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Kazimierczak W, Gawin G, Janiszewska-Olszowska J, Dyszkiewicz-Konwińska M, Nowicki P, Kazimierczak N, Serafin Z, Orhan K. Comparison of Three Commercially Available, AI-Driven Cephalometric Analysis Tools in Orthodontics. J Clin Med 2024; 13:3733. [PMID: 38999299 PMCID: PMC11242750 DOI: 10.3390/jcm13133733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 06/20/2024] [Accepted: 06/24/2024] [Indexed: 07/14/2024] Open
Abstract
Background: Cephalometric analysis (CA) is an indispensable diagnostic tool in orthodontics for treatment planning and outcome assessment. Manual CA is time-consuming and prone to variability. Methods: This study aims to compare the accuracy and repeatability of CA results among three commercial AI-driven programs: CephX, WebCeph, and AudaxCeph. This study involved a retrospective analysis of lateral cephalograms from a single orthodontic center. Automated CA was performed using the AI programs, focusing on common parameters defined by Downs, Ricketts, and Steiner. Repeatability was tested through 50 randomly reanalyzed cases by each software. Statistical analyses included intraclass correlation coefficients (ICC3) for agreement and the Friedman test for concordance. Results: One hundred twenty-four cephalograms were analyzed. High agreement between the AI systems was noted for most parameters (ICC3 > 0.9). Notable differences were found in the measurements of angle convexity and the occlusal plane, where discrepancies suggested different methodologies among the programs. Some analyses presented high variability in the results, indicating errors. Repeatability analysis revealed perfect agreement within each program. Conclusions: AI-driven cephalometric analysis tools demonstrate a high potential for reliable and efficient orthodontic assessments, with substantial agreement in repeated analyses. Despite this, the observed discrepancies and high variability in part of analyses underscore the need for standardization across AI platforms and the critical evaluation of automated results by clinicians, particularly in parameters with significant treatment implications.
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Affiliation(s)
- Wojciech Kazimierczak
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Grzegorz Gawin
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | | | | | - Paweł Nowicki
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Kaan Orhan
- Department of DentoMaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06500, Turkey
- Medical Design Application and Research Center (MEDITAM), Ankara University, Ankara 06500, Turkey
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, 1085 Budapest, Hungary
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Kazimierczak W, Kazimierczak N, Kędziora K, Szcześniak M, Serafin Z. Reliability of the AI-Assisted Assessment of the Proximity of the Root Apices to Mandibular Canal. J Clin Med 2024; 13:3605. [PMID: 38930132 PMCID: PMC11204399 DOI: 10.3390/jcm13123605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024] Open
Abstract
Background: This study evaluates the diagnostic accuracy of an AI-assisted tool in assessing the proximity of the mandibular canal (MC) to the root apices (RAs) of mandibular teeth using computed tomography (CT). Methods: This study involved 57 patients aged 18-30 whose CT scans were analyzed by both AI and human experts. The primary aim was to measure the closest distance between the MC and RAs and to assess the AI tool's diagnostic performance. The results indicated significant variability in RA-MC distances, with third molars showing the smallest mean distances and first molars the greatest. Diagnostic accuracy metrics for the AI tool were assessed at three thresholds (0 mm, 0.5 mm, and 1 mm). Results: The AI demonstrated high specificity but generally low diagnostic accuracy, with the highest metrics at the 0.5 mm threshold with 40.91% sensitivity and 97.06% specificity. Conclusions: This study underscores the limited potential of tested AI programs in reducing iatrogenic damage to the inferior alveolar nerve (IAN) during dental procedures. Significant differences in RA-MC distances between evaluated teeth were found.
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Affiliation(s)
- Wojciech Kazimierczak
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland; (K.K.); (Z.S.)
| | - Natalia Kazimierczak
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Kamila Kędziora
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland; (K.K.); (Z.S.)
| | - Marta Szcześniak
- Chair of Practical Clinical Dentistry, Department of Diagnostics, Poznań University of Medical Sciences, Fredry 10, 61-701 Poznań, Poland;
| | - Zbigniew Serafin
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland; (K.K.); (Z.S.)
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Tashkandi NE, Alnaqa NH, Al-Saif NM, Allam E. Accuracy of Gonial Angle Measurements Using Panoramic Imaging versus Lateral Cephalograms in Adults with Different Mandibular Divergence Patterns. J Multidiscip Healthc 2024; 17:1923-1929. [PMID: 38706500 PMCID: PMC11070157 DOI: 10.2147/jmdh.s463688] [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: 02/09/2024] [Accepted: 04/23/2024] [Indexed: 05/07/2024] Open
Abstract
Introduction Gonial angle is an important craniofacial parameter providing information about symmetry and vertical dimensions of the facial skeleton. It can be measured on panoramic radiographs and lateral cephalograms. Reliable assessment of the gonial angle is challenged by the superimpositions associated with lateral cephalograms. The aim of the current study was to assess the precision of panoramic imaging in measuring the gonial angles compared to lateral cephalograms in adult patients with different mandibular divergence patterns. Methods Panoramic radiographs and lateral cephalograms of 448 adults (18-30 years old) were utilized in the study. The gonial angle was determined on the lateral cephalograms using an online AI-driven assessment tool (WebCephTM) and compared to the panoramic measurements among the different gender, malocclusion, and mandibular divergence groups. Results Statistically significant differences were recorded between measurements taken on lateral cephalograms or panoramic radiographs (p=0.022). In addition, statistically significant differences were reported in gonial angle measurements on panoramic radiographs among the different mandibular divergence groups (p=0.004) for FMA (p=0.002) for Sn-GoMe. Conclusion While cephalometry is considered the gold standard tool for reliable gonial angle assessment, panoramic radiographs were more accurate in detecting the differences between the divergence groups in the current study.
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Affiliation(s)
- Nada E Tashkandi
- Preventative Department, Riyadh Elm University, Riyadh, Saudi Arabia
| | | | | | - Eman Allam
- Basic Dental Science Department, Oral and Dental Research Institute, National Research Centre, Cairo, Egypt
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Guinot-Barona C, Alonso Pérez-Barquero J, Galán López L, Barmak AB, Att W, Kois JC, Revilla-León M. Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs. J ESTHET RESTOR DENT 2024; 36:555-565. [PMID: 37882509 DOI: 10.1111/jerd.13156] [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: 06/16/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/27/2023]
Abstract
PURPOSE The purpose of the present clinical study was to compare the Ricketts and Steiner cephalometric analysis obtained by two experienced orthodontists and artificial intelligence (AI)-based software program and measure the orthodontist variability. MATERIALS AND METHODS A total of 50 lateral cephalometric radiographs from 50 patients were obtained. Two groups were created depending on the operator performing the cephalometric analysis: orthodontists (Orthod group) and an AI software program (AI group). In the Orthod group, two independent experienced orthodontists performed the measurements by performing a manual identification of the cephalometric landmarks and a software program (NemoCeph; Nemotec) to calculate the measurements. In the AI group, an AI software program (CephX; ORCA Dental AI) was selected for both the automatic landmark identification and cephalometric measurements. The Ricketts and Steiner cephalometric analyses were assessed in both groups including a total of 24 measurements. The Shapiro-Wilk test showed that the data was normally distributed. The t-test was used to analyze the data (α = 0.05). RESULTS The t-test analysis showed significant measurement discrepancies between the Orthod and AI group in seven of the 24 cephalometric parameters tested, namely the corpus length (p = 0.003), mandibular arc (p < 0.001), lower face height (p = 0.005), overjet (p = 0.019), and overbite (p = 0.022) in the Ricketts cephalometric analysis and occlusal to SN (p = 0.002) and GoGn-SN (p < 0.001) in the Steiner cephalometric analysis. The intraclass correlation coefficient (ICC) between both orthodontists of the Orthod group for each cephalometric measurement was calculated. CONCLUSIONS Significant discrepancies were found in seven of the 24 cephalometric measurements tested between the orthodontists and the AI-based program assessed. The intra-operator reliability analysis showed reproducible measurements between both orthodontists, except for the corpus length measurement. CLINICAL SIGNIFICANCE The artificial intelligence software program tested has the potential to automatically obtain cephalometric analysis using lateral cephalometric radiographs; however, additional studies are needed to further evaluate the accuracy of this AI-based system.
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Affiliation(s)
- Clara Guinot-Barona
- Department of Dental Orthodontics, Faculty of Medicine and Health Sciences, Catholic University of Valencia, Valencia, Spain
| | | | - Lidia Galán López
- Department of Dental Orthodontics, Faculty of Medicine and Health Sciences, Catholic University of Valencia, Valencia, Spain
| | - Abdul B Barmak
- Clinical Research and Biostatistics, Eastman Institute of Oral Health, University of Rochester Medical Center, Rochester, New York, USA
| | - Wael Att
- Department of Prosthodontics, University Hospital of Freiburg, Freiburg, Germany, USA
| | - John C Kois
- Kois Center, Seattle, Washington, USA
- Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Washington, USA
- Private Practice, Seattle, Washington, USA
| | - Marta Revilla-León
- Kois Center, Seattle, Washington, USA
- Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Washington, USA
- Department of Prosthodontics, School of Dental Medicine, Tufts University, Boston, Massachusetts, USA
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Kang S, Kim I, Kim YJ, Kim N, Baek SH, Sung SJ. Accuracy and clinical validity of automated cephalometric analysis using convolutional neural networks. Orthod Craniofac Res 2024; 27:64-77. [PMID: 37326233 DOI: 10.1111/ocr.12683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/02/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND This study aimed to assess the error range of cephalometric measurements based on the landmarks detected using cascaded CNNs and determine how horizontal and vertical positional errors of individual landmarks affect lateral cephalometric measurements. METHODS In total, 120 lateral cephalograms were obtained consecutively from patients (mean age, 32.5 ± 11.6) who visited the Asan Medical Center, Seoul, Korea, for orthodontic treatment between 2019 and 2021. An automated lateral cephalometric analysis model previously developed from a nationwide multi-centre database was used to digitize the lateral cephalograms. The horizontal and vertical landmark position error attributable to the AI model was defined as the distance between the landmark identified by the human and that identified by the AI model on the x- and y-axes. The differences between the cephalometric measurements based on the landmarks identified by the AI model vs those identified by the human examiner were assessed. The association between the lateral cephalometric measurements and the positioning errors in the landmarks comprising the cephalometric measurement was assessed. RESULTS The mean difference in the angular and linear measurements based on AI vs human landmark localization was .99 ± 1.05°, and .80 ± .82 mm, respectively. Significant differences between the measurements derived from AI-based and human localization were observed for all cephalometric variables except SNA, pog-Nperp, facial angle, SN-GoGn, FMA, Bjork sum, U1-SN, U1-FH, IMPA, L1-NB (angular) and interincisal angle. CONCLUSIONS The errors in landmark positions, especially those that define reference planes, may significantly affect cephalometric measurements. The possibility of errors generated by automated lateral cephalometric analysis systems should be considered when using such systems for orthodontic diagnoses.
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Affiliation(s)
- Seyun Kang
- Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Inhwan Kim
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yoon-Ji Kim
- Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 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, Korea
| | - Seung-Hak Baek
- Department of Orthodontics, School of Dentistry, Dental Research Institute, Seoul National University, Seoul, Korea
| | - Sang-Jin Sung
- Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Kazimierczak N, Kazimierczak W, Serafin Z, Nowicki P, Jankowski T, Jankowska A, Janiszewska-Olszowska J. Skeletal facial asymmetry: reliability of manual and artificial intelligence-driven analysis. Dentomaxillofac Radiol 2024; 53:52-59. [PMID: 38214946 PMCID: PMC11003660 DOI: 10.1093/dmfr/twad006] [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: 06/20/2023] [Revised: 08/29/2023] [Accepted: 11/11/2023] [Indexed: 01/13/2024] Open
Abstract
OBJECTIVES To compare artificial intelligence (AI)-driven web-based platform and manual measurements for analysing facial asymmetry in craniofacial CT examinations. METHODS The study included 95 craniofacial CT scans from patients aged 18-30 years. The degree of asymmetry was measured based on AI platform-predefined anatomical landmarks: sella (S), condylion (Co), anterior nasal spine (ANS), and menton (Me). The concordance between the results of automatic asymmetry reports and manual linear 3D measurements was calculated. The asymmetry rate (AR) indicator was determined for both automatic and manual measurements, and the concordance between them was calculated. The repeatability of manual measurements in 20 randomly selected subjects was assessed. The concordance of measurements of quantitative variables was assessed with interclass correlation coefficient (ICC) according to the Shrout and Fleiss classification. RESULTS Erroneous AI tracings were found in 16.8% of cases, reducing the analysed cases to 79. The agreement between automatic and manual asymmetry measurements was very low (ICC < 0.3). A lack of agreement between AI and manual AR analysis (ICC type 3 = 0) was found. The repeatability of manual measurements and AR calculations showed excellent correlation (ICC type 2 > 0.947). CONCLUSIONS The results indicate that the rate of tracing errors and lack of agreement with manual AR analysis make it impossible to use the tested AI platform to assess the degree of facial asymmetry.
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Affiliation(s)
| | - Wojciech Kazimierczak
- Kazimierczak Private Dental Practice, 85-009 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, 85-067 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, 85-067 Bydgoszcz, Poland
| | - Paweł Nowicki
- Kazimierczak Private Dental Practice, 85-009 Bydgoszcz, Poland
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Menezes LDS, Silva TP, Lima Dos Santos MA, Hughes MM, Mariano Souza SDR, Leite Ribeiro PM, Freitas PHLD, Takeshita WM. Assessment of landmark detection in cephalometric radiographs with different conditions of brightness and contrast using the an artificial intelligence software. Dentomaxillofac Radiol 2023; 52:20230065. [PMID: 37869886 DOI: 10.1259/dmfr.20230065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2023] Open
Abstract
OBJECTIVES To evaluate the reliability and reproducibility of an artificial intelligence (AI) software in identifying cephalometric points on lateral cephalometric radiographs considering four settings of brightness and contrast. METHODS AND MATERIALS Brightness and contrast of 30 lateral cephalometric radiographs were adjusted into four different settings. Then, the control examiner (ECont), the calibrated examiner (ECal), and the CEFBOT AI software (AIs) each marked 19 cephalometric points on all radiographs. Reliability was assessed with a second analysis of the radiographs 15 days after the first one. Statistical significance was set at p < 0.05. RESULTS Reliability of landmark identification was excellent for the human examiners and the AIs regardless of the type of brightness and contrast setting (mean intraclass correlation coefficient >0.89). When ECont and ECal were compared for reproducibility, there were more cephalometric points with significant differences on the x-axis of the image with the highest contrast and the lowest brightness, namely N(p = 0.033), S(p = 0.030), Po(p < 0.001), and Pog'(p = 0.012). Between ECont and AIs, there were more cephalometric points with significant differences on the image with the highest contrast and the lowest brightness, namely N(p = 0.034), Or(p = 0.048), Po(p < 0.001), A(p = 0.042), Pog'(p = 0.004), Ll(p = 0.005), Ul(p < 0.001), and Sn(p = 0.001). CONCLUSIONS While the reliability of the AIs for cephalometric landmark identification was rated as excellent, low brightness and high contrast seemed to affect its reproducibility. The experienced human examiner, on the other hand, did not show such faulty reproducibility; therefore, the AIs used in this study is an excellent auxiliary tool for cephalometric analysis, but still depends on human supervision to be clinically reliable.
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Affiliation(s)
| | - Thaísa Pinheiro Silva
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, São Paulo, Brazil
| | | | | | | | | | | | - Wilton Mitsunari Takeshita
- Diagnosis and Surgery, São Paulo State University (Unesp), School of Dentistry, Araçatuba, São Paulo, Brazil
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Kazimierczak N, Kazimierczak W, Serafin Z, Nowicki P, Lemanowicz A, Nadolska K, Janiszewska-Olszowska J. Correlation Analysis of Nasal Septum Deviation and Results of AI-Driven Automated 3D Cephalometric Analysis. J Clin Med 2023; 12:6621. [PMID: 37892759 PMCID: PMC10607148 DOI: 10.3390/jcm12206621] [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: 08/31/2023] [Revised: 10/04/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023] Open
Abstract
The nasal septum is believed to play a crucial role in the development of the craniofacial skeleton. Nasal septum deviation (NSD) is a common condition, affecting 18-65% of individuals. This study aimed to assess the prevalence of NSD and its potential association with abnormalities detected through cephalometric analysis using artificial intelligence (AI) algorithms. The study included CT scans of 120 consecutive, post-traumatic patients aged 18-30. Cephalometric analysis was performed using an AI web-based software, CephX. The automatic analysis comprised all the available cephalometric analyses. NSD was assessed using two methods: maximum deviation from an ideal non-deviated septum and septal deviation angle (SDA). The concordance of repeated manual measurements and automatic analyses was assessed. Of the 120 cases, 90 met the inclusion criteria. The AI-based cephalometric analysis provided comprehensive reports with over 100 measurements. Only the hinge axis angle (HAA) and SDA showed significant (p = 0.039) negative correlations. The rest of the cephalometric analyses showed no correlation with the NSD indicators. The analysis of the agreement between repeated manual measurements and automatic analyses showed good-to-excellent concordance, except in the case of two angular measurements: LI-N-B and Pr-N-A. The CephX AI platform showed high repeatability in automatic cephalometric analyses, demonstrating the reliability of the AI model for most cephalometric analyses.
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Affiliation(s)
| | - Wojciech Kazimierczak
- Kazimierczak Private Dental Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
- Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland; (Z.S.)
| | - Zbigniew Serafin
- Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland; (Z.S.)
| | - Paweł Nowicki
- Kazimierczak Private Dental Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Adam Lemanowicz
- Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland; (Z.S.)
| | - Katarzyna Nadolska
- Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland; (Z.S.)
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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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Bao H, Zhang K, Yu C, Li H, Cao D, Shu H, Liu L, Yan B. Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence. BMC Oral Health 2023; 23:191. [PMID: 37005593 DOI: 10.1186/s12903-023-02881-8.pmid:37005593;pmcid:pmc10067288] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 03/14/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND The purpose of this study was to evaluate the accuracy of automatic cephalometric landmark localization and measurements using cephalometric analysis via artificial intelligence (AI) compared with computer-assisted manual analysis. METHODS Reconstructed lateral cephalograms (RLCs) from cone-beam computed tomography (CBCT) in 85 patients were selected. Computer-assisted manual analysis (Dolphin Imaging 11.9) and AI automatic analysis (Planmeca Romexis 6.2) were used to locate 19 landmarks and obtain 23 measurements. Mean radial error (MRE) and successful detection rate (SDR) values were calculated to assess the accuracy of automatic landmark digitization. Paired t tests and Bland‒Altman plots were used to compare the differences and consistencies in cephalometric measurements between manual and automatic analysis programs. RESULTS The MRE for 19 cephalometric landmarks was 2.07 ± 1.35 mm with the automatic program. The average SDR within 1 mm, 2 mm, 2.5 mm, 3 and 4 mm were 18.82%, 58.58%, 71.70%, 82.04% and 91.39%, respectively. Soft tissue landmarks (1.54 ± 0.85 mm) had the most consistency, while dental landmarks (2.37 ± 1.55 mm) had the most variation. In total, 15 out of 23 measurements were within the clinically acceptable level of accuracy, 2 mm or 2°. The rates of consistency within the 95% limits of agreement were all above 90% for all measurement parameters. CONCLUSION Automatic analysis software collects cephalometric measurements almost effectively enough to be acceptable in clinical work. Nevertheless, automatic cephalometry is not capable of completely replacing manual tracing. Additional manual supervision and adjustment for automatic programs can increase accuracy and efficiency.
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Affiliation(s)
- Han Bao
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Kejia Zhang
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Chenhao Yu
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Hu Li
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Dan Cao
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Huazhong Shu
- Laboratory of Image Science and Technology, Southeast University, Nanjing, 210096, China
- Centre de Recherche en Information Biomédicale Sino-Français, Rennes, 35000, France
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Southeast University, Nanjing, 210096, China
| | - Luwei Liu
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China.
| | - Bin Yan
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China.
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Bao H, Zhang K, Yu C, Li H, Cao D, Shu H, Liu L, Yan B. Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence. BMC Oral Health 2023; 23:191. [PMID: 37005593 PMCID: PMC10067288 DOI: 10.1186/s12903-023-02881-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 03/14/2023] [Indexed: 04/04/2023] Open
Abstract
BACKGROUND The purpose of this study was to evaluate the accuracy of automatic cephalometric landmark localization and measurements using cephalometric analysis via artificial intelligence (AI) compared with computer-assisted manual analysis. METHODS Reconstructed lateral cephalograms (RLCs) from cone-beam computed tomography (CBCT) in 85 patients were selected. Computer-assisted manual analysis (Dolphin Imaging 11.9) and AI automatic analysis (Planmeca Romexis 6.2) were used to locate 19 landmarks and obtain 23 measurements. Mean radial error (MRE) and successful detection rate (SDR) values were calculated to assess the accuracy of automatic landmark digitization. Paired t tests and Bland‒Altman plots were used to compare the differences and consistencies in cephalometric measurements between manual and automatic analysis programs. RESULTS The MRE for 19 cephalometric landmarks was 2.07 ± 1.35 mm with the automatic program. The average SDR within 1 mm, 2 mm, 2.5 mm, 3 and 4 mm were 18.82%, 58.58%, 71.70%, 82.04% and 91.39%, respectively. Soft tissue landmarks (1.54 ± 0.85 mm) had the most consistency, while dental landmarks (2.37 ± 1.55 mm) had the most variation. In total, 15 out of 23 measurements were within the clinically acceptable level of accuracy, 2 mm or 2°. The rates of consistency within the 95% limits of agreement were all above 90% for all measurement parameters. CONCLUSION Automatic analysis software collects cephalometric measurements almost effectively enough to be acceptable in clinical work. Nevertheless, automatic cephalometry is not capable of completely replacing manual tracing. Additional manual supervision and adjustment for automatic programs can increase accuracy and efficiency.
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Affiliation(s)
- Han Bao
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Kejia Zhang
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Chenhao Yu
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Hu Li
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Dan Cao
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Huazhong Shu
- Laboratory of Image Science and Technology, Southeast University, Nanjing, 210096, China
- Centre de Recherche en Information Biomédicale Sino-Français, Rennes, 35000, France
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Southeast University, Nanjing, 210096, China
| | - Luwei Liu
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China.
| | - Bin Yan
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China.
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Junaid N, Khan N, Ahmed N, Abbasi MS, Das G, Maqsood A, Ahmed AR, Marya A, Alam MK, Heboyan A. Development, Application, and Performance of Artificial Intelligence in Cephalometric Landmark Identification and Diagnosis: A Systematic Review. Healthcare (Basel) 2022; 10:2454. [PMID: 36553978 PMCID: PMC9778374 DOI: 10.3390/healthcare10122454] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/26/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
This study aimed to analyze the existing literature on how artificial intelligence is being used to support the identification of cephalometric landmarks. The systematic analysis of literature was carried out by performing an extensive search in PubMed/MEDLINE, Google Scholar, Cochrane, Scopus, and Science Direct databases. Articles published in the last ten years were selected after applying the inclusion and exclusion criteria. A total of 17 full-text articles were systematically appraised. The Cochrane Handbook for Systematic Reviews of Interventions (CHSRI) and Newcastle-Ottawa quality assessment scale (NOS) were adopted for quality analysis of the included studies. The artificial intelligence systems were mainly based on deep learning-based convolutional neural networks (CNNs) in the included studies. The majority of the studies proposed that AI-based automatic cephalometric analyses provide clinically acceptable diagnostic performance. They have worked remarkably well, with accuracy and precision similar to the trained orthodontist. Moreover, they can simplify cephalometric analysis and provide a quick outcome in practice. Therefore, they are of great benefit to orthodontists, as with these systems they can perform tasks more efficiently.
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Affiliation(s)
- Nuha Junaid
- Department of Prosthodontics, Altamash Institute of Dental Medicine, Karachi 75500, Pakistan
| | - Niha Khan
- Department of Prosthodontics, Altamash Institute of Dental Medicine, Karachi 75500, Pakistan
| | - Naseer Ahmed
- Department of Prosthodontics, Altamash Institute of Dental Medicine, Karachi 75500, Pakistan
- Prosthodontics Unit, School of Dental Sciences, Health Campus, University Sains Malaysia, Kota Bharu 16150, Malaysia
| | - Maria Shakoor Abbasi
- Department of Prosthodontics, Altamash Institute of Dental Medicine, Karachi 75500, Pakistan
| | - Gotam Das
- Department of Prosthodontics, College of Dentistry, King Khalid University, Abha 61421, Saudi Arabia
| | - Afsheen Maqsood
- Department of Oral Pathology, Bahria University Dental College, Karachi 74400, Pakistan
| | - Abdul Razzaq Ahmed
- Department of Prosthodontics, College of Dentistry, King Khalid University, Abha 61421, Saudi Arabia
| | - Anand Marya
- Department of Orthodontics, Faculty of Dentistry, University of Puthisastra, Phnom Penh 12211, Cambodia
| | - Mohammad Khursheed Alam
- Department of Preventive Dentistry, College of Dentistry, Jouf University, Sakaka 72345, Saudi Arabia
- Center for Transdisciplinary Research (CFTR), Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College, Saveetha University, Chennai 602105, India
- Department of Public Health, Faculty of Allied Health Sciences, Daffodil International University, Dhaka 1341, Bangladesh
| | - Artak Heboyan
- Department of Prosthodontics, Faculty of Stomatology, Yerevan State Medical University after Mkhitar Heratsi, Str. Koryun 2, Yerevan 0025, Armenia
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Cephalometric Analysis in Orthodontics Using Artificial Intelligence-A Comprehensive Review. BIOMED RESEARCH INTERNATIONAL 2022; 2022:1880113. [PMID: 35757486 PMCID: PMC9225851 DOI: 10.1155/2022/1880113] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 05/13/2022] [Indexed: 11/17/2022]
Abstract
Artificial intelligence (AI) is a branch of science concerned with developing programs and computers that can gather data, reason about it, and then translate it into intelligent actions. AI is a broad area that includes reasoning, typical linguistic dispensation, machine learning, and planning. In the area of medicine and dentistry, machine learning is currently the most widely used AI application. This narrative review is aimed at giving an outline of cephalometric analysis in orthodontics using AI. Latest algorithms are developing rapidly, and computational resources are increasing, resulting in increased efficiency, accuracy, and reliability. Current techniques for completely automatic identification of cephalometric landmarks have considerably improved efficiency and growth prospects for their regular use. The primary considerations for effective orthodontic treatment are an accurate diagnosis, exceptional treatment planning, and good prognosis estimation. The main objective of the AI technique is to make dentists' work more precise and accurate. AI is increasingly being used in the area of orthodontic treatment. It has been evidenced to be a time-saving and reliable tool in many ways. AI is a promising tool for facilitating cephalometric tracing in routine clinical practice and analyzing large databases for research purposes.
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Katyal D, Balakrishnan N. Evaluation of the accuracy and reliability of WebCeph – An artificial intelligence-based online software. APOS TRENDS IN ORTHODONTICS 2022. [DOI: 10.25259/apos_138_2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Objectives:
Landmark identification is of utmost importance in cephalometric analysis but it turns out to be the main source of error. With modern inventions in the field of artificial intelligence (AI), it becomes essential to assess the reliability of computer-automated programs. A greater deal of time can be conserved with fully automated programs such as WebCeph, which uses an AI-based algorithm that performs automated and immediate cephalometric analysis. This study aimed to evaluate the accuracy, reliability, and duration of tracing cephalometric radiographs with WebCeph, an AI-based software in comparison to digital tracing with FACAD and manual tracing. The null hypothesis proposed is that there is no statistically significant difference among the three methods with regard to accuracy of cephalometric analysis.
Material and Methods:
Pre-treatment cephalometric radiographs of 25 patients (14 males and 11 females, mean age of 18 ± 3.2 years) were selected randomly from the dental information archiving software of Saveetha University, Department of Orthodontics, Chennai. Composite analysis with skeletal, dental and soft-tissue parameters was selected and cephalometric analysis was done with all three methods – Manual tracing (Group 1), digital tracing using FACAD (Group 2), and fully automated AI-based software WebCeph (Group 3). The timing for each method of analysis was calculated using a stopwatch in seconds. Values were tabulated in an Excel sheet and statistical analysis including one-way analysis of variance and post hoc Tukey test were performed.
Results:
No statistically significant difference was found between the three methods for cephalometric analysis, P > 0.05. The time taken for measurement using the three different methods was the least while using WebCeph (30.2 ± 6.4 s) and the maximum while manual tracing (472 ± 40.4 s).
Conclusion:
WebCeph is a reliable, faster and practical tool for analyzing cephalometric analysis in comparison to digital tracing using FACAD and manual tracing.
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Affiliation(s)
- Deepika Katyal
- Department of Orthodontics, Saveetha Dental College, Chennai, Tamil Nadu, India,
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Mahto RK, Kafle D, Giri A, Luintel S, Karki A. Evaluation of fully automated cephalometric measurements obtained from web-based artificial intelligence driven platform. BMC Oral Health 2022; 22:132. [PMID: 35440037 PMCID: PMC9020017 DOI: 10.1186/s12903-022-02170-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 04/12/2022] [Indexed: 11/30/2022] Open
Abstract
Background Artificial Intelligence has created a huge impact in different areas of dentistry. Automated cephalometric analysis is one of the major applications of artificial intelligence in the field of orthodontics. Various automated cephalometric software have been developed which utilizes artificial intelligence and claim to be reliable. The purpose of this study was to compare the linear and angular cephalometric measurements obtained from web-based fully automated Artificial Intelligence (AI) driven platform “WebCeph”™ with that from manual tracing and evaluate the validity and reliability of automated cephalometric measurements obtained from “WebCeph”™. Methods Thirty pre-treatment lateral cephalograms of patients were randomly selected. For manual tracing, digital images of same cephalograms were printed using compatible X-ray printer. After calibration, a total of 18 landmarks was plotted and 12 measurements (8 angular and 4 linear) were obtained using standard protocols. The digital images of each cephalogram were uploaded to “WebCeph”™ server. After image calibration, the automated cephalometric measurements obtained through AI digitization were downloaded for each image. Intraclass correlation coefficient (ICC) was used to determine agreement between the measurements obtained from two methods. ICC value < 0.75 was considered as poor to moderate agreement while an ICC value between 0.75 and 0.90 was considered as good agreement. Agreement was rated as excellent when ICC value > 0.90 was obtained. Results All the measurements had ICC value above 0.75. A higher ICC value > 0.9 was obtained for seven parameters i.e. ANB, FMA, IMPA/L1 to MP (°), LL to E-line, L1 to NB (mm), L1 to NB (°), S-N to Go-Gn whereas five parameters i.e. UL to E-line, U1 to NA (mm), SNA, SNB, U1 to NA (°) showed ICC value between 0.75 and 0.90. Conclusion A good agreement was found between the cephalometric measurements obtained from “WebCeph”™ and manual tracing.
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Affiliation(s)
- Ravi Kumar Mahto
- Department of Orthodontics and Dentofacial Orthopedics, Kathmandu University School of Medical Sciences, Dhulikhel, Nepal.
| | - Dashrath Kafle
- Department of Orthodontics and Dentofacial Orthopedics, Kathmandu University School of Medical Sciences, Dhulikhel, Nepal
| | - Abhishek Giri
- Department of Orthodontics and Dentofacial Orthopedics, Kathmandu University School of Medical Sciences, Dhulikhel, Nepal
| | - Sanjeev Luintel
- Department of Orthodontics and Dentofacial Orthopedics, Kathmandu University School of Medical Sciences, Dhulikhel, Nepal
| | - Arjun Karki
- Department of Orthodontics and Dentofacial Orthopedics, Kathmandu University School of Medical Sciences, Dhulikhel, Nepal
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Meriç P, Naoumova J. Web-based Fully Automated Cephalometric Analysis: Comparisons between App-aided, Computerized, and Manual Tracings. Turk J Orthod 2020; 33:142-149. [PMID: 32974059 DOI: 10.5152/turkjorthod.2020.20062] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 07/16/2020] [Indexed: 11/22/2022]
Abstract
Objective To compare the accuracy of cephalometric analyses made with fully automated tracings, computerized tracing, and app-aided tracings with equivalent hand-traced measurements, and to evaluate the tracing time for each cephalometric analysis method. Methods Pre-treatment lateral cephalometric radiographs of 40 patients were randomly selected. Eight angular and 4 linear parameters were measured by 1 operator using 3 methods: computerized tracing with software Dolphin Imaging 13.01(Dolphin Imaging and Management Solutions, Chatsworth, Calif, USA), app-aided tracing using the CephNinja 3.51 app (Cyncronus LLC, WA, USA), and web-based fully automated tracing with CephX (ORCA Dental AI, Las Vegas, NV). Correction of CephX landmarks was also made. Manual tracings were performed by 3 operators. Remeasurement of 15 radiographs was carried out to determine the intra-examiner and inter-examiner (manual tracings) correlation coefficient (ICC). Inter-group comparisons were made with one-way analysis of variance. The Tukey test was used for post hoc testing. Results Overall, greater variability was found with CephX compared with the other methods. Differences in GoGn-SN (°), I-NA (°), I-NB (°), I-NA (mm), and I-NB (mm) were statistically (p<0.05) and clinically significant using CephX, whereas CephNinja and Dolphin were comparable to manual tracings. Correction of CephX landmarks gave similar results to CephNinja and Dolphin. All the ICCs exceeded 0.85, except for I-NA (°), I-NB (°), and I-NB (mm), which were traced with CephX. The shortest analyzing time was obtained with CephX. Conclusion Fully automatic analysis with CephX needs to be more reliable. However, CephX analysis with manual correction is promising for use in clinical practice because it is comparable to CephNinja and Dolphin, and the analyzing time is significantly shorter.
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Affiliation(s)
- Pamir Meriç
- Department of Orthodontics, Faculty of Dentistry, Trakya University, Edirne, Turkey
| | - Julia Naoumova
- Specialist Clinic of Orthodontics, Public Dental Service, Västra Götaland Region, Gothenburg, Sweden
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