türkiye
| | - Mustafa Alkhader
- Department of Oral Medicine and Oral Surgery, Faculty of Dentistry, Jordan University of Science and Technology, Irbid 22110, Jordan;
| | - Gözde Serindere
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Mustafa Kemal University, Hatay 31060, Türkiye; (G.S.); (C.A.B.)
| | - Karolina Futyma-Gąbka
- Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, 20-093 Lublin, Poland; (K.F.-G.); or (I.R.-K.)
| | - Ceren Aktuna Belgin
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Mustafa Kemal University, Hatay 31060, Türkiye; (G.S.); (C.A.B.)
| | - Maxim Gusarev
- Diagnocat, Inc., San Francisco, CA 94102, USA; (M.G.); (M.E.); (A.S.)
| | - Matvey Ezhov
- Diagnocat, Inc., San Francisco, CA 94102, USA; (M.G.); (M.E.); (A.S.)
| | - Ingrid Różyło-Kalinowska
- Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, 20-093 Lublin, Poland; (K.F.-G.); or (I.R.-K.)
| | - Merve Önder
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 0600, Türkiye;
| | - Alex Sanders
- Diagnocat, Inc., San Francisco, CA 94102, USA; (M.G.); (M.E.); (A.S.)
| | - Andre Luiz Ferreira Costa
- Postgraduate Program in Dentistry, Cruzeiro do Sul University (UNICSUL), São Paulo 08060-070, SP, Brazil;
| | - Sérgio Lúcio Pereira de Castro Lopes
- Science and Technology Institute, Department of Diagnosis and Surgery, São Paulo State University (UNESP), São José dos Campos 01049-010, SP, Brazil;
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 0600, Türkiye;
- Research Center (MEDITAM), Ankara University Medical Design Application, Ankara 06560, Türkiye
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, 1088 Budapest, Hungary
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Chen H, Lv T, Luo Q, Li L, Wang Q, Li Y, Zhou D, Emami E, Schmittbuhl M, van der Stelt P, Huynh N. Reliability and accuracy of a semi-automatic segmentation protocol of the nasal cavity using cone beam computed tomography in patients with sleep apnea. Clin Oral Investig 2023; 27:6813-6821. [PMID: 37796336 DOI: 10.1007/s00784-023-05295-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 09/27/2023] [Indexed: 10/06/2023]
Abstract
OBJECTIVES The objectives of this study included using the cone beam computed tomography (CBCT) technology to assess: (1) intra- and inter-observer reliability of the volume measurement of the nasal cavity; (2) the accuracy of the segmentation protocol for evaluation of the nasal cavity. MATERIALS AND METHODS This study used test-retest reliability and accuracy methods within two different population sample groups, from Eastern Asia and North America. Thirty obstructive sleep apnea (OSA) patients were randomly selected from administrative and research oral health data archived at two dental faculties in China and Canada. To assess the reliability of the protocol, two observers performed nasal cavity volume measurement twice with a 10-day interval, using Amira software (v4.1, Visage Imaging Inc., Carlsbad, CA). The accuracy study used a computerized tomography (CT) scan of an OSA patient, who was not included in the study sample, to fabricate an anthropomorphic phantom of the nasal cavity volume with known dimensions (18.9 ml, gold standard). This phantom was scanned using one NewTom 5G (QR systems, Verona, Italy) CBCT scanner. The nasal cavity was segmented based on CBCT images and converted into standard tessellation language (STL) models. The volume of the nasal cavity was measured on the acquired STL models (18.99 ± 0.066 ml). RESULTS The intra-observer and inter-observer intraclass correlation coefficients for the volume measurement of the nasal cavity were 0.980-0.997 and 0.948-0.992 consecutively. The nasal cavity volume measurement was overestimated by 1.1%-3.1%, compared to the gold standard. CONCLUSIONS The semi-automatic segmentation protocol of the nasal cavity in patients with sleep apnea and by using cone beam computed tomography is reliable and accurate. CLINICAL RELEVANCE This study provides a reliable and accurate protocol for segmentation of nasal cavity, which will facilitate the clinician to analyze the images within nasoethmoidal region.
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Affiliation(s)
- Hui Chen
- Department of Orthodontics, School and Hospital of Stomatology, Shandong University, Shandong Key Laboratory of Oral Tissue Regeneration, Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Shandong Provincial Clinical Research Center for Oral Diseases, Cheeloo College of Medicine, Shandong University, Jinan, 250100, Shandong, China.
| | - Tao Lv
- Department of Orthodontics, School and Hospital of Stomatology, Shandong University, Shandong Key Laboratory of Oral Tissue Regeneration, Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Shandong Provincial Clinical Research Center for Oral Diseases, Cheeloo College of Medicine, Shandong University, Jinan, 250100, Shandong, China.
| | - Qing Luo
- Hospital of Stomatology, Ningbo, Zhejiang, China
| | - Lei Li
- Centre for Advanced Jet Engineering Technologies (CaJET), School of Mechanical Engineering, Key Laboratory of High-Efficiency and Clean Mechanical Manufacture at Shandong University, Ministry of Education, National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan, China
| | - Qing Wang
- Department of Orthodontics, Stomatological Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Yanzhong Li
- Department of Otorhinolaryngology, NHC Key Laboratory of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, China
| | - Debo Zhou
- Key Laboratory of Special Functional Aggregated Materials, Ministry of Education, School of Chemistry and Chemical Engineering, Shandong University, Jinan, China
| | - Elham Emami
- Faculty of Dentistry, McGill University, Montreal, Quebec, Canada
| | | | - Paul van der Stelt
- Department of Oral Radilology, Academic Centre for Dentistry Amsterdam, Amsterdam, the Netherlands
| | - Nelly Huynh
- Faculty of Dental Medicine, Université de Montréal, Montreal, Quebec, Canada
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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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Moufti MA, Trabulsi N, Ghousheh M, Fattal T, Ashira A, Danishvar S. Developing an Artificial Intelligence Solution to Autosegment the Edentulous Mandibular Bone for Implant Planning. Eur J Dent 2023; 17:1330-1337. [PMID: 37172946 PMCID: PMC10756774 DOI: 10.1055/s-0043-1764425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2023] Open
Abstract
OBJECTIVE Dental implants are considered the optimum solution to replace missing teeth and restore the mouth's function and aesthetics. Surgical planning of the implant position is critical to avoid damage to vital anatomical structures; however, the manual measurement of the edentulous (toothless) bone on cone beam computed tomography (CBCT) images is time-consuming and is subject to human error. An automated process has the potential to reduce human errors and save time and costs. This study developed an artificial intelligence (AI) solution to identify and delineate edentulous alveolar bone on CBCT images before implant placement. MATERIALS AND METHODS After obtaining the ethical approval, CBCT images were extracted from the database of the University Dental Hospital Sharjah based on predefined selection criteria. Manual segmentation of the edentulous span was done by three operators using ITK-SNAP software. A supervised machine learning approach was undertaken to develop a segmentation model on a "U-Net" convolutional neural network (CNN) in the Medical Open Network for Artificial Intelligence (MONAI) framework. Out of the 43 labeled cases, 33 were utilized to train the model, and 10 were used for testing the model's performance. STATISTICAL ANALYSIS The degree of 3D spatial overlap between the segmentation made by human investigators and the model's segmentation was measured by the dice similarity coefficient (DSC). RESULTS The sample consisted mainly of lower molars and premolars. DSC yielded an average value of 0.89 for training and 0.78 for testing. Unilateral edentulous areas, comprising 75% of the sample, resulted in a better DSC (0.91) than bilateral cases (0.73). CONCLUSION Segmentation of the edentulous spans on CBCT images was successfully conducted by machine learning with good accuracy compared to manual segmentation. Unlike traditional AI object detection models that identify objects present in the image, this model identifies missing objects. Finally, challenges in data collection and labeling are discussed, together with an outlook at the prospective stages of a larger project for a complete AI solution for automated implant planning.
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Affiliation(s)
- Mohammad Adel Moufti
- Department of Preventive and Restorative Dentistry, University of Sharjah, United Arab Emirates
| | - Nuha Trabulsi
- Department of Preventive and Restorative Dentistry, University of Sharjah, United Arab Emirates
| | - Marah Ghousheh
- Department of Preventive and Restorative Dentistry, University of Sharjah, United Arab Emirates
| | - Tala Fattal
- Department of Preventive and Restorative Dentistry, University of Sharjah, United Arab Emirates
| | - Ali Ashira
- Department of Preventive and Restorative Dentistry, University of Sharjah, United Arab Emirates
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Jin S, Han H, Huang Z, Xiang Y, Du M, Hua F, Guan X, Liu J, Chen F, He H. Automatic three-dimensional nasal and pharyngeal airway subregions identification via Vision Transformer. J Dent 2023; 136:104595. [PMID: 37343616 DOI: 10.1016/j.jdent.2023.104595] [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: 04/06/2023] [Revised: 06/06/2023] [Accepted: 06/19/2023] [Indexed: 06/23/2023] Open
Abstract
OBJECTIVES Upper airway assessment requires a fully-automated segmentation system for complete or sub-regional identification. This study aimed to develop a novel Deep Learning (DL) model for accurate segmentation of the upper airway and achieve entire and subregional identification. METHODS Fifty cone-beam computed tomography (CBCT) scans, including 24,502 slices, were labelled as the ground truth by one orthodontist and two otorhinolaryngologists. A novel model, a lightweight multitask network based on the Swin Transformer and U-Net, was built for automatic segmentation of the entire upper airway and subregions. Segmentation performance was evaluated using Precision, Recall, Dice similarity coefficient (DSC) and Intersection over union (IoU). The clinical implications of the precision errors were quantitatively analysed, and comparisons between the AI model and Dolphin software were conducted. RESULTS Our model achieved good performance with a precision of 85.88-94.25%, recall of 93.74-98.44%, DSC of 90.95-96.29%, IoU of 83.68-92.85% in the overall and subregions of three-dimensional (3D) upper airway, and a precision of 91.22-97.51%, recall of 90.70-97.62%, DSC of 90.92-97.55%, and IoU of 83.41-95.29% in the subregions of two-dimensional (2D) crosssections. Discrepancies in volume and area caused by precision errors did not affect clinical outcomes. Both our AI model and the Dolphin software provided clinically acceptable consistency for pharyngeal airway assessments. CONCLUSION The novel DL model not only achieved segmentation of the entire upper airway, including the nasal cavity and subregion identification, but also performed exceptionally well, making it well suited for 3D upper airway assessment from the nasal cavity to the hypopharynx, especially for intricate structures. CLINICAL SIGNIFICANCE This system provides insights into the aetiology, risk, severity, treatment effect, and prognosis of dentoskeletal deformities and obstructive sleep apnea. It achieves rapid assessment of the entire upper airway and its subregions, making airway management-an integral part of orthodontic treatment, orthognathic surgery, and ENT surgery-easier.
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Affiliation(s)
- Suhan Jin
- Department of Orthodontics, Hubei-MOST KLOS & KLOBM, School & Hospital of Stomatology, Wuhan University,Wuhan, China; Department of Orthodontics, Affiliated Stomatological Hospital of Zunyi Medical University, Zunyi, China
| | - Haojie Han
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China
| | - Zhiqun Huang
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yuandi Xiang
- Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mingyuan Du
- Department of Orthodontics, Hubei-MOST KLOS & KLOBM, School & Hospital of Stomatology, Wuhan University,Wuhan, China
| | - Fang Hua
- Department of Orthodontics, Hubei-MOST KLOS & KLOBM, School & Hospital of Stomatology, Wuhan University,Wuhan, China
| | - Xiaoyan Guan
- Department of Orthodontics, Affiliated Stomatological Hospital of Zunyi Medical University, Zunyi, China
| | - Jianguo Liu
- School of Stomatology, Zunyi Medical University, Zunyi, China; Special Key Laboratory of Oral Diseases Research, Higher Education Institution, Zunyi, China
| | - Fang Chen
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, China.
| | - Hong He
- Department of Orthodontics, Hubei-MOST KLOS & KLOBM, School & Hospital of Stomatology, Wuhan University,Wuhan, China.
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Sánchez-Bonaste A, Merchante LFS, Gónzalez-Bravo C, Carnicero A. Systematic measuring cortical thickness in tibiae for bio-mechanical analysis. Comput Biol Med 2023; 163:107123. [PMID: 37343467 DOI: 10.1016/j.compbiomed.2023.107123] [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: 02/10/2023] [Revised: 05/08/2023] [Accepted: 05/30/2023] [Indexed: 06/23/2023]
Abstract
BACKGROUND AND OBJECTIVE Measuring the thickness of cortical bone tissue helps diagnose bone diseases or monitor the progress of different treatments. This type of measurement can be performed visually from CAT images by a radiologist or by semi-automatic algorithms from Hounsfield values. This article proposes a mechanism capable of measuring thickness over the entire bone surface, aligning and orienting all the images in the same direction to have comparable references and reduce human intervention to a minimum. The objective is to batch process large numbers of patients' CAT images obtaining thicknesses profiles of their cortical tissue to be used in many applications. METHODS Classical morphological and Deep Learning segmentation is used to extract the area of interest, filtering and interpolation to clean the bones and contour detection and Signed Distance Functions to measure the cortical Thickness. The alignment of the set of bones is achieved by detecting their longitudinal direction, and the orientation is performed by computing their principal component of the center of mass slice. RESULTS The method processed in an unattended manner 67% of the patients in the first run and 100% in the second run. The difference in the thickness values between the values provided by the algorithm and the measures done by a radiologist was, on average, 0.25 millimetres with a standard deviation of 0.2. CONCLUSION Measuring the cortical thickness of a bone would allow us to prepare accurate traumatological surgeries or study their structural properties. Obtaining thickness profiles of an extensive set of patients opens the way for numerous studies to be carried out to find patterns between bone thickness and the patients' medical, social or demographic variables.
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Affiliation(s)
- Alberto Sánchez-Bonaste
- ICAI School of Engineering, Comillas Pontifical University, Alberto Aguilera 25, 28015, Madrid, Spain
| | - Luis F S Merchante
- MOBIOS Lab, Institute for Research in Technology, Comillas Pontifical University, Sta Cruz de Marcenado 26, 28015, Madrid, Spain
| | - Carlos Gónzalez-Bravo
- ICAI School of Engineering, Comillas Pontifical University, Alberto Aguilera 25, 28015, Madrid, Spain
| | - Alberto Carnicero
- MOBIOS Lab, Institute for Research in Technology, Comillas Pontifical University, Sta Cruz de Marcenado 26, 28015, Madrid, Spain.
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Meng X, Mao F, Mao Z, Xue Q, Jia J, Hu M. Multi-stage Unet segmentation and automatic measurement of pharyngeal airway based on lateral cephalograms. J Dent 2023; 136:104637. [PMID: 37506811 DOI: 10.1016/j.jdent.2023.104637] [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: 12/20/2022] [Revised: 07/17/2023] [Accepted: 07/25/2023] [Indexed: 07/30/2023] Open
Abstract
OBJECTIVES Orthodontic treatment profoundly impact the pharyngeal airway (PA) of patients. Airway examination is an integral part of daily orthodontic diagnosis, and lateral cephalograms (LC) are reliable to reveal PA structures. This study attempted to develop a simple method to help clinicians make a preliminary judgement of patients' PA conditions and assess the impact of orthodontic treatment on their airways. METHODS LCs of 764 patients were used to train a multistage unit segmentation model. Another 130 images were used to validate the model and more 130 images were used to test the model. RESULTS Unet was used as the backbone, with a mean dice value of 0.8180, precision of 0.8393, and recall of 0.8188. Furthermore, we identified seven key points and measured related indices. The length of the line separating the nasopharynx and oropharynx and the line separating the oropharynx and hypopharynx were manually measured thrice and the average values was compared. The intraclass correlation coefficient (ICC) for the two lines was 0.599 and 0.855. Then, we performed a single linear regression analysis, which indicated a strong correlation between the predictions and measurements for the two lines. CONCLUSIONS This method is reliable for segmenting three regions (nasopharynx, oropharynx, and hypopharynx) of the PA and calculating related indices. However, the predictions obtained from this model still have errors, and it is necessary for clinical practitioners to assess and adjust the predictions. CLINICAL SIGNIFICANCE Our model can help orthodontists formulate personalised treatment plans and evaluate the risk of airway stenosis during orthodontic treatment. This method may mark the beginning of a new and simpler approach for PA obstruction detection, specifically tailored to orthodontic patients.
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Affiliation(s)
- Xiangquan Meng
- School of Mathematics, Jilin University, Changchun 130012, China
| | - Feng Mao
- Hospital of Stomatology, Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun 130021, China
| | - Zhi Mao
- Hospital of Stomatology, Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun 130021, China
| | - Qing Xue
- Hospital of Stomatology, Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun 130021, China
| | - Jiwei Jia
- School of Mathematics, Jilin University, Changchun 130012, China; National Applied Mathematical Center (Jilin), Changchun 130012, China
| | - Min Hu
- Hospital of Stomatology, Key Laboratory of Pathobiology, Ministry of Education, Jilin University, Changchun 130021, China.
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11
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Chu G, Zhang R, He Y, Ng CH, Gu M, Leung YY, He H, Yang Y. Deep Learning Models for Automatic Upper Airway Segmentation and Minimum Cross-Sectional Area Localisation in Two-Dimensional Images. Bioengineering (Basel) 2023; 10:915. [PMID: 37627800 PMCID: PMC10451171 DOI: 10.3390/bioengineering10080915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/19/2023] [Accepted: 07/31/2023] [Indexed: 08/27/2023] Open
Abstract
OBJECTIVE To develop and validate convolutional neural network algorithms for automatic upper airway segmentation and minimum cross-sectional area (CSAmin) localisation in two-dimensional (2D) radiographic airway images. MATERIALS AND METHODS Two hundred and one 2D airway images acquired using cone-beam computed tomography (CBCT) scanning were randomly assigned to a test group (n = 161) to train artificial intelligence (AI) models and a validation group (n = 40) to evaluate the accuracy of AI processing. Four AI models, UNet18, UNet36, DeepLab50 and DeepLab101, were trained to automatically segment the upper airway 2D images in the test group. Precision, recall, Intersection over Union, the dice similarity coefficient and size difference were used to evaluate the performance of the AI-driven segmentation models. The CSAmin height in each image was manually determined using three-dimensional CBCT data. The nonlinear mathematical morphology technique was used to calculate the CSAmin level. Height errors were assessed to evaluate the CSAmin localisation accuracy in the validation group. The time consumed for airway segmentation and CSAmin localisation was compared between manual and AI processing methods. RESULTS The precision of all four segmentation models exceeded 90.0%. No significant differences were found in the accuracy of any AI models. The consistency of CSAmin localisation in specific segments between manual and AI processing was 0.944. AI processing was much more efficient than manual processing in terms of airway segmentation and CSAmin localisation. CONCLUSIONS We successfully developed and validated a fully automatic AI-driven system for upper airway segmentation and CSAmin localisation using 2D radiographic airway images.
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Affiliation(s)
- Guang Chu
- Orthodontics, Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (G.C.)
| | - Rongzhao Zhang
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Yingqing He
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Chun Hown Ng
- Orthodontics, Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (G.C.)
| | - Min Gu
- Orthodontics, Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (G.C.)
| | - Yiu Yan Leung
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Hong He
- Department of Orthodontics, The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST), Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan 430072, China
| | - Yanqi Yang
- Orthodontics, Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (G.C.)
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12
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Maken P, Gupta A, Gupta MK. A systematic review of the techniques for automatic segmentation of the human upper airway using volumetric images. Med Biol Eng Comput 2023; 61:1901-1927. [PMID: 37248380 DOI: 10.1007/s11517-023-02842-x] [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: 09/22/2022] [Accepted: 04/20/2023] [Indexed: 05/31/2023]
Abstract
The human upper airway is comprised of many anatomical volumes. The obstructions in the upper airway volumes are needed to be diagnosed which requires volumetric segmentation. Manual segmentation is time-consuming and requires expertise in the field. Automatic segmentation provides reliable results and also saves time and effort for the expert. The objective of this study is to systematically review the literature to study various techniques used for the automatic segmentation of the human upper airway regions in volumetric images. PRISMA guidelines were followed to conduct the systematic review. Four online databases Scopus, Google Scholar, PubMed, and JURN were used for the searching of the relevant papers. The relevant papers were shortlisted using inclusion and exclusion eligibility criteria. Three review questions were made and explored to find their answers. The best technique among all the literature studies based on the Dice coefficient and precision was identified and justified through the analysis. This systematic review provides insight to the researchers so that they shall be able to overcome the prominent issues in the field identified from the literature. The outcome of the review is based on several parameters, e.g., accuracy, techniques, challenges, datasets, and segmentation of different sub-regions. Flowchart of the search process as per PRISMA guidelines along with inclusion and exclusion criteria.
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Affiliation(s)
- Payal Maken
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, India
| | - Abhishek Gupta
- Biomedical Application Division, CSIR-Central Scientific Instruments Organisation, Chandigarh, 160030, India.
| | - Manoj Kumar Gupta
- School of Computer Science and Engineering, Shri Mata Vaishno Devi University, Katra, India
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13
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Fan W, Zhang J, Wang N, Li J, Hu L. The Application of Deep Learning on CBCT in Dentistry. Diagnostics (Basel) 2023; 13:2056. [PMID: 37370951 DOI: 10.3390/diagnostics13122056] [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: 05/11/2023] [Revised: 06/06/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
Cone beam computed tomography (CBCT) has become an essential tool in modern dentistry, allowing dentists to analyze the relationship between teeth and the surrounding tissues. However, traditional manual analysis can be time-consuming and its accuracy depends on the user's proficiency. To address these limitations, deep learning (DL) systems have been integrated into CBCT analysis to improve accuracy and efficiency. Numerous DL models have been developed for tasks such as automatic diagnosis, segmentation, classification of teeth, inferior alveolar nerve, bone, airway, and preoperative planning. All research articles summarized were from Pubmed, IEEE, Google Scholar, and Web of Science up to December 2022. Many studies have demonstrated that the application of deep learning technology in CBCT examination in dentistry has achieved significant progress, and its accuracy in radiology image analysis has reached the level of clinicians. However, in some fields, its accuracy still needs to be improved. Furthermore, ethical issues and CBCT device differences may prohibit its extensive use. DL models have the potential to be used clinically as medical decision-making aids. The combination of DL and CBCT can highly reduce the workload of image reading. This review provides an up-to-date overview of the current applications of DL on CBCT images in dentistry, highlighting its potential and suggesting directions for future research.
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Affiliation(s)
- Wenjie Fan
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jiaqi Zhang
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Nan Wang
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jia Li
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Li Hu
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
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Kim DY, Woo S, Roh JY, Choi JY, Kim KA, Cha JY, Kim N, Kim SJ. Subregional pharyngeal changes after orthognathic surgery in skeletal Class III patients analyzed by convolutional neural networks-based segmentation. J Dent 2023:104565. [PMID: 37308053 DOI: 10.1016/j.jdent.2023.104565] [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: 01/24/2023] [Revised: 05/03/2023] [Accepted: 05/27/2023] [Indexed: 06/14/2023] Open
Abstract
OBJECTIVES To evaluate the accuracy of fully automatic segmentation of pharyngeal volume of interests (VOIs) before and after orthognathic surgery in skeletal Class III patients using a convolutional neural network (CNN) model and to investigate the clinical applicability of artificial intelligence for quantitative evaluation of treatment changes in pharyngeal VOIs. METHODS 310 cone-beam computed tomography (CBCT) images were divided into a training set (n=150), validation set (n=40), and test set (n=120). The test datasets comprised matched pairs of pre- and posttreatment images of 60 skeletal Class III patients (mean age 23.1±5.0 years; ANB<-2⁰) who underwent bimaxillary orthognathic surgery with orthodontic treatment. A 3D U-Net CNNs model was applied for fully automatic segmentation and measurement of subregional pharyngeal volumes of pretreatment (T0) and posttreatment (T1) scans. The model's accuracy was compared to semi-automatic segmentation outcomes by humans using the dice similarity coefficient (DSC) and volume similarity (VS). The correlation between surgical skeletal changes and model accuracy was obtained. RESULTS The proposed model achieved high performance of subregional pharyngeal segmentation on both T0 and T1 images, representing a significant T1-T0 difference of DSC only in the nasopharynx. Region-specific differences among pharyngeal VOIs, which were observed at T0, disappeared on the T1 images. The decreased DSC of nasopharyngeal segmentation after treatment was weakly correlated with the amount of maxillary advancement. There was no correlation between the mandibular setback amount and model accuracy. CONCLUSIONS The proposed model offers fast and accurate subregional pharyngeal segmentation on both pretreatment and posttreatment CBCT images in skeletal Class III patients. CLINICAL SIGNIFICANCE We elucidated the clinical applicability of the CNNs model to quantitatively evaluate subregional pharyngeal changes after surgical-orthodontic treatment, which offers a basis for developing a fully integrated multiclass CNNs model to predict pharyngeal responses after dentoskeletal treatments.
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Affiliation(s)
- Dong-Yul Kim
- Department of Dentistry, Graduate School, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Republic of Korea
| | - Seoyeon Woo
- Department of Convergence Medicine, Asan Medical Institute of Convergence, Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Jae-Yon Roh
- Department of Dentistry, Graduate School, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Republic of Korea
| | - Jin-Young Choi
- Department of Orthodontics, Kyung Hee University Dental Hospital, 23, Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Republic of Korea
| | - Kyung-A Kim
- Department of Orthodontics, School of Dentistry, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Republic of Korea
| | - Jung-Yul Cha
- Department of Orthodontics, The Institute of Craniofacial Deformity, College of Dentistry, Yonsei University, 50-1 Yonseiro, Seodaemun-gu, Seoul, 03722, Korea
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Republic of Korea
| | - Su-Jung Kim
- Department of Orthodontics, School of Dentistry, Kyung Hee University, 26, Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Republic of Korea.
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15
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Amasya H, Aydoğan T, Cesur E, Kemaloğlu Alagöz N, Uğurlu M, Bayrakdar İŞ, Orhan K. Using artificial intelligence models to evaluate envisaged points initially: A pilot study. Proc Inst Mech Eng H 2023:9544119231173165. [PMID: 37211725 DOI: 10.1177/09544119231173165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
The morphology of the finger bones in hand-wrist radiographs (HWRs) can be considered as a radiological skeletal maturity indicator, along with the other indicators. This study aims to validate the anatomical landmarks envisaged to be used for classification of the morphology of the phalanges, by developing classical neural network (NN) classifiers based on a sub-dataset of 136 HWRs. A web-based tool was developed and 22 anatomical landmarks were labeled on four region of interests (proximal (PP3), medial (MP3), distal (DP3) phalanges of the third and medial phalanx (MP5) of the fifth finger) and the epiphysis-diaphysis relationships were saved as "narrow,""equal,""capping" or "fusion" by three observers. In each region, 18 ratios and 15 angles were extracted using anatomical points. The data set is analyzed by developing two NN classifiers, without (NN-1) and with (NN-2) the 5-fold cross-validation. The performance of the models was evaluated with percentage of agreement, Cohen's (cκ) and Weighted (wκ) Kappa coefficients, precision, recall, F1-score and accuracy (statistically significance: p < 0.05). Method error was found to be in the range of cκ: 0.7-1. Overall classification performance of the models was changed between 82.14% and 89.29%. On average, performance of the NN-1 and NN-2 models were found to be 85.71% and 85.52%, respectively. The cκ and wκ of the NN-1 model were changed between -0.08 (p > 0.05) and 0.91 among regions. The average performance was found to be promising except the regions without adequate samples and the anatomical points are validated to be used in the future studies, initially.
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Affiliation(s)
- Hakan Amasya
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Istanbul University-Cerrahpaşa, Istanbul, Turkey
- CAST (Cerrahpasa Research, Simulation and Design Laboratory), Istanbul University-Cerrahpaşa, Istanbul, Turkey
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Turgay Aydoğan
- Faculty of Engineering, Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey
| | - Emre Cesur
- Faculty of Dentistry, Department of Orthodontics, Medipol Mega University Hospital, Istanbul, Turkey
| | - Nazan Kemaloğlu Alagöz
- Uluborlu Selahattin Karasoy Vocational School, Isparta University of Applied Sciences, Isparta, Turkey
| | - Mehmet Uğurlu
- Faculty of Dentistry, Department of Orthodontics, Eskişehir Osmangazi University, Eskisehir, Turkey
| | - İbrahim Şevki Bayrakdar
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Eskişehir Osmangazi University, Eskisehir, Turkey
| | - Kaan Orhan
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Ankara University, Ankara, Turkey
- Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey
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16
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Gardiyanoğlu E, Ünsal G, Akkaya N, Aksoy S, Orhan K. Automatic Segmentation of Teeth, Crown-Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls. Diagnostics (Basel) 2023; 13:diagnostics13081487. [PMID: 37189586 DOI: 10.3390/diagnostics13081487] [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: 12/22/2022] [Revised: 02/26/2023] [Accepted: 03/01/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND The aim of our study is to provide successful automatic segmentation of various objects on orthopantomographs (OPGs). METHODS 8138 OPGs obtained from the archives of the Department of Dentomaxillofacial Radiology were included. OPGs were converted into PNGs and transferred to the segmentation tool's database. All teeth, crown-bridge restorations, dental implants, composite-amalgam fillings, dental caries, residual roots, and root canal fillings were manually segmented by two experts with the manual drawing semantic segmentation technique. RESULTS The intra-class correlation coefficient (ICC) for both inter- and intra-observers for manual segmentation was excellent (ICC > 0.75). The intra-observer ICC was found to be 0.994, while the inter-observer reliability was 0.989. No significant difference was detected amongst observers (p = 0.947). The calculated DSC and accuracy values across all OPGs were 0.85 and 0.95 for the tooth segmentation, 0.88 and 0.99 for dental caries, 0.87 and 0.99 for dental restorations, 0.93 and 0.99 for crown-bridge restorations, 0.94 and 0.99 for dental implants, 0.78 and 0.99 for root canal fillings, and 0.78 and 0.99 for residual roots, respectively. CONCLUSIONS Thanks to faster and automated diagnoses on 2D as well as 3D dental images, dentists will have higher diagnosis rates in a shorter time even without excluding cases.
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Affiliation(s)
- Emel Gardiyanoğlu
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, 99138 Nicosia, Cyprus
| | - Gürkan Ünsal
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, 99138 Nicosia, Cyprus
- DESAM Institute, Near East University, 99138 Nicosia, Cyprus
| | - Nurullah Akkaya
- Department of Computer Engineering, Applied Artificial Intelligence Research Centre, Near East University, 99138 Nicosia, Cyprus
| | - Seçil Aksoy
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, 99138 Nicosia, Cyprus
| | - Kaan Orhan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, 06560 Ankara, Turkey
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Hung KF, Ai QYH, Wong LM, Yeung AWK, Li DTS, Leung YY. Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases. Diagnostics (Basel) 2022; 13:diagnostics13010110. [PMID: 36611402 PMCID: PMC9818323 DOI: 10.3390/diagnostics13010110] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 12/31/2022] Open
Abstract
The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models.
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Affiliation(s)
- Kuo Feng Hung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H. Ai
- Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lun M. Wong
- Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Dion Tik Shun Li
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Yiu Yan Leung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
- Correspondence:
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18
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Artificial Intelligence as an Aid in CBCT Airway Analysis: A Systematic Review. LIFE (BASEL, SWITZERLAND) 2022; 12:life12111894. [PMID: 36431029 PMCID: PMC9696726 DOI: 10.3390/life12111894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/10/2022] [Accepted: 11/11/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND The use of artificial intelligence (AI) in health sciences is becoming increasingly popular among doctors nowadays. This study evaluated the literature regarding the use of AI for CBCT airway analysis. To our knowledge, this is the first systematic review that examines the performance of artificial intelligence in CBCT airway analysis. METHODS Electronic databases and the reference lists of the relevant research papers were searched for published and unpublished literature. Study selection, data extraction, and risk of bias evaluation were all carried out independently and twice. Finally, five articles were chosen. RESULTS The results suggested a high correlation between the automatic and manual airway measurements indicating that the airway measurements may be automatically and accurately calculated from CBCT images. CONCLUSIONS According to the present literature, automatic airway segmentation can be used for clinical purposes. The main key findings of this systematic review are that the automatic airway segmentation is accurate in the measurement of the airway and, at the same time, appears to be fast and easy to use. However, the present literature is really limited, and more studies in the future providing high-quality evidence are needed.
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19
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Bayrakdar IS, Orhan K, Akarsu S, Çelik Ö, Atasoy S, Pekince A, Yasa Y, Bilgir E, Sağlam H, Aslan AF, Odabaş A. Deep-learning approach for caries detection and segmentation on dental bitewing radiographs. Oral Radiol 2022; 38:468-479. [PMID: 34807344 DOI: 10.1007/s11282-021-00577-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/09/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVES The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer. METHODS A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University. VGG-16 and U-Net implemented with PyTorch models were used for the detection and segmentation of caries lesions, respectively. RESULTS The sensitivity, precision, and F-measure rates for caries detection and caries segmentation were 0.84, 0.81; 0.84, 0.86; and 0.84, 0.84, respectively. Comparing to 5 different experienced observers and AI models on external radiographic dataset, AI models showed superiority to assistant specialists. CONCLUSION CNN-based AI algorithms can have the potential to detect and segmentation of dental caries accurately and effectively in bitewing radiographs. AI algorithms based on the deep-learning method have the potential to assist clinicians in routine clinical practice for quickly and reliably detecting the tooth caries. The use of these algorithms in clinical practice can provide to important benefit to physicians as a clinical decision support system in dentistry.
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Affiliation(s)
- Ibrahim Sevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey.
- Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir, Turkey.
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
- Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey
| | - Serdar Akarsu
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Özer Çelik
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
- Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey
| | - Samet Atasoy
- Department of Restorative Dentistry, Faculty of Dentistry, Ordu University, Ordu, Turkey
| | - Adem Pekince
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Karabuk University, Karabuk, Turkey
| | - Yasin Yasa
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ordu University, Ordu, Turkey
| | - Elif Bilgir
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey
| | - Hande Sağlam
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey
| | - Ahmet Faruk Aslan
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Alper Odabaş
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
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20
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Cho HN, Gwon E, Kim KA, Baek SH, Kim N, Kim SJ. Accuracy of convolutional neural networks-based automatic segmentation of pharyngeal airway sections according to craniofacial skeletal pattern. Am J Orthod Dentofacial Orthop 2022; 162:e53-e62. [PMID: 35654686 DOI: 10.1016/j.ajodo.2022.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 01/01/2022] [Accepted: 01/01/2022] [Indexed: 11/28/2022]
Abstract
INTRODUCTION This study aimed to evaluate a 3-dimensional (3D) U-Net-based convolutional neural networks model for the fully automatic segmentation of regional pharyngeal volume of interests (VOIs) in cone-beam computed tomography scans to compare the accuracy of the model performance across different skeletal patterns presenting with various pharyngeal dimensions. METHODS Two-hundred sixteen cone-beam computed tomography scans of adult patients were randomly divided into training (n = 100), validation (n = 16), and test (n = 100) datasets. We trained the 3D U-Net model for fully automatic segmentation of pharyngeal VOIs and their measurements: nasopharyngeal, velopharyngeal, glossopharyngeal, and hypopharyngeal sections as well as total pharyngeal airway space (PAS). The test datasets were subdivided according to the sagittal and vertical skeletal patterns. The segmentation performance was assessed by dice similarity coefficient, volumetric similarity, precision, and recall values, compared with the ground truth created by 1 expert's manual processing using semiautomatic software. RESULTS The proposed model achieved highly accurate performance, showing a mean dice similarity coefficient of 0.928 ± 0.023, the volumetric similarity of 0.928 ± 0.023, precision of 0.925 ± 0.030, and recall of 0.921 ± 0.029 for total PAS segmentation. The performance showed region-specific differences, revealing lower accuracy in the glossopharyngeal and hypopharyngeal sections than in the upper sections (P <0.001). However, the accuracy of model performance at each pharyngeal VOI showed no significant difference according to sagittal or vertical skeletal patterns. CONCLUSIONS The 3D-convolutional neural network performance for region-specific PAS analysis is promising to substitute for laborious and time-consuming manual analysis in every skeletal and pharyngeal pattern.
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Affiliation(s)
- Ha-Nul Cho
- Department of Dentistry, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Eunseo Gwon
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Kyung-A Kim
- Department of Dentistry, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Seung-Hak Baek
- Department of Orthodontics, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea; Department of Radiology, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
| | - Su-Jung Kim
- Department of Dentistry, Graduate School, Kyung Hee University, Seoul, South Korea.
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21
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Orhan K, Shamshiev M, Ezhov M, Plaksin A, Kurbanova A, Ünsal G, Gusarev M, Golitsyna M, Aksoy S, Mısırlı M, Rasmussen F, Shumilov E, Sanders A. AI-based automatic segmentation of craniomaxillofacial anatomy from CBCT scans for automatic detection of pharyngeal airway evaluations in OSA patients. Sci Rep 2022; 12:11863. [PMID: 35831451 PMCID: PMC9279304 DOI: 10.1038/s41598-022-15920-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 07/01/2022] [Indexed: 11/21/2022] Open
Abstract
This study aims to generate and also validate an automatic detection algorithm for pharyngeal airway on CBCT data using an AI software (Diagnocat) which will procure a measurement method. The second aim is to validate the newly developed artificial intelligence system in comparison to commercially available software for 3D CBCT evaluation. A Convolutional Neural Network-based machine learning algorithm was used for the segmentation of the pharyngeal airways in OSA and non-OSA patients. Radiologists used semi-automatic software to manually determine the airway and their measurements were compared with the AI. OSA patients were classified as minimal, mild, moderate, and severe groups, and the mean airway volumes of the groups were compared. The narrowest points of the airway (mm), the field of the airway (mm2), and volume of the airway (cc) of both OSA and non-OSA patients were also compared. There was no statistically significant difference between the manual technique and Diagnocat measurements in all groups (p > 0.05). Inter-class correlation coefficients were 0.954 for manual and automatic segmentation, 0.956 for Diagnocat and automatic segmentation, 0.972 for Diagnocat and manual segmentation. Although there was no statistically significant difference in total airway volume measurements between the manual measurements, automatic measurements, and DC measurements in non-OSA and OSA patients, we evaluated the output images to understand why the mean value for the total airway was higher in DC measurement. It was seen that the DC algorithm also measures the epiglottis volume and the posterior nasal aperture volume due to the low soft-tissue contrast in CBCT images and that leads to higher values in airway volume measurement.
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Affiliation(s)
- Kaan Orhan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey. .,Medical Design Application, and Research Center (MEDITAM), Ankara University, Ankara, Turkey. .,Department of Dental and Maxillofacial Radiodiagnostics, Medical University of Lublin, Lublin, Poland.
| | | | | | | | - Aida Kurbanova
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, Nicosia, Cyprus
| | - Gürkan Ünsal
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, Nicosia, Cyprus.,Research Center of Experimental Health Science (DESAM), Near East University, Nicosia, Cyprus
| | | | | | - Seçil Aksoy
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, Nicosia, Cyprus
| | - Melis Mısırlı
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, Nicosia, Cyprus
| | - Finn Rasmussen
- Internal Medicine Department Lunge Section, SVS Esbjerg, Esbjerg, Denmark.,Life Lung Health Center, Nicosia, Cyprus
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22
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Shujaat S, Jazil O, Willems H, Van Gerven A, Shaheen E, Politis C, Jacobs R. Automatic segmentation of the pharyngeal airway space with convolutional neural network. J Dent 2021; 111:103705. [PMID: 34077802 DOI: 10.1016/j.jdent.2021.103705] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 04/30/2021] [Accepted: 05/24/2021] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVES This study proposed and investigated the performance of a deep learning based three-dimensional (3D) convolutional neural network (CNN) model for automatic segmentation of the pharyngeal airway space (PAS). METHODS A dataset of 103 computed tomography (CT) and cone-beam CT (CBCT) scans was acquired from an orthognathic surgery patients database. The acquisition devices consisted of 1 CT (128-slice multi-slice spiral CT, Siemens Somatom Definition Flash, Siemens AG, Erlangen, Germany) and 2 CBCT devices (Promax 3D Max, Planmeca, Helsinki, Finland and Newtom VGi evo, Cefla, Imola, Italy) with different scanning parameters. A 3D CNN-based model (3D U-Net) was built for automatic segmentation of the PAS. The complete CT/CBCT dataset was split into three sets, training set (n = 48) for training the model based on the ground-truth observer-based manual segmentation, test set (n = 25) for getting the final performance of the model and validation set (n = 30) for evaluating the model's performance versus observer-based segmentation. RESULTS The CNN model was able to identify the segmented region with optimal precision (0.97±0.01) and recall (0.96±0.03). The maximal difference between the automatic segmentation and ground truth based on 95% hausdorff distance score was 0.98±0.74mm. The dice score of 0.97±0.02 confirmed the high similarity of the segmented region to the ground truth. The Intersection over union (IoU) metric was also found to be high (0.93±0.03). Based on the acquisition devices, Newtom VGi evo CBCT showed improved performance compared to the Promax 3D Max and CT device. CONCLUSION The proposed 3D U-Net model offered an accurate and time-efficient method for the segmentation of PAS from CT/CBCT images. CLINICAL SIGNIFICANCE The proposed method can allow clinicians to accurately and efficiently diagnose, plan treatment and follow-up patients with dento-skeletal deformities and obstructive sleep apnea which might influence the upper airway space, thereby further improving patient care.
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Affiliation(s)
- Sohaib Shujaat
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer 33, Leuven, Belgium.
| | - Omid Jazil
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer 33, Leuven, Belgium
| | | | | | - Eman Shaheen
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer 33, Leuven, Belgium
| | - Constantinus Politis
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer 33, Leuven, Belgium
| | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer 33, Leuven, Belgium; Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden
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Jeong SH, Yun JP, Yeom HG, Kim HK, Kim BC. Deep-Learning-Based Detection of Cranio-Spinal Differences between Skeletal Classification Using Cephalometric Radiography. Diagnostics (Basel) 2021; 11:591. [PMID: 33806132 PMCID: PMC8064489 DOI: 10.3390/diagnostics11040591] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/10/2021] [Accepted: 03/22/2021] [Indexed: 12/22/2022] Open
Abstract
The aim of this study was to reveal cranio-spinal differences between skeletal classification using convolutional neural networks (CNNs). Transverse and longitudinal cephalometric images of 832 patients were used for training and testing of CNNs (365 males and 467 females). Labeling was performed such that the jawbone was sufficiently masked, while the parts other than the jawbone were minimally masked. DenseNet was used as the feature extractor. Five random sampling crossvalidations were performed for two datasets. The average and maximum accuracy of the five crossvalidations were 90.43% and 92.54% for test 1 (evaluation of the entire posterior-anterior (PA) and lateral cephalometric images) and 88.17% and 88.70% for test 2 (evaluation of the PA and lateral cephalometric images obscuring the mandible). In this study, we found that even when jawbones of class I (normal mandible), class II (retrognathism), and class III (prognathism) are masked, their identification is possible through deep learning applied only in the cranio-spinal area. This suggests that cranio-spinal differences between each class exist.
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Affiliation(s)
- Seung Hyun Jeong
- Safety System Research Group, Korea Institute of Industrial Technology (KITECH), Gyeongsan 38408, Korea; (S.H.J.); (J.P.Y.)
| | - Jong Pil Yun
- Safety System Research Group, Korea Institute of Industrial Technology (KITECH), Gyeongsan 38408, Korea; (S.H.J.); (J.P.Y.)
| | - Han-Gyeol Yeom
- Department of Oral and Maxillofacial Radiology, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon 35233, Korea;
| | - Hwi Kang Kim
- Department of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon 35233, Korea;
| | - Bong Chul Kim
- Department of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon 35233, Korea;
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