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Hendrickx J, Gracea RS, Vanheers M, Winderickx N, Preda F, Shujaat S, Jacobs R. Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis. Eur J Orthod 2024; 46:cjae029. [PMID: 38895901 PMCID: PMC11185929 DOI: 10.1093/ejo/cjae029] [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: 06/21/2024]
Abstract
OBJECTIVES This systematic review and meta-analysis aimed to investigate the accuracy and efficiency of artificial intelligence (AI)-driven automated landmark detection for cephalometric analysis on two-dimensional (2D) lateral cephalograms and three-dimensional (3D) cone-beam computed tomographic (CBCT) images. SEARCH METHODS An electronic search was conducted in the following databases: PubMed, Web of Science, Embase, and grey literature with search timeline extending up to January 2024. SELECTION CRITERIA Studies that employed AI for 2D or 3D cephalometric landmark detection were included. DATA COLLECTION AND ANALYSIS The selection of studies, data extraction, and quality assessment of the included studies were performed independently by two reviewers. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A meta-analysis was conducted to evaluate the accuracy of the 2D landmarks identification based on both mean radial error and standard error. RESULTS Following the removal of duplicates, title and abstract screening, and full-text reading, 34 publications were selected. Amongst these, 27 studies evaluated the accuracy of AI-driven automated landmarking on 2D lateral cephalograms, while 7 studies involved 3D-CBCT images. A meta-analysis, based on the success detection rate of landmark placement on 2D images, revealed that the error was below the clinically acceptable threshold of 2 mm (1.39 mm; 95% confidence interval: 0.85-1.92 mm). For 3D images, meta-analysis could not be conducted due to significant heterogeneity amongst the study designs. However, qualitative synthesis indicated that the mean error of landmark detection on 3D images ranged from 1.0 to 5.8 mm. Both automated 2D and 3D landmarking proved to be time-efficient, taking less than 1 min. Most studies exhibited a high risk of bias in data selection (n = 27) and reference standard (n = 29). CONCLUSION The performance of AI-driven cephalometric landmark detection on both 2D cephalograms and 3D-CBCT images showed potential in terms of accuracy and time efficiency. However, the generalizability and robustness of these AI systems could benefit from further improvement. REGISTRATION PROSPERO: CRD42022328800.
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Affiliation(s)
- Julie Hendrickx
- Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
| | - Rellyca Sola Gracea
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
| | - Michiel Vanheers
- Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
| | - Nicolas Winderickx
- Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
| | - Flavia Preda
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
| | - Sohaib Shujaat
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
- King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh 14611, Kingdom of Saudi Arabia
| | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
- Department of Dental Medicine, Karolinska Institutet, 141 04 Stockholm, Sweden
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Surendran A, Daigavane P, Shrivastav S, Kamble R, Sanchla AD, Bharti L, Shinde M. The Future of Orthodontics: Deep Learning Technologies. Cureus 2024; 16:e62045. [PMID: 38989357 PMCID: PMC11234326 DOI: 10.7759/cureus.62045] [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: 03/03/2024] [Accepted: 06/09/2024] [Indexed: 07/12/2024] Open
Abstract
Deep learning has emerged as a revolutionary technical advancement in modern orthodontics, offering novel methods for diagnosis, treatment planning, and outcome prediction. Over the past 25 years, the field of dentistry has widely adopted information technology (IT), resulting in several benefits, including decreased expenses, increased efficiency, decreased need for human expertise, and reduced errors. The transition from preset rules to learning from real-world examples, particularly machine learning (ML) and artificial intelligence (AI), has greatly benefited the organization, analysis, and storage of medical data. Deep learning, a type of AI, enables robots to mimic human neural networks, allowing them to learn and make decisions independently without the need for explicit programming. Its ability to automate cephalometric analysis and enhance diagnosis through 3D imaging has revolutionized orthodontic operations. Deep learning models have the potential to significantly improve treatment outcomes and reduce human errors by accurately identifying anatomical characteristics on radiographs, thereby expediting analytical processes. Additionally, the use of 3D imaging technologies such as cone-beam computed tomography (CBCT) can facilitate precise treatment planning, allowing for comprehensive examinations of craniofacial architecture, tooth movements, and airway dimensions. In today's era of personalized medicine, deep learning's ability to customize treatments for individual patients has propelled the field of orthodontics forward tremendously. However, it is essential to address issues related to data privacy, model interpretability, and ethical considerations before orthodontic practices can use deep learning in an ethical and responsible manner. Modern orthodontics is evolving, thanks to the ability of deep learning to deliver more accurate, effective, and personalized orthodontic treatments, improving patient care as technology develops.
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Affiliation(s)
- Aathira Surendran
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Pallavi Daigavane
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Sunita Shrivastav
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Ranjit Kamble
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Abhishek D Sanchla
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Lovely Bharti
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Mrudula Shinde
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
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Kanemeier M, Middelberg C, Stamm T, Albert F, Hohoff A, Schmid JQ. Accuracy and tracing time of cephalometric analyses on a tablet or desktop computer : A prospective study. Head Face Med 2024; 20:9. [PMID: 38347578 PMCID: PMC10860254 DOI: 10.1186/s13005-024-00413-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 01/30/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND This prospective study aimed to evaluate the influence of the computer type (tablet or desktop) on accuracy and tracing time of cephalometric analyses. METHODS Dental students used a web-based application specifically developed for this purpose to perform cephalometric analyses on tablet and desktop computers. Landmark locations and timestamps were exported to measure the accuracy, successful detection rate and tracing time. Reference landmarks were established by six experienced orthodontists. Statistical analysis included reliability assessment, descriptive statistics, and linear mixed effect models. RESULTS Over a period of 8 semesters a total of 277 cephalometric analyses by 161 students were included. The interrater reliability of the orthodontists establishing the reference coordinates was excellent (ICC > 0.9). For the students, the mean landmark deviation was 2.05 mm and the successful detection rate for the clinically acceptable threshold of 2 mm suggested in the literature was 68.6%, with large variations among landmarks. No effect of the computer type on accuracy and tracing time of the cephalometric analyses could be found. CONCLUSION The use of tablet computers for cephalometric analyses can be recommended.
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Affiliation(s)
- Moritz Kanemeier
- Department of Orthodontics, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany.
| | - Claudius Middelberg
- Department of Orthodontics, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Thomas Stamm
- Department of Orthodontics, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Felix Albert
- Institute of Biostatistics and Clinical Research, University of Münster, Schmeddingstr. 56, 48149, Münster, Germany
| | - Ariane Hohoff
- Department of Orthodontics, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Jonas Q Schmid
- Department of Orthodontics, University of Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
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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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Kotuła J, Kuc A, Szeląg E, Babczyńska A, Lis J, Matys J, Kawala B, Sarul M. Comparison of Diagnostic Validity of Cephalometric Analyses of the ANB Angle and Tau Angle for Assessment of the Sagittal Relationship of Jaw and Mandible. J Clin Med 2023; 12:6333. [PMID: 37834977 PMCID: PMC10573306 DOI: 10.3390/jcm12196333] [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: 09/11/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND Cephalometric analysis is an essential tool used in orthodontic diagnosis and treatment planning. The aim of this study was to evaluate the reliability and repeatability of new cephalometric points introduced in Tau angle analysis, in contrast to the gold standard, which is the analysis of the ANB angle. For this purpose, an attempt was made to assess the repeatability and reliability of the introduction of anthropometric points by evaluating both inter- and intraobserver parameters, as well as the agreement among the orthodontists participating in the study. METHODS Repeatability and reliability assessments for all six anthropometric points (N, A, B, T, M, G) used in the analysis of the ANB and Tau angles were conducted individually by 29 orthodontists. This assessment was performed in triplicate on the day of the study, on the day following the first study, and on the seventh day after the second study. Measurement errors for the ANB and Tau angles were evaluated using the Dahlberg formula and intraclass correlation coefficients (ICCs). RESULTS The orthodontists in the study measured sagittal discrepancy significantly more accurately using the ANB angle compared to the Tau angle (p < 0.001). The Dahlberg error for measuring the Tau angle was three times greater than that for the ANB angle (p < 0.001). Additionally, the ICC for the Tau angle was more than 3.5 times smaller than that for the ANB angle, while the R&R error for Tau measurement was more than three times greater than that for the ANB angle (p < 0.001). CONCLUSIONS The results of ANB angle measurements exhibit fewer errors in comparison to Tau angle measurements.
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Affiliation(s)
- Jacek Kotuła
- Department of Dentofacial Orthopedics and Orthodontics, Wroclaw Medical University, Krakowska 26, 50-425 Wroclaw, Poland; (A.K.); (E.S.); (A.B.); (J.L.); (B.K.); (M.S.)
| | - Anna Kuc
- Department of Dentofacial Orthopedics and Orthodontics, Wroclaw Medical University, Krakowska 26, 50-425 Wroclaw, Poland; (A.K.); (E.S.); (A.B.); (J.L.); (B.K.); (M.S.)
| | - Ewa Szeląg
- Department of Dentofacial Orthopedics and Orthodontics, Wroclaw Medical University, Krakowska 26, 50-425 Wroclaw, Poland; (A.K.); (E.S.); (A.B.); (J.L.); (B.K.); (M.S.)
| | - Alicja Babczyńska
- Department of Dentofacial Orthopedics and Orthodontics, Wroclaw Medical University, Krakowska 26, 50-425 Wroclaw, Poland; (A.K.); (E.S.); (A.B.); (J.L.); (B.K.); (M.S.)
| | - Joanna Lis
- Department of Dentofacial Orthopedics and Orthodontics, Wroclaw Medical University, Krakowska 26, 50-425 Wroclaw, Poland; (A.K.); (E.S.); (A.B.); (J.L.); (B.K.); (M.S.)
| | - Jacek Matys
- Oral Surgery Department, Wroclaw Medical University, Krakowska 26, 50-425 Wroclaw, Poland
- Department of Orthodontics, Technische Universitat Dresden, 01307 Dresden, Germany
| | - Beata Kawala
- Department of Dentofacial Orthopedics and Orthodontics, Wroclaw Medical University, Krakowska 26, 50-425 Wroclaw, Poland; (A.K.); (E.S.); (A.B.); (J.L.); (B.K.); (M.S.)
| | - Michał Sarul
- Department of Dentofacial Orthopedics and Orthodontics, Wroclaw Medical University, Krakowska 26, 50-425 Wroclaw, Poland; (A.K.); (E.S.); (A.B.); (J.L.); (B.K.); (M.S.)
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Kunz F, Stellzig-Eisenhauer A, Widmaier LM, Zeman F, Boldt J. Assessment of the quality of different commercial providers using artificial intelligence for automated cephalometric analysis compared to human orthodontic experts. J Orofac Orthop 2023:10.1007/s00056-023-00491-1. [PMID: 37642657 DOI: 10.1007/s00056-023-00491-1] [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: 02/15/2023] [Accepted: 06/28/2023] [Indexed: 08/31/2023]
Abstract
PURPOSE The aim of this investigation was to evaluate the accuracy of various skeletal and dental cephalometric parameters as produced by different commercial providers that make use of artificial intelligence (AI)-assisted automated cephalometric analysis and to compare their quality to a gold standard established by orthodontic experts. METHODS Twelve experienced orthodontic examiners pinpointed 15 radiographic landmarks on a total of 50 cephalometric X‑rays. The landmarks were used to generate 9 parameters for orthodontic treatment planning. The "humans' gold standard" was defined by calculating the median value of all 12 human assessments for each parameter, which in turn served as reference values for comparisons with results given by four different commercial providers of automated cephalometric analyses (DentaliQ.ortho [CellmatiQ GmbH, Hamburg, Germany], WebCeph [AssembleCircle Corp, Seongnam-si, Korea], AudaxCeph [Audax d.o.o., Ljubljana, Slovenia], CephX [Orca Dental AI, Herzliya, Israel]). Repeated measures analysis of variances (ANOVAs) were calculated and Bland-Altman plots were generated for comparisons. RESULTS The results of the repeated measures ANOVAs indicated significant differences between the commercial providers' predictions and the humans' gold standard for all nine investigated parameters. However, the pairwise comparisons also demonstrate that there were major differences among the four commercial providers. While there were no significant mean differences between the values of DentaliQ.ortho and the humans' gold standard, the predictions of AudaxCeph showed significant deviations in seven out of nine parameters. Also, the Bland-Altman plots demonstrate that a reduced precision of AI predictions must be expected especially for values attributed to the inclination of the incisors. CONCLUSION Fully automated cephalometric analyses are promising in terms of timesaving and avoidance of individual human errors. At present, however, they should only be used under supervision of experienced clinicians.
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Affiliation(s)
- Felix Kunz
- Department of Orthodontics, University Hospital of Würzburg, Pleicherwall 2, 97070, Würzburg, Germany.
| | | | - Lisa Marie Widmaier
- Department of Orthodontics, University Hospital of Würzburg, Pleicherwall 2, 97070, Würzburg, Germany
| | - Florian Zeman
- Centre for Clinical Studies, University Hospital of Regensburg, Regensburg, Germany
| | - Julian Boldt
- Department of Prosthetic Dentistry, University Hospital of Würzburg, Würzburg, Germany
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Ye H, Cheng Z, Ungvijanpunya N, Chen W, Cao L, Gou Y. Is automatic cephalometric software using artificial intelligence better than orthodontist experts in landmark identification? BMC Oral Health 2023; 23:467. [PMID: 37422630 PMCID: PMC10329795 DOI: 10.1186/s12903-023-03188-4] [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: 01/24/2023] [Accepted: 06/29/2023] [Indexed: 07/10/2023] Open
Abstract
BACKGROUND To evaluate the techniques used for the automatic digitization of cephalograms using artificial intelligence algorithms, highlighting the strengths and weaknesses of each one and reviewing the percentage of success in localizing each cephalometric point. METHODS Lateral cephalograms were digitized and traced by three calibrated senior orthodontic residents with or without artificial intelligence (AI) assistance. The same radiographs of 43 patients were uploaded to AI-based machine learning programs MyOrthoX, Angelalign, and Digident. Image J was used to extract x- and y-coordinates for 32 cephalometric points: 11 soft tissue landmarks and 21 hard tissue landmarks. The mean radical errors (MRE) were assessed radical to the threshold of 1.0 mm,1.5 mm, and 2 mm to compare the successful detection rate (SDR). One-way ANOVA analysis at a significance level of P < .05 was used to compare MRE and SDR. The SPSS (IBM-vs. 27.0) and PRISM (GraphPad-vs.8.0.2) software were used for the data analysis. RESULTS Experimental results showed that three methods were able to achieve detection rates greater than 85% using the 2 mm precision threshold, which is the acceptable range in clinical practice. The Angelalign group even achieved a detection rate greater than 78.08% using the 1.0 mm threshold. A marked difference in time was found between the AI-assisted group and the manual group due to heterogeneity in the performance of techniques to detect the same landmark. CONCLUSIONS AI assistance may increase efficiency without compromising accuracy with cephalometric tracings in routine clinical practice and research settings.
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Affiliation(s)
- Huayu Ye
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
| | - Zixuan Cheng
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
- Chongqing Haochi Private Dental Clinic, No. 711, Konggang Avenue, Yubei District, Chongqing, 401147 PR China
| | - Nicha Ungvijanpunya
- Faculty of Dentistry, Chulalongkorn University, 34 Henri Dunant Road, Pathumwan, Bangkok, 10330 Thailand
| | - Wenjing Chen
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
| | - Li Cao
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
| | - Yongchao Gou
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
- Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
- Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, 426#, Songshi North Road, Yubei District, Chongqing, 401147 PR China
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Rauniyar S, Jena S, Sahoo N, Mohanty P, Dash BP. Artificial Intelligence and Machine Learning for Automated Cephalometric Landmark Identification: A Meta-Analysis Previewed by a Systematic Review. Cureus 2023; 15:e40934. [PMID: 37496553 PMCID: PMC10368300 DOI: 10.7759/cureus.40934] [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] [Accepted: 06/24/2023] [Indexed: 07/28/2023] Open
Abstract
Digital dentistry has become an integral part of our practice today, with artificial intelligence (AI) playing the predominant role. The present systematic review was intended to detect the accuracy of landmarks identified cephalometrically using machine learning and artificial intelligence and compare the same with the manual tracing (MT) group. According to the PRISMA-DTA guidelines, a scoping evaluation of the articles was performed. Electronic databases like Doaj, PubMed, Scopus, Google Scholar, and Embase from January 2001 to November 2022 were searched. Inclusion and exclusion criteria were applied, and 13 articles were studied in detail. Six full-text articles were further excluded (three articles did not provide a comparison between manual tracing and AI for cephalometric landmark detection, and three full-text articles were systematic reviews and meta-analyses). Finally, seven articles were found appropriate to be included in this review. The outcome of this systematic review has led to the conclusion that AI, when employed for cephalometric landmark detection, has shown extremely positive and promising results as compared to manual tracing.
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Affiliation(s)
- Sabita Rauniyar
- Orthodontics and Dentofacial Orthopaedics, Kalinga Institute of Dental Science, Bhubaneswar, IND
| | - Sanghamitra Jena
- Department of Orthodontics and Dentofacial Orthopaedics, Kalinga Institute of Dental Sciences, Kalinga Institute of Industrial Technology (KIIT) (Deemed to be University), Bhubaneswar, IND
| | - Nivedita Sahoo
- Department of Orthodontics and Dentofacial Orthopaedics, Kalinga Institute of Dental Sciences, Kalinga Institute of Industrial Technology (KIIT) (Deemed to be University), Bhubaneswar, IND
| | - Pritam Mohanty
- Department of Orthodontics, Kalinga Institute of Dental Sciences, Odisha, IND
| | - Bhagabati P Dash
- Department of Orthodontics and Dentofacial Orthopaedics, Kalinga Institute of Dental Sciences, Kalinga Institute of Industrial Technology (KIIT) (Deemed to be University), Bhubaneswar, IND
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Popova T, Stocker T, Khazaei Y, Malenova Y, Wichelhaus A, Sabbagh H. Influence of growth structures and fixed appliances on automated cephalometric landmark recognition with a customized convolutional neural network. BMC Oral Health 2023; 23:274. [PMID: 37165409 PMCID: PMC10173502 DOI: 10.1186/s12903-023-02984-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 04/20/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND One of the main uses of artificial intelligence in the field of orthodontics is automated cephalometric analysis. Aim of the present study was to evaluate whether developmental stages of a dentition, fixed orthodontic appliances or other dental appliances may affect detection of cephalometric landmarks. METHODS For the purposes of this study a Convolutional Neural Network (CNN) for automated detection of cephalometric landmarks was developed. The model was trained on 430 cephalometric radiographs and its performance was then tested on 460 new radiographs. The accuracy of landmark detection in patients with permanent dentition was compared with that in patients with mixed dentition. Furthermore, the influence of fixed orthodontic appliances and orthodontic brackets and/or bands was investigated only in patients with permanent dentition. A t-test was performed to evaluate the mean radial errors (MREs) against the corresponding SDs for each landmark in the two categories, of which the significance was set at p < 0.05. RESULTS The study showed significant differences in the recognition accuracy of the Ap-Inferior point and the Is-Superior point between patients with permanent dentition and mixed dentition, and no significant differences in the recognition process between patients without fixed orthodontic appliances and patients with orthodontic brackets and/or bands and other fixed orthodontic appliances. CONCLUSIONS The results indicated that growth structures and developmental stages of a dentition had an impact on the performance of the customized CNN model by dental cephalometric landmarks. Fixed orthodontic appliances such as brackets, bands, and other fixed orthodontic appliances, had no significant effect on the performance of the CNN model.
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Affiliation(s)
- Teodora Popova
- Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany
| | - Thomas Stocker
- Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany
| | - Yeganeh Khazaei
- Department of Statistics, Statistical Consultation Unit, StaBLab, LMU Munich, Akademiestr. 1, 80799, Munich, Germany
| | - Yoana Malenova
- Department of Oral and Maxillofacial Surgery, University Hospital, LMU Munich, Lindwurmstrasse 2a, 80337, Munich, Germany
| | - Andrea Wichelhaus
- Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany
| | - Hisham Sabbagh
- Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany.
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10
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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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Strunga M, Urban R, Surovková J, Thurzo A. Artificial Intelligence Systems Assisting in the Assessment of the Course and Retention of Orthodontic Treatment. Healthcare (Basel) 2023; 11:healthcare11050683. [PMID: 36900687 PMCID: PMC10000479 DOI: 10.3390/healthcare11050683] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 02/17/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023] Open
Abstract
This scoping review examines the contemporary applications of advanced artificial intelligence (AI) software in orthodontics, focusing on its potential to improve daily working protocols, but also highlighting its limitations. The aim of the review was to evaluate the accuracy and efficiency of current AI-based systems compared to conventional methods in diagnosing, assessing the progress of patients' treatment and follow-up stability. The researchers used various online databases and identified diagnostic software and dental monitoring software as the most studied software in contemporary orthodontics. The former can accurately identify anatomical landmarks used for cephalometric analysis, while the latter enables orthodontists to thoroughly monitor each patient, determine specific desired outcomes, track progress, and warn of potential changes in pre-existing pathology. However, there is limited evidence to assess the stability of treatment outcomes and relapse detection. The study concludes that AI is an effective tool for managing orthodontic treatment from diagnosis to retention, benefiting both patients and clinicians. Patients find the software easy to use and feel better cared for, while clinicians can make diagnoses more easily and assess compliance and damage to braces or aligners more quickly and frequently.
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Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis. Healthcare (Basel) 2022; 10:healthcare10071269. [PMID: 35885796 PMCID: PMC9320442 DOI: 10.3390/healthcare10071269] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/25/2022] [Accepted: 06/30/2022] [Indexed: 12/29/2022] Open
Abstract
This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.
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