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Danz JC, Stöckli S, Rank CP. Precision and accuracy of craniofacial growth and orthodontic treatment evaluation by digital image correlation: a prospective cohort study. FRONTIERS IN ORAL HEALTH 2024; 5:1419481. [PMID: 39130491 PMCID: PMC11310159 DOI: 10.3389/froh.2024.1419481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 06/28/2024] [Indexed: 08/13/2024] Open
Abstract
Introduction A precise and accurate method for structural superimposition is essential for analyzing dentofacial growth and orthodontic or surgical treatment in longitudinal studies. The errors associated with different superimposition methods have not yet been assessed in high-quality studies. Objectives This study aimed to assess the precision and accuracy of digital image correlation (DIC) for structural superimposition. Methods Two cephalometric images from 30 consecutive patients were superimposed using three DIC methods, each measured twice by two examiners. Areas including the contours of the sella, the whole cranial base (CB), and Walker's point and lamina cribrosa (WPLC) were compared using a random coefficient model. Inter-rater and intra-rater errors were assessed for each method. Results WPLC provided the best precision for image rotation and cephalometric landmarks. Systematic bias was observed between the WPLC and CB methods for image rotation and most landmarks. The intra-rater error in image rotation during DIC was strongly correlated with the intra-rater error in the landmarks of the anterior nasal spine, articulare, and pogonion. Conclusion Structural superimposition using DIC with WPLC is a precise method for analyzing dentofacial growth and orthodontic or surgical treatment. Moreover, the best method is the measurement of longitudinal dental and craniofacial changes on structurally superimposed cephalometric radiographs with WPLC and a reference grid including the true vertical and horizontal lines from Walker's point.
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Affiliation(s)
- Jan Christian Danz
- Department of Orthodontics and Dentofacial Orthopedics, School of Dental Medicine ZMK, University of Bern, Bern, Switzerland
| | - Simone Stöckli
- Department of Orthodontics and Dentofacial Orthopedics, School of Dental Medicine ZMK, University of Bern, Bern, Switzerland
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Polizzi A, Leonardi R. Automatic cephalometric landmark identification with artificial intelligence: An umbrella review of systematic reviews. J Dent 2024; 146:105056. [PMID: 38729291 DOI: 10.1016/j.jdent.2024.105056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/25/2024] [Accepted: 05/07/2024] [Indexed: 05/12/2024] Open
Abstract
OBJECTIVES The transition from manual to automatic cephalometric landmark identification has not yet reached a consensus for clinical application in orthodontic diagnosis. The present umbrella review aimed to assess artificial intelligence (AI) performance in automatic 2D and 3D cephalometric landmark identification. DATA A combination of free text words and MeSH keywords pooled by boolean operators: Automa* AND cephalo* AND ("artificial intelligence" OR "machine learning" OR "deep learning" OR "learning"). SOURCES A search strategy without a timeframe setting was conducted on PubMed, Scopus, Web of Science, Cochrane Library and LILACS. STUDY SELECTION The study protocol followed the PRISMA guidelines and the PICO question was formulated according to the aim of the article. The database search led to the selection of 15 articles that were assessed for eligibility in full-text. Finally, 11 systematic reviews met the inclusion criteria and were analyzed according to the risk of bias in systematic reviews (ROBIS) tool. CONCLUSIONS AI was not able to identify the various cephalometric landmarks with the same accuracy. Since most of the included studies' conclusions were based on a wrong 2 mm cut-off difference between the AI automatic landmark location and that allocated by human operators, future research should focus on refining the most powerful architectures to improve the clinical relevance of AI-driven automatic cephalometric analysis. CLINICAL SIGNIFICANCE Despite a progressively improved performance, AI has exceeded the recommended magnitude of error for most cephalometric landmarks. Moreover, AI automatic landmarking on 3D CBCT appeared to be less accurate compared to that on 2D X-rays. To date, AI-driven cephalometric landmarking still requires the final supervision of an experienced orthodontist.
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Affiliation(s)
- Alessandro Polizzi
- Department of General Surgery and Medical-Surgical Specialties, Section of Orthodontics, University of Catania, Policlinico Universitario "Gaspare Rodolico - San Marco", Via Santa Sofia 78, 95123, Catania, Italy.
| | - Rosalia Leonardi
- Department of General Surgery and Medical-Surgical Specialties, Section of Orthodontics, University of Catania, Policlinico Universitario "Gaspare Rodolico - San Marco", Via Santa Sofia 78, 95123, Catania, Italy
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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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Takahashi K, Shimamura Y, Tachiki C, Nishii Y, Hagiwara M. Cephalometric landmark detection without X-rays combining coordinate regression and heatmap regression. Sci Rep 2023; 13:20011. [PMID: 37974018 PMCID: PMC10654665 DOI: 10.1038/s41598-023-46919-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 11/07/2023] [Indexed: 11/19/2023] Open
Abstract
Fully automated techniques using convolutional neural networks for cephalometric landmark detection have recently advanced. However, all existing studies have adopted X-rays. The problem of direct exposure of patients to X-ray radiation remains unsolved. We propose a model for detecting cephalometric landmarks using only facial profile images without X-rays. First, the model estimates the landmark coordinates using the features of facial profile images through high-resolution representation learning. Second, considering the spatial relationship of the landmarks, the model refines the estimated coordinates. The estimated coordinates are input into fully connected networks to improve the accuracy. During the experiment, a total of 2000 facial profile images collected from 2000 female patients were used. Experiments results suggested that the proposed method may perform at a level equal to or potentially better than existing methods using cephalograms. We obtained an MRE of 0.61 mm for the test data and a mean detection rate of 98.20% within 2 mm. Our proposed two-stage learning method enables a highly accurate estimation of the landmark positions using only facial profile images. The results indicate that X-rays may not be required when detecting cephalometric landmarks.
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Affiliation(s)
- Kaisei Takahashi
- Department of Information and Computer Science, Faculty of Science and Technology, Keio University, Kanagawa, 223-8522, Japan.
| | - Yui Shimamura
- Department of Orthodontics, Tokyo Dental College, Tokyo, 101-0061, Japan
| | - Chie Tachiki
- Department of Orthodontics, Tokyo Dental College, Tokyo, 101-0061, Japan
| | - Yasushi Nishii
- Department of Orthodontics, Tokyo Dental College, Tokyo, 101-0061, Japan
| | - Masafumi Hagiwara
- Department of Information and Computer Science, Faculty of Science and Technology, Keio University, Kanagawa, 223-8522, Japan
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Hong W, Kim SM, Choi J, Ahn J, Paeng JY, Kim H. Automated Cephalometric Landmark Detection Using Deep Reinforcement Learning. J Craniofac Surg 2023; 34:2336-2342. [PMID: 37622568 DOI: 10.1097/scs.0000000000009685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 06/25/2023] [Indexed: 08/26/2023] Open
Abstract
Accurate cephalometric landmark detection leads to accurate analysis, diagnosis, and surgical planning. Many studies on automated landmark detection have been conducted, however reinforcement learning-based networks have not yet been applied. This is the first study to apply deep Q-network (DQN) and double deep Q-network (DDQN) to automated cephalometric landmark detection to the best of our knowledge. The performance of the DQN-based network for cephalometric landmark detection was evaluated using the IEEE International Symposium of Biomedical Imaging (ISBI) 2015 Challenge data set and compared with the previously proposed methods. Furthermore, the clinical applicability of DQN-based automated cephalometric landmark detection was confirmed by testing the DQN-based and DDQN-based network using 500-patient data collected in a clinic. The DQN-based network demonstrated that the average mean radius error of 19 landmarks was smaller than 2 mm, that is, the clinically accepted level, without data augmentation and additional preprocessing. Our DQN-based and DDQN-based approaches tested with the 500-patient data set showed the average success detection rate of 67.33% and 66.04% accuracy within 2 mm, respectively, indicating the feasibility and potential of clinical application.
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Affiliation(s)
- Woojae Hong
- Department of Biomechatronic Engineering, Sungkyunkwan University, Suwon, Gyeonggi
| | - Seong-Min Kim
- Department of Biomechatronic Engineering, Sungkyunkwan University, Suwon, Gyeonggi
| | - Joongyeon Choi
- Department of Biomechatronic Engineering, Sungkyunkwan University, Suwon, Gyeonggi
| | - Jaemyung Ahn
- Department of Oral and Maxillofacial Surgery, Samsung Medical Center, Seoul, Republic of Korea
| | - Jun-Young Paeng
- Department of Oral and Maxillofacial Surgery, Samsung Medical Center, Seoul, Republic of Korea
| | - Hyunggun Kim
- Department of Biomechatronic Engineering, Sungkyunkwan University, Suwon, Gyeonggi
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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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Elkhill C, Liu J, Linguraru MG, LeBeau S, Khechoyan D, French B, Porras AR. Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107689. [PMID: 37393741 PMCID: PMC10527531 DOI: 10.1016/j.cmpb.2023.107689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 05/11/2023] [Accepted: 06/22/2023] [Indexed: 07/04/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate and repeatable detection of craniofacial landmarks is crucial for automated quantitative evaluation of head development anomalies. Since traditional imaging modalities are discouraged in pediatric patients, 3D photogrammetry has emerged as a popular and safe imaging alternative to evaluate craniofacial anomalies. However, traditional image analysis methods are not designed to operate on unstructured image data representations such as 3D photogrammetry. METHODS We present a fully automated pipeline to identify craniofacial landmarks in real time, and we use it to assess the head shape of patients with craniosynostosis using 3D photogrammetry. To detect craniofacial landmarks, we propose a novel geometric convolutional neural network based on Chebyshev polynomials to exploit the point connectivity information in 3D photogrammetry and quantify multi-resolution spatial features. We propose a landmark-specific trainable scheme that aggregates the multi-resolution geometric and texture features quantified at every vertex of a 3D photogram. Then, we embed a new probabilistic distance regressor module that leverages the integrated features at every point to predict landmark locations without assuming correspondences with specific vertices in the original 3D photogram. Finally, we use the detected landmarks to segment the calvaria from the 3D photograms of children with craniosynostosis, and we derive a new statistical index of head shape anomaly to quantify head shape improvements after surgical treatment. RESULTS We achieved an average error of 2.74 ± 2.70 mm identifying Bookstein Type I craniofacial landmarks, which is a significant improvement compared to other state-of-the-art methods. Our experiments also demonstrated a high robustness to spatial resolution variability in the 3D photograms. Finally, our head shape anomaly index quantified a significant reduction of head shape anomalies as a consequence of surgical treatment. CONCLUSION Our fully automated framework provides real-time craniofacial landmark detection from 3D photogrammetry with state-of-the-art accuracy. In addition, our new head shape anomaly index can quantify significant head phenotype changes and can be used to quantitatively evaluate surgical treatment in patients with craniosynostosis.
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Affiliation(s)
- Connor Elkhill
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA.
| | - Jiawei Liu
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Marius George Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, 7144 13th Pl NW, Washington, DC 20012, USA; Departments of Radiology and Pediatrics, George Washington University School of Medicine and Health Sciences, Ross Hall, 2300 Eye Street, NW, Washington, DC 20037, USA
| | - Scott LeBeau
- Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA
| | - David Khechoyan
- Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA; Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA
| | - Brooke French
- Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA; Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA
| | - Antonio R Porras
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; Department of Pediatric Plastic and Reconstructive Surgery, Children's Hospital Colorado, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA; Department of Surgery, School of Medicine, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA; Department of Biomedical Informatics, School of Medicine, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA; Department of Pediatrics and Department of Neurosurgery, School of Medicine, University of Colorado Anschutz Medical Campus, 13123 E 16th Ave, Aurora, CO 80045, USA
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Lee H, Cho JM, Ryu S, Ryu S, Chang E, Jung YS, Kim JY. Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: a comparative study with human experts. Sci Rep 2023; 13:15506. [PMID: 37726392 PMCID: PMC10509166 DOI: 10.1038/s41598-023-42870-z] [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: 03/21/2023] [Accepted: 09/15/2023] [Indexed: 09/21/2023] Open
Abstract
This study aimed to propose a fully automatic posteroanterior (PA) cephalometric landmark identification model using deep learning algorithms and compare its accuracy and reliability with those of expert human examiners. In total, 1032 PA cephalometric images were used for model training and validation. Two human expert examiners independently and manually identified 19 landmarks on 82 test set images. Similarly, the constructed artificial intelligence (AI) algorithm automatically identified the landmarks on the images. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the performance of the model. The performance of the model was comparable with that of the examiners. The MRE of the model was 1.87 ± 1.53 mm, and the SDR was 34.7%, 67.5%, and 91.5% within error ranges of < 1.0, < 2.0, and < 4.0 mm, respectively. The sphenoid points and mastoid processes had the lowest MRE and highest SDR in auto-identification; the condyle points had the highest MRE and lowest SDR. Comparable with human examiners, the fully automatic PA cephalometric landmark identification model showed promising accuracy and reliability and can help clinicians perform cephalometric analysis more efficiently while saving time and effort. Future advancements in AI could further improve the model accuracy and efficiency.
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Affiliation(s)
- Hwangyu Lee
- Department of Oral and Maxillofacial Surgery, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Jung Min Cho
- Department of Oral and Maxillofacial Surgery, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Susie Ryu
- Research and Development Team, Laon Medi Inc., 404 Park B, 723 Pangyo-ro, Bundang-gu, Seongnam-si, 13511, South Korea
| | - Seungmin Ryu
- Department of Orthodontics, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Euijune Chang
- Department of Oral and Maxillofacial Surgery, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Young-Soo Jung
- Department of Oral and Maxillofacial Surgery, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Jun-Young Kim
- Department of Oral and Maxillofacial Surgery, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
- Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, 03722, South Korea.
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Weingart JV, Schlager S, Metzger MC, Brandenburg LS, Hein A, Schmelzeisen R, Bamberg F, Kim S, Kellner E, Reisert M, Russe MF. Automated detection of cephalometric landmarks using deep neural patchworks. Dentomaxillofac Radiol 2023; 52:20230059. [PMID: 37427585 PMCID: PMC10461263 DOI: 10.1259/dmfr.20230059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/25/2023] [Accepted: 05/13/2023] [Indexed: 07/11/2023] Open
Abstract
OBJECTIVES This study evaluated the accuracy of deep neural patchworks (DNPs), a deep learning-based segmentation framework, for automated identification of 60 cephalometric landmarks (bone-, soft tissue- and tooth-landmarks) on CT scans. The aim was to determine whether DNP could be used for routine three-dimensional cephalometric analysis in diagnostics and treatment planning in orthognathic surgery and orthodontics. METHODS Full skull CT scans of 30 adult patients (18 female, 12 male, mean age 35.6 years) were randomly divided into a training and test data set (each n = 15). Clinician A annotated 60 landmarks in all 30 CT scans. Clinician B annotated 60 landmarks in the test data set only. The DNP was trained using spherical segmentations of the adjacent tissue for each landmark. Automated landmark predictions in the separate test data set were created by calculating the center of mass of the predictions. The accuracy of the method was evaluated by comparing these annotations to the manual annotations. RESULTS The DNP was successfully trained to identify all 60 landmarks. The mean error of our method was 1.94 mm (SD 1.45 mm) compared to a mean error of 1.32 mm (SD 1.08 mm) for manual annotations. The minimum error was found for landmarks ANS 1.11 mm, SN 1.2 mm, and CP_R 1.25 mm. CONCLUSION The DNP-algorithm was able to accurately identify cephalometric landmarks with mean errors <2 mm. This method could improve the workflow of cephalometric analysis in orthodontics and orthognathic surgery. Low training requirements while still accomplishing high precision make this method particularly promising for clinical use.
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Affiliation(s)
- Julia Vera Weingart
- Department of Oral and Maxillofacial Surgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Stefan Schlager
- Department of Oral and Maxillofacial Surgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marc Christian Metzger
- Department of Oral and Maxillofacial Surgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Leonard Simon Brandenburg
- Department of Oral and Maxillofacial Surgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Anna Hein
- Department of Oral and Maxillofacial Surgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Rainer Schmelzeisen
- Department of Oral and Maxillofacial Surgery, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Suam Kim
- Department of Diagnostic and Interventional Radiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Elias Kellner
- Department of Medical Physics, Faculty of Medicine, Medical Center – University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Marco Reisert
- Department of Medical Physics, Faculty of Medicine, Medical Center – University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Maximilian Frederik Russe
- Department of Diagnostic and Interventional Radiology, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Londono J, Ghasemi S, Hussain Shah A, Fahimipour A, Ghadimi N, Hashemi S, Khurshid Z, Dashti M. Evaluation of deep learning and convolutional neural network algorithms accuracy for detecting and predicting anatomical landmarks on 2D lateral cephalometric images: A systematic review and meta-analysis. Saudi Dent J 2023; 35:487-497. [PMID: 37520606 PMCID: PMC10373073 DOI: 10.1016/j.sdentj.2023.05.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/15/2023] [Accepted: 05/17/2023] [Indexed: 08/01/2023] Open
Abstract
Introduction Cephalometry is the study of skull measurements for clinical evaluation, diagnosis, and surgical planning. Machine learning (ML) algorithms have been used to accurately identify cephalometric landmarks and detect irregularities related to orthodontics and dentistry. ML-based cephalometric imaging reduces errors, improves accuracy, and saves time. Method In this study, we conducted a meta-analysis and systematic review to evaluate the accuracy of ML software for detecting and predicting anatomical landmarks on two-dimensional (2D) lateral cephalometric images. The meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for selecting and screening research articles. The eligibility criteria were established based on the diagnostic accuracy and prediction of ML combined with 2D lateral cephalometric imagery. The search was conducted among English articles in five databases, and data were managed using Review Manager software (v. 5.0). Quality assessment was performed using the diagnostic accuracy studies (QUADAS-2) tool. Result Summary measurements included the mean departure from the 1-4-mm threshold or the percentage of landmarks identified within this threshold with a 95% confidence interval (CI). This meta-analysis included 21 of 577 articles initially collected on the accuracy of ML algorithms for detecting and predicting anatomical landmarks. The studies were conducted in various regions of the world, and 20 of the studies employed convolutional neural networks (CNNs) for detecting cephalometric landmarks. The pooled successful detection rates for the 1-mm, 2-mm, 2.5-mm, 3-mm, and 4-mm ranges were 65%, 81%, 86%, 91%, and 96%, respectively. Heterogeneity was determined using the random effect model. Conclusion In conclusion, ML has shown promise for landmark detection in 2D cephalometric imagery, although the accuracy has varied among studies and clinicians. Consequently, more research is required to determine its effectiveness and reliability in clinical settings.
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Affiliation(s)
- Jimmy Londono
- FACP, Professor and Director of the Prosthodontics Residency Program and the Ronald Goldstein Center for Esthetics and Implant Dentistry, Dental College of Georgia at Augusta University, Augusta, GA, United States
| | - Shohreh Ghasemi
- Department of Oral and Maxillofacial Surgery, The Dental College of Georgia at Augusta University, Augusta, GA, United States
| | - Altaf Hussain Shah
- Special Care Dentistry Clinics, University Dental Hospital, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Amir Fahimipour
- School of Dentistry, Faculty of Medicine and Health, Westmead Centre for Oral Health, The University of Sydney, NSW 2145, Australia
| | - Niloofar Ghadimi
- Department of Oral and Maxillofacial Radiology, Dental School, Islamic Azad University of Medical Sciences, Tehran, Iran
| | - Sara Hashemi
- School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Zohaib Khurshid
- Department of Prosthodontics and Dental Implantology, King Faisal University, Al-Ahsa 31982, Saudi Arabia
- Center of Excellence for Regenerative Dentistry, Department of Anatomy, Faculty of Dentistry, Chulalongkorn University, Bangkok 10330, Thailand
| | - Mahmood Dashti
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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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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Nishimoto S, Saito T, Ishise H, Fujiwara T, Kawai K, Kakibuchi M. Three-Dimensional Craniofacial Landmark Detection in Series of CT Slices Using Multi-Phased Regression Networks. Diagnostics (Basel) 2023; 13:diagnostics13111930. [PMID: 37296782 DOI: 10.3390/diagnostics13111930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 05/26/2023] [Accepted: 05/28/2023] [Indexed: 06/12/2023] Open
Abstract
Geometrical assessments of human skulls have been conducted based on anatomical landmarks. If developed, the automatic detection of these landmarks will yield both medical and anthropological benefits. In this study, an automated system with multi-phased deep learning networks was developed to predict the three-dimensional coordinate values of craniofacial landmarks. Computed tomography images of the craniofacial area were obtained from a publicly available database. They were digitally reconstructed into three-dimensional objects. Sixteen anatomical landmarks were plotted on each of the objects, and their coordinate values were recorded. Three-phased regression deep learning networks were trained using ninety training datasets. For the evaluation, 30 testing datasets were employed. The 3D error for the first phase, which tested 30 data, was 11.60 px on average (1 px = 500/512 mm). For the second phase, it was significantly improved to 4.66 px. For the third phase, it was further significantly reduced to 2.88. This was comparable to the gaps between the landmarks, as plotted by two experienced practitioners. Our proposed method of multi-phased prediction, which conducts coarse detection first and narrows down the detection area, may be a possible solution to prediction problems, taking into account the physical limitations of memory and computation.
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Affiliation(s)
- Soh Nishimoto
- Department of Plastic Surgery, Hyogo Medical University, Nishinomiya 663-8501, Japan
| | - Takuya Saito
- Department of Plastic Surgery, Hyogo Medical University, Nishinomiya 663-8501, Japan
| | - Hisako Ishise
- Department of Plastic Surgery, Hyogo Medical University, Nishinomiya 663-8501, Japan
| | - Toshihiro Fujiwara
- Department of Plastic Surgery, Hyogo Medical University, Nishinomiya 663-8501, Japan
| | - Kenichiro Kawai
- Department of Plastic Surgery, Hyogo Medical University, Nishinomiya 663-8501, Japan
| | - Masao Kakibuchi
- Department of Plastic Surgery, Hyogo Medical University, Nishinomiya 663-8501, Japan
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Blum FMS, Möhlhenrich SC, Raith S, Pankert T, Peters F, Wolf M, Hölzle F, Modabber A. Evaluation of an artificial intelligence-based algorithm for automated localization of craniofacial landmarks. Clin Oral Investig 2023; 27:2255-2265. [PMID: 37014502 PMCID: PMC10159965 DOI: 10.1007/s00784-023-04978-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 03/21/2023] [Indexed: 04/05/2023]
Abstract
OBJECTIVES Due to advancing digitalisation, it is of interest to develop standardised and reproducible fully automated analysis methods of cranial structures in order to reduce the workload in diagnosis and treatment planning and to generate objectifiable data. The aim of this study was to train and evaluate an algorithm based on deep learning methods for fully automated detection of craniofacial landmarks in cone-beam computed tomography (CBCT) in terms of accuracy, speed, and reproducibility. MATERIALS AND METHODS A total of 931 CBCTs were used to train the algorithm. To test the algorithm, 35 landmarks were located manually by three experts and automatically by the algorithm in 114 CBCTs. The time and distance between the measured values and the ground truth previously determined by an orthodontist were analyzed. Intraindividual variations in manual localization of landmarks were determined using 50 CBCTs analyzed twice. RESULTS The results showed no statistically significant difference between the two measurement methods. Overall, with a mean error of 2.73 mm, the AI was 2.12% better and 95% faster than the experts. In the area of bilateral cranial structures, the AI was able to achieve better results than the experts on average. CONCLUSION The achieved accuracy of automatic landmark detection was in a clinically acceptable range, is comparable in precision to manual landmark determination, and requires less time. CLINICAL RELEVANCE Further enlargement of the database and continued development and optimization of the algorithm may lead to ubiquitous fully automated localization and analysis of CBCT datasets in future routine clinical practice.
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Affiliation(s)
| | | | - Stefan Raith
- Department of Maxillofacial Surgery, RWTH Aachen University, Aachen, Germany
| | - Tobias Pankert
- Department of Maxillofacial Surgery, RWTH Aachen University, Aachen, Germany
| | - Florian Peters
- Department of Maxillofacial Surgery, RWTH Aachen University, Aachen, Germany
| | - Michael Wolf
- Department of Orthodontics, University Hospital of RWTH Aachen, Pauwelsstraße 30, D-52074, Aachen, Germany
| | - Frank Hölzle
- Department of Maxillofacial Surgery, RWTH Aachen University, Aachen, Germany
| | - Ali Modabber
- Department of Maxillofacial Surgery, RWTH Aachen University, Aachen, Germany
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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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Ao Y, Wu H. Feature Aggregation and Refinement Network for 2D Anatomical Landmark Detection. J Digit Imaging 2023; 36:547-561. [PMID: 36401132 PMCID: PMC10039137 DOI: 10.1007/s10278-022-00718-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 11/19/2022] Open
Abstract
Localization of anatomical landmarks is essential for clinical diagnosis, treatment planning, and research. This paper proposes a novel deep network named feature aggregation and refinement network (FARNet) for automatically detecting anatomical landmarks. FARNet employs an encoder-decoder structure architecture. To alleviate the problem of limited training data in the medical domain, we adopt a backbone network pre-trained on natural images as the encoder. The decoder includes a multi-scale feature aggregation module for multi-scale feature fusion and a feature refinement module for high-resolution heatmap regression. Coarse-to-fine supervisions are applied to the two modules to facilitate end-to-end training. We further propose a novel loss function named Exponential Weighted Center loss for accurate heatmap regression, which focuses on the losses from the pixels near landmarks and suppresses the ones from far away. We evaluate FARNet on three publicly available anatomical landmark detection datasets, including cephalometric, hand, and spine radiographs. Our network achieves state-of-the-art performances on all three datasets. Code is available at https://github.com/JuvenileInWind/FARNet .
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Affiliation(s)
- Yueyuan Ao
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731 China
| | - Hong Wu
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731 China
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Chang Q, Wang Z, Wang F, Dou J, Zhang Y, Bai Y. Automatic analysis of lateral cephalograms based on high-resolution net. Am J Orthod Dentofacial Orthop 2022; 163:501-508.e4. [PMID: 36528536 DOI: 10.1016/j.ajodo.2022.02.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 02/01/2022] [Accepted: 02/01/2022] [Indexed: 12/23/2022]
Abstract
INTRODUCTION Cephalometric analysis is essential in orthodontic treatment, and it is progressing toward automatic cephalometric analysis. This study aimed to establish a cephalometric landmark detection model on the basis of a high-resolution net and improve the accuracy with high resolution. METHODS A total of 2000 lateral cephalograms were collected to construct a dataset, and the number of target landmarks was 51. A high-resolution network model was applied to the landmark detection task. Four models were trained by adjusting different input resolutions to choose the most suitable resolution. A test set consisting of 300 lateral cephalograms was used for evaluation. The model was evaluated from the error size and distribution of each landmark. RESULTS After 200 epochs of training, a landmark detection model was established. Under different resolutions of the input image, the mean model radial error decreased initially and then increased. At 680 × 920 pixels resolution, the minimum error and the highest detection success rate were obtained. The mean radial error was 1.08 ± 0.87 mm. The detection success rates of 2.0 mm, 2.5 mm, 3.0 mm, and 4.0 mm were 89.00%, 94.00%, 96.33%, and 98.67%, respectively. The mean radial errors of 22 landmarks were <1 mm, and the errors of other landmarks were <2 mm except for the pterion. The error distribution of landmarks followed a certain pattern. CONCLUSIONS An automatic landmark detection model based on a high-resolution net was established to recognize 51 landmarks. The model showed high detection accuracy, which provides a basis for further measurement application.
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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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Ghowsi A, Hatcher D, Suh H, Wile D, Castro W, Krueger J, Park J, Oh H. Automated landmark identification on cone-beam computed tomography: Accuracy and reliability. Angle Orthod 2022; 92:482422. [PMID: 35653226 PMCID: PMC9374352 DOI: 10.2319/122121-928.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 04/01/2022] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVES To evaluate the accuracy and reliability of a fully automated landmark identification (ALI) system as a tool for automatic landmark location compared with human judges. MATERIALS AND METHODS A total of 100 cone-beam computed tomography (CBCT) images were collected. After the calibration procedure, two human judges identified 53 landmarks in the x, y, and z coordinate planes on CBCTs using Checkpoint Software (Stratovan Corporation, Davis, Calif). The ground truth was created by averaging landmark coordinates identified by two human judges for each landmark. To evaluate the accuracy of ALI, the mean absolute error (mm) at the x, y, and z coordinates and mean error distance (mm) between the human landmark identification and the ALI were determined, and a successful detection rate was calculated. RESULTS Overall, the ALI system was as successful at landmarking as the human judges. The ALI's mean absolute error for all coordinates was 1.57 mm on average. Across all three coordinate planes, 94% of the landmarks had a mean absolute error of less than 3 mm. The mean error distance for all 53 landmarks was 3.19 ± 2.6 mm. When applied to 53 landmarks on 100 CBCTs, the ALI system showed a 75% success rate in detecting landmarks within a 4-mm error distance range. CONCLUSIONS Overall, ALI showed clinically acceptable mean error distances except for a few landmarks. The ALI was more precise than humans when identifying landmarks on the same image at different times. This study demonstrates the promise of ALI in aiding orthodontists with landmark identifications on CBCTs.
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Lu G, Zhang Y, Kong Y, Zhang C, Coatrieux JL, Shu H. Landmark Localization for Cephalometric Analysis using Multiscale Image Patch-based Graph Convolutional Networks. IEEE J Biomed Health Inform 2022; 26:3015-3024. [PMID: 35259123 DOI: 10.1109/jbhi.2022.3157722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Accurate and robust cephalometric image analysis plays an essential role in orthodontic diagnosis, treatment assessment and surgical planning. This paper proposes a novel landmark localization method for cephalometric analysis using multiscale image patch-based graph convolutional networks. In detail, image patches with the same size are hierarchically sampled from the Gaussian pyramid to well preserve multiscale context information. We combine local appearance and shape information into spatialized features with an attention module to enrich node representations in graph. The spatial relationships of landmarks are built with the incorporation of three-layer graph convolutional networks, and multiple landmarks are simultaneously updated and moved toward the targets in a cascaded coarse-to-fine process. Quantitative results obtained on publicly available cephalometric X-ray images have exhibited superior performance compared with other state-of-the-art methods in terms of mean radial error and successful detection rate within various precision ranges. Our approach performs significantly better especially in the clinically accepted range of 2 mm and this makes it suitable in cephalometric analysis and orthognathic surgery.
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Gil SM, Kim I, Cho JH, Hong M, Kim M, Kim SJ, Kim YJ, Kim YH, Lim SH, Sung SJ, Baek SH, Kim N, Kang KH. Accuracy of auto-identification of the posteroanterior cephalometric landmarks using cascade convolution neural network algorithm and cephalometric images of different quality from nationwide multiple centers. Am J Orthod Dentofacial Orthop 2022; 161:e361-e371. [DOI: 10.1016/j.ajodo.2021.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 11/01/2021] [Accepted: 11/01/2021] [Indexed: 12/01/2022]
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Yao J, Zeng W, He T, Zhou S, Zhang Y, Guo J, Tang W. Automatic localization of cephalometric landmarks based on convolutional neural network. Am J Orthod Dentofacial Orthop 2021; 161:e250-e259. [PMID: 34802868 DOI: 10.1016/j.ajodo.2021.09.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 09/01/2021] [Accepted: 09/01/2021] [Indexed: 11/01/2022]
Abstract
INTRODUCTION Cephalometry plays an important role in the diagnosis and treatment of orthodontics and orthognathic surgery. This study intends to develop an automatic landmark location system to make cephalometry more convenient. METHODS In this study, 512 lateral cephalograms were collected, and 37 landmarks were included. The coordinates of all landmarks in the 512 films were obtained to establish a labeled dataset: 312 were used as a training set, 100 as a validation set, and 100 as a testing set. An automatic landmark location system based on the convolutional neural network was developed. This system consisted of a global detection module and a locally modified module. The lateral cephalogram was first fed into the global module to obtain an initial estimate of the landmark's position, which was then adjusted with the locally modified module to improve accuracy. Mean radial error (MRE) and success detection rate (SDR) within the range of 1-4 mm were used to evaluate the method. RESULTS The MRE of our validation set was 1.127 ± 1.028 mm, and SDR of 1.0, 1.5, 2.0, 2.5, 3.0, and 4.0 mm were respectively 45.95%, 89.19%, 97.30%, 97.30%, and 97.30%. The MRE of our testing set was 1.038 ± 0.893 mm, and SDR of 1.0, 1.5, 2.0, 2.5, 3.0, and 4.0 mm were respectively 54.05%, 91.89%, 97.30%, 100%, 100%, and 100%. CONCLUSIONS In this study, we proposed a new automatic landmark location system on the basis of the convolutional neural network. The system could detect 37 landmarks with high accuracy. All landmarks are commonly used in clinical practice and could meet the requirements of different cephalometric analysis methods.
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Affiliation(s)
- Jie Yao
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, Shaanxi, China State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases and Department of Oral and Maxillofacial Surgery, West China College of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Wei Zeng
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases and Department of Oral and Maxillofacial Surgery, West China College of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Tao He
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Shanluo Zhou
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases and Department of Oral and Maxillofacial Surgery, West China College of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Yi Zhang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Jixiang Guo
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, China.
| | - Wei Tang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases and Department of Oral and Maxillofacial Surgery, West China College of Stomatology, Sichuan University, Chengdu, Sichuan, China.
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Kim YH, Lee C, Ha EG, Choi YJ, Han SS. A fully deep learning model for the automatic identification of cephalometric landmarks. Imaging Sci Dent 2021; 51:299-306. [PMID: 34621657 PMCID: PMC8479429 DOI: 10.5624/isd.20210077] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/24/2021] [Accepted: 05/28/2021] [Indexed: 11/30/2022] Open
Abstract
Purpose This study aimed to propose a fully automatic landmark identification model based on a deep learning algorithm using real clinical data and to verify its accuracy considering inter-examiner variability. Materials and Methods In total, 950 lateral cephalometric images from Yonsei Dental Hospital were used. Two calibrated examiners manually identified the 13 most important landmarks to set as references. The proposed deep learning model has a 2-step structure—a region of interest machine and a detection machine—each consisting of 8 convolution layers, 5 pooling layers, and 2 fully connected layers. The distance errors of detection between 2 examiners were used as a clinically acceptable range for performance evaluation. Results The 13 landmarks were automatically detected using the proposed model. Inter-examiner agreement for all landmarks indicated excellent reliability based on the 95% confidence interval. The average clinically acceptable range for all 13 landmarks was 1.24 mm. The mean radial error between the reference values assigned by 1 expert and the proposed model was 1.84 mm, exhibiting a successful detection rate of 36.1%. The A-point, the incisal tip of the maxillary and mandibular incisors, and ANS showed lower mean radial error than the calibrated expert variability. Conclusion This experiment demonstrated that the proposed deep learning model can perform fully automatic identification of cephalometric landmarks and achieve better results than examiners for some landmarks. It is meaningful to consider between-examiner variability for clinical applicability when evaluating the performance of deep learning methods in cephalometric landmark identification.
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Affiliation(s)
- Young Hyun Kim
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea
| | - Chena Lee
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea
| | - Eun-Gyu Ha
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea
| | - Yoon Jeong Choi
- Department of Orthodontics, Institute of Craniofacial Deformity, Yonsei University College of Dentistry, Seoul, Korea
| | - Sang-Sun Han
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Korea.,Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul, Korea
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Kolsanov AV, Popov NV, Ayupova IO, Tsitsiashvili AM, Gaidel AV, Dobratulin KS. [Cephalometric analysis of lateral skull X-ray images using soft computing components in the search for key points]. STOMATOLOGII︠A︡ 2021; 100:63-67. [PMID: 34357730 DOI: 10.17116/stomat202110004163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
THE AIM OF THE STUDY Was to investigate the efficiency of decoding teleradiological studies using an algorithm based on the use of convolutional neural networks - a simple convolutional architecture, as well as an extended U-Net architecture. MATERIALS AND METHODS For the experiment, a dataset was prepared by three orthodontists with over 10 years of clinical experience. Each of the orthodontists processed 100 X-ray images of the lateral projection of the head according to 27 parameters, 2700 measurements were made. The coordinates of the control points found by orthodontists in the images were compared with each other and a conclusion was made about the consistency of experts in the data obtained. RESULTS The results of convolutional neural network CNN were not satisfactory in 17 (62.96%) features, satisfactory in 10 (37.04%). The assessment of orthodontists resulted in non-satisfactory evaluation in 6 (22.22%), satisfactory in 8 (29.63%), good in 8 (29.63%), and excellent in 5 (18.52%) coordinates. Neural networks with U-Net architecture showed satisfactory results in 9 (33.3%) cases, good in 16 (59.3%) and excellent in 2 (7.4%) cases, with no non-satisfactory results. CONCLUSION The neural network of the U-Net architecture is more effective than a simple fully convolutional neural network and its results of determining anatomical reference points on two-dimensional images of the head are relatively comparable with the data obtained by medical specialists.
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Affiliation(s)
| | - N V Popov
- Samara State Medical University, Samara, Russia
| | - I O Ayupova
- Samara State Medical University, Samara, Russia
| | - A M Tsitsiashvili
- A.I. Yevdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia
| | - A V Gaidel
- Samara National Research University, Samara, Russia
| | - K S Dobratulin
- National University of Science and Technology MISIS, Moscow, Russia
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Choi YJ, Lee KJ. Possibilities of artificial intelligence use in orthodontic diagnosis and treatment planning: Image recognition and three-dimensional VTO. Semin Orthod 2021. [DOI: 10.1053/j.sodo.2021.05.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Ren R, Luo H, Su C, Yao Y, Liao W. Machine learning in dental, oral and craniofacial imaging: a review of recent progress. PeerJ 2021; 9:e11451. [PMID: 34046262 PMCID: PMC8136280 DOI: 10.7717/peerj.11451] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 04/22/2021] [Indexed: 02/05/2023] Open
Abstract
Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.
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Affiliation(s)
- Ruiyang Ren
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China School of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Haozhe Luo
- School of Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Chongying Su
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China School of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Yang Yao
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Wen Liao
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Orthodontics, Osaka Dental University, Hirakata, Osaka, Japan
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Kim J, Kim I, Kim YJ, Kim M, Cho JH, Hong M, Kang KH, Lim SH, Kim SJ, Kim YH, Kim N, Sung SJ, Baek SH. Accuracy of automated identification of lateral cephalometric landmarks using cascade convolutional neural networks on lateral cephalograms from nationwide multi-centres. Orthod Craniofac Res 2021; 24 Suppl 2:59-67. [PMID: 33973341 DOI: 10.1111/ocr.12493] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/16/2021] [Accepted: 04/27/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To investigate the accuracy of automated identification of cephalometric landmarks using the cascade convolutional neural networks (CNN) on lateral cephalograms acquired from nationwide multi-centres. SETTINGS AND SAMPLE POPULATION A total of 3150 lateral cephalograms were acquired from 10 university hospitals in South Korea for training. MATERIALS AND METHODS We evaluated the accuracy of the developed model with independent 100 lateral cephalograms as an external validation. Two orthodontists independently identified the anatomic landmarks of the test data set using the V-ceph software (version 8.0, Osstem, Seoul, Korea). The mean positions of the landmarks identified by two orthodontists were regarded as the gold standard. The performance of the CNN model was evaluated by calculating the mean absolute distance between the gold standard and the automatically detected positions. Factors associated with the detection accuracy for landmarks were analysed using the linear regression models. RESULTS The mean inter-examiner difference was 1.31 ± 1.13 mm. The overall automated detection error was 1.36 ± 0.98 mm. The mean detection error for each landmark ranged between 0.46 ± 0.37 mm (maxillary incisor crown tip) and 2.09 ± 1.91 mm (distal root tip of the mandibular first molar). A significant difference in the detection accuracy among cephalograms was noted according to hospital (P = .011), sensor type (P < .01), and cephalography machine model (P < .01). CONCLUSION The automated cephalometric landmark detection model may aid in preliminary screening for patient diagnosis and mid-treatment assessment, independent of the type of the radiography machines tested.
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Affiliation(s)
- Jaerong Kim
- 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
| | - Minji Kim
- Department of Orthodontics, College of Medicine, Ewha Woman's University, Seoul, Korea
| | - Jin-Hyoung Cho
- Department of Orthodontics, Chonnam National University School of Dentistry, Gwangju, Korea
| | - Mihee Hong
- Department of Orthodontics, School of Dentistry, Kyungpook National University, Daegu, Korea
| | - Kyung-Hwa Kang
- Department of Orthodontics, School of Dentistry, Wonkwang University, Iksan, Korea
| | - Sung-Hoon Lim
- Department of Orthodontics, College of Dentistry, Chosun University, Gwangju, Korea
| | - Su-Jung Kim
- Department of Orthodontics, Kyung Hee University School of Dentistry, Seoul, Korea
| | - Young Ho Kim
- Department of Orthodontics, Institute of Oral Health Science, Ajou University School of Medicine, Suwon, 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
| | - Sang-Jin Sung
- Department of Orthodontics, 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
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Kolsanov AV, Popov NV, Ayupova IO, Ivleva AI. [Consistency of expert opinions on localization of the reference points for studying a soft tissue face profile in digital teleradiological images of the skull lateral projection]. STOMATOLOGIIA 2021; 100:49-54. [PMID: 34357728 DOI: 10.17116/stomat202110004149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
OBJECTIVE The aim of the study is to identify the consistency of expert opinions when manually identifying the reference points positions for studying the soft-tissue face profile in digital teleradiological images of the skull lateral projection. MATERIAL AND METHODS The study involved 11 orthodontists having a 1 to 8 year experience. They localized the reference points manually, using the software with image enhancement options. Each doctor processed 100 X-ray images of the skull lateral projection. Totally, 1100 positions were identified (11 parameters in 100 images). The average position of 11 manual localizations was taken as the baseline. Then, manually identified positions were automatically compared with the basic localization. RESULTS We have a good consistency of expert opinions when determining the reference points of the soft-tissue face profile. Herewith, the highest consistency of expert opinions is observed for soft-tissue reference points of the upper and lower lips (LL and EN points) with the average variation coefficients equal to 0.557726 and 0.566349 respectively; and the lowest consistency is observed for the hard tissue point Po with the average variation coefficient of 0.819904. Despite rather serious shortcomings in determining separate points (such as Or, DT, Po) in teleradiological images of the lateral skull projection, the method for determining soft-tissue profilometric reference points gives satisfactory results for more than 85.87% cases. In general, the method can be considered clinically reliable. However, the means and methods for identifying reference points require improving.
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Affiliation(s)
| | - N V Popov
- Samara State Medical University, Samara, Russia
| | - I O Ayupova
- Samara State Medical University, Samara, Russia
| | - A I Ivleva
- Samara Federal Research Scientific Center RAS, Samara, Russia
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Lee JH, Yu HJ, Kim MJ, Kim JW, Choi J. Automated cephalometric landmark detection with confidence regions using Bayesian convolutional neural networks. BMC Oral Health 2020; 20:270. [PMID: 33028287 PMCID: PMC7541217 DOI: 10.1186/s12903-020-01256-7] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 09/20/2020] [Indexed: 11/13/2022] Open
Abstract
Background Despite the integral role of cephalometric analysis in orthodontics, there have been limitations regarding the reliability, accuracy, etc. of cephalometric landmarks tracing. Attempts on developing automatic plotting systems have continuously been made but they are insufficient for clinical applications due to low reliability of specific landmarks. In this study, we aimed to develop a novel framework for locating cephalometric landmarks with confidence regions using Bayesian Convolutional Neural Networks (BCNN). Methods We have trained our model with the dataset from the ISBI 2015 grand challenge in dental X-ray image analysis. The overall algorithm consisted of a region of interest (ROI) extraction of landmarks and landmarks estimation considering uncertainty. Prediction data produced from the Bayesian model has been dealt with post-processing methods with respect to pixel probabilities and uncertainties. Results Our framework showed a mean landmark error (LE) of 1.53 ± 1.74 mm and achieved a successful detection rate (SDR) of 82.11, 92.28 and 95.95%, respectively, in the 2, 3, and 4 mm range. Especially, the most erroneous point in preceding studies, Gonion, reduced nearly halves of its error compared to the others. Additionally, our results demonstrated significantly higher performance in identifying anatomical abnormalities. By providing confidence regions (95%) that consider uncertainty, our framework can provide clinical convenience and contribute to making better decisions. Conclusion Our framework provides cephalometric landmarks and their confidence regions, which could be used as a computer-aided diagnosis tool and education.
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Affiliation(s)
- Jeong-Hoon Lee
- School of Mechanical Engineering, Yonsei University, 50 Yonsei Ro, Seodaemun Gu, Seoul, 03722, Republic of Korea
| | - Hee-Jin Yu
- School of Mechanical Engineering, Yonsei University, 50 Yonsei Ro, Seodaemun Gu, Seoul, 03722, Republic of Korea
| | - Min-Ji Kim
- Department of Orthodontics, School of Medicine, Ewha Womans University, Anyangcheon-ro 1071, Yangcheon-gu, Seoul, 07985, Republic of Korea
| | - Jin-Woo Kim
- Department of Oral and Maxillofacial Surgery, School of Medicine, Ewha Womans University, Anyangcheon-ro 1071, Yangcheon-gu, Seoul, 07985, Republic of Korea.
| | - Jongeun Choi
- School of Mechanical Engineering, Yonsei University, 50 Yonsei Ro, Seodaemun Gu, Seoul, 03722, Republic of Korea.
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Lachinov D, Getmanskaya A, Turlapov V. Cephalometric Landmark Regression with Convolutional Neural Networks on 3D Computed Tomography Data. PATTERN RECOGNITION AND IMAGE ANALYSIS 2020. [DOI: 10.1134/s1054661820030165] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Automatic Cephalometric Landmark Detection on X-ray Images Using a Deep-Learning Method. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10072547] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Accurate automatic quantitative cephalometry are essential for orthodontics. However, manual labeling of cephalometric landmarks is tedious and subjective, which also must be performed by professional doctors. In recent years, deep learning has gained attention for its success in computer vision field. It has achieved large progress in resolving problems like image classification or image segmentation. In this paper, we propose a two-step method which can automatically detect cephalometric landmarks on skeletal X-ray images. First, we roughly extract a region of interest (ROI) patch for each landmark by registering the testing image to training images, which have annotated landmarks. Then, we utilize pre-trained networks with a backbone of ResNet50, which is a state-of-the-art convolutional neural network, to detect each landmark in each ROI patch. The network directly outputs the coordinates of the landmarks. We evaluate our method on two datasets: ISBI 2015 Grand Challenge in Dental X-ray Image Analysis and our own dataset provided by Shandong University. The experiments demonstrate that the proposed method can achieve satisfying results on both SDR (Successful Detection Rate) and SCR (Successful Classification Rate). However, the computational time issue remains to be improved in the future.
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Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10062124] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The aim of this study was to evaluate the deep convolutional neural networks (DCNNs) based on analysis of cephalometric radiographs for the differential diagnosis of the indications of orthognathic surgery. Among the DCNNs, Modified-Alexnet, MobileNet, and Resnet50 were used, and the accuracy of the models was evaluated by performing 4-fold cross validation. Additionally, gradient-weighted class activation mapping (Grad-CAM) was used to perform visualized interpretation to determine which region affected the DCNNs’ class classification. The prediction accuracy of the models was 96.4% for Modified-Alexnet, 95.4% for MobileNet, and 95.6% for Resnet50. According to the Grad-CAM analysis, the most influential regions for the DCNNs’ class classification were the maxillary and mandibular teeth, mandible, and mandibular symphysis. This study suggests that DCNNs-based analysis of cephalometric radiograph images can be successfully applied for differential diagnosis of the indications of orthognathic surgery.
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Jiang Y, Song G, Yu X, Dou Y, Li Q, Liu S, Han B, Xu T. The application and accuracy of feature matching on automated cephalometric superimposition. BMC Med Imaging 2020; 20:31. [PMID: 32192440 PMCID: PMC7083061 DOI: 10.1186/s12880-020-00432-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 03/12/2020] [Indexed: 11/24/2022] Open
Abstract
Background The aim of this study was to establish a computer-aided automated method for cephalometric superimposition and to evaluate the accuracy of this method based on free-hand tracing. Methods Twenty-eight pairs of pre-treatment (T1) and post-treatment (T2) cephalograms were selected. Structural superimpositions of the anterior cranial base, maxilla and mandible were independently completed by three operators performing traditional hand tracing methods and by computerized automation using the feature matching algorithm. To quantitatively evaluate the differences between the two methods, the hand superimposed patterns were digitized. After automated and hand superimposition of T2 cephalograms to T1 cephalometric templates, landmark distances between paired automated and hand T2 cephalometric landmarks were measured. Differences in hand superimposition among the operators were also calculated. Results The T2 landmark differences in hand tracing between the operators ranged from 0.61 mm to 1.65 mm for the three types of superimposition. There were no significant differences in accuracy between hand and automated superimposition (p > 0.05). Conclusions Computer-aided cephalometric superimposition provides comparably accurate results to those of traditional hand tracing and will provide a powerful tool for academic research.
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Affiliation(s)
- Yiran Jiang
- Department of Orthodontics, Peking University School and Hospital of Stomatology, 22 Zhongguancun South Street, Haidian District, Beijing, 100081, China
| | - Guangying Song
- Department of Orthodontics, Peking University School and Hospital of Stomatology, 22 Zhongguancun South Street, Haidian District, Beijing, 100081, China
| | - Xiaonan Yu
- Department of Orthodontics, Peking University School and Hospital of Stomatology, 22 Zhongguancun South Street, Haidian District, Beijing, 100081, China
| | - Yuanbo Dou
- State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), Beihang University, Beijing, China.,Hangzhou Innovation Research Institute, Beihang University, Beijing, China
| | - Qingfeng Li
- State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC), Beihang University, Beijing, China.,Hangzhou Innovation Research Institute, Beihang University, Beijing, China
| | - Siqi Liu
- Department of Orthodontics, Peking University School and Hospital of Stomatology, 22 Zhongguancun South Street, Haidian District, Beijing, 100081, China.,First Clinical Division, Department of Orthodontics, Peking University School and Hospital of Stomatology, Beijing, China
| | - Bing Han
- Department of Orthodontics, Peking University School and Hospital of Stomatology, 22 Zhongguancun South Street, Haidian District, Beijing, 100081, China.
| | - Tianmin Xu
- Department of Orthodontics, Peking University School and Hospital of Stomatology, 22 Zhongguancun South Street, Haidian District, Beijing, 100081, China.
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Automatic Analysis of Lateral Cephalograms Based on Multiresolution Decision Tree Regression Voting. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:1797502. [PMID: 30581546 PMCID: PMC6276415 DOI: 10.1155/2018/1797502] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Accepted: 10/22/2018] [Indexed: 11/20/2022]
Abstract
Cephalometric analysis is a standard tool for assessment and prediction of craniofacial growth, orthodontic diagnosis, and oral-maxillofacial treatment planning. The aim of this study is to develop a fully automatic system of cephalometric analysis, including cephalometric landmark detection and cephalometric measurement in lateral cephalograms for malformation classification and assessment of dental growth and soft tissue profile. First, a novel method of multiscale decision tree regression voting using SIFT-based patch features is proposed for automatic landmark detection in lateral cephalometric radiographs. Then, some clinical measurements are calculated by using the detected landmark positions. Finally, two databases are tested in this study: one is the benchmark database of 300 lateral cephalograms from 2015 ISBI Challenge, and the other is our own database of 165 lateral cephalograms. Experimental results show that the performance of our proposed method is satisfactory for landmark detection and measurement analysis in lateral cephalograms.
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Identification of 'Point A' as the prevalent source of error in cephalometric analysis of lateral radiographs. Int J Oral Maxillofac Surg 2018; 47:1322-1329. [PMID: 29650356 DOI: 10.1016/j.ijom.2018.03.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Revised: 02/05/2018] [Accepted: 03/20/2018] [Indexed: 11/22/2022]
Abstract
Deviations in measuring dentofacial components in a lateral X-ray represent a major hurdle in the subsequent treatment of dysgnathic patients. In a retrospective study, we investigated the most prevalent source of error in the following commonly used cephalometric measurements: the angles Sella-Nasion-Point A (SNA), Sella-Nasion-Point B (SNB) and Point A-Nasion-Point B (ANB); the Wits appraisal; the anteroposterior dysplasia indicator (APDI); and the overbite depth indicator (ODI). Preoperative lateral radiographic images of patients with dentofacial deformities were collected and the landmarks digitally traced by three independent raters. Cephalometric analysis was automatically performed based on 1116 tracings. Error analysis identified the x-coordinate of Point A as the prevalent source of error in all investigated measurements, except SNB, in which it is not incorporated. In SNB, the y-coordinate of Nasion predominated error variance. SNB showed lowest inter-rater variation. In addition, our observations confirmed previous studies showing that landmark identification variance follows characteristic error envelopes in the highest number of tracings analysed up to now. Variance orthogonal to defining planes was of relevance, while variance parallel to planes was not. Taking these findings into account, orthognathic surgeons as well as orthodontists would be able to perform cephalometry more accurately and accomplish better therapeutic results.
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Arık SÖ, Ibragimov B, Xing L. Fully automated quantitative cephalometry using convolutional neural networks. J Med Imaging (Bellingham) 2017; 4:014501. [PMID: 28097213 PMCID: PMC5220585 DOI: 10.1117/1.jmi.4.1.014501] [Citation(s) in RCA: 110] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Accepted: 12/12/2016] [Indexed: 11/14/2022] Open
Abstract
Quantitative cephalometry plays an essential role in clinical diagnosis, treatment, and surgery. Development of fully automated techniques for these procedures is important to enable consistently accurate computerized analyses. We study the application of deep convolutional neural networks (CNNs) for fully automated quantitative cephalometry for the first time. The proposed framework utilizes CNNs for detection of landmarks that describe the anatomy of the depicted patient and yield quantitative estimation of pathologies in the jaws and skull base regions. We use a publicly available cephalometric x-ray image dataset to train CNNs for recognition of landmark appearance patterns. CNNs are trained to output probabilistic estimations of different landmark locations, which are combined using a shape-based model. We evaluate the overall framework on the test set and compare with other proposed techniques. We use the estimated landmark locations to assess anatomically relevant measurements and classify them into different anatomical types. Overall, our results demonstrate high anatomical landmark detection accuracy ([Formula: see text] to 2% higher success detection rate for a 2-mm range compared with the top benchmarks in the literature) and high anatomical type classification accuracy ([Formula: see text] average classification accuracy for test set). We demonstrate that CNNs, which merely input raw image patches, are promising for accurate quantitative cephalometry.
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Affiliation(s)
- Sercan Ö. Arık
- Baidu USA, 1195 Bordeaux Drive, Sunnyvale, California 94089, United States
| | - Bulat Ibragimov
- Stanford University, Department of Radiation Oncology, School of Medicine, 875 Blake Wilbur Drive, Stanford, California 94305, United States
| | - Lei Xing
- Stanford University, Department of Radiation Oncology, School of Medicine, 875 Blake Wilbur Drive, Stanford, California 94305, United States
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Mosleh MAA, Baba MS, Malek S, Almaktari RA. Ceph-X: development and evaluation of 2D cephalometric system. BMC Bioinformatics 2016; 17:499. [PMID: 28155649 PMCID: PMC5259857 DOI: 10.1186/s12859-016-1370-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background Cephalometric analysis and measurements of skull parameters using X-Ray images plays an important role in predicating and monitoring orthodontic treatment. Manual analysis and measurements of cephalometric is considered tedious, time consuming, and subjected to human errors. Several cephalometric systems have been developed to automate the cephalometric procedure; however, no clear insights have been reported about reliability, performance, and usability of those systems. This study utilizes some techniques to evaluate reliability, performance, and usability metric using SUS methods of the developed cephalometric system which has not been reported in previous studies. Methods In this study a novel system named Ceph-X is developed to computerize the manual tasks of orthodontics during cephalometric measurements. Ceph-X is developed by using image processing techniques with three main models: enhancements X-ray image model, locating landmark model, and computation model. Ceph-X was then evaluated by using X-ray images of 30 subjects (male and female) obtained from University of Malaya hospital. Three orthodontics specialists were involved in the evaluation of accuracy to avoid intra examiner error, and performance for Ceph-X, and 20 orthodontics specialists were involved in the evaluation of the usability, and user satisfaction for Ceph-X by using the SUS approach. Results Statistical analysis for the comparison between the manual and automatic cephalometric approaches showed that Ceph-X achieved a great accuracy approximately 96.6%, with an acceptable errors variation approximately less than 0.5 mm, and 1°. Results showed that Ceph-X increased the specialist performance, and minimized the processing time to obtain cephalometric measurements of human skull. Furthermore, SUS analysis approach showed that Ceph-X has an excellent usability user’s feedback. Conclusions The Ceph-X has proved its reliability, performance, and usability to be used by orthodontists for the analysis, diagnosis, and treatment of cephalometric.
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Affiliation(s)
- Mogeeb Ahmed Ahmed Mosleh
- Software Engineering Department, Faculty of Engineering & Information Technology, Taiz University, 6169, Taiz, Yemen.
| | - Mohd Sapiyan Baba
- Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Sorayya Malek
- Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Rasheed A Almaktari
- Faculty of Dentistry, Orthodontic Department, University of Malaya, 50603, Kuala Lumpur, Malaysia
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Tam WK, Lee HJ. Improving point correspondence in cephalograms by using a two-stage rectified point transform. Comput Biol Med 2015; 65:114-23. [DOI: 10.1016/j.compbiomed.2015.07.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2015] [Revised: 07/15/2015] [Accepted: 07/27/2015] [Indexed: 11/15/2022]
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Wang CW, Huang CT, Hsieh MC, Li CH, Chang SW, Li WC, Vandaele R, Marée R, Jodogne S, Geurts P, Chen C, Zheng G, Chu C, Mirzaalian H, Hamarneh G, Vrtovec T, Ibragimov B. Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:1890-900. [PMID: 25794388 DOI: 10.1109/tmi.2015.2412951] [Citation(s) in RCA: 85] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Cephalometric analysis is an essential clinical and research tool in orthodontics for the orthodontic analysis and treatment planning. This paper presents the evaluation of the methods submitted to the Automatic Cephalometric X-Ray Landmark Detection Challenge, held at the IEEE International Symposium on Biomedical Imaging 2014 with an on-site competition. The challenge was set to explore and compare automatic landmark detection methods in application to cephalometric X-ray images. Methods were evaluated on a common database including cephalograms of 300 patients aged six to 60 years, collected from the Dental Department, Tri-Service General Hospital, Taiwan, and manually marked anatomical landmarks as the ground truth data, generated by two experienced medical doctors. Quantitative evaluation was performed to compare the results of a representative selection of current methods submitted to the challenge. Experimental results show that three methods are able to achieve detection rates greater than 80% using the 4 mm precision range, but only one method achieves a detection rate greater than 70% using the 2 mm precision range, which is the acceptable precision range in clinical practice. The study provides insights into the performance of different landmark detection approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.
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A knowledge-based algorithm for automatic detection of cephalometric landmarks on CBCT images. Int J Comput Assist Radiol Surg 2015; 10:1737-52. [DOI: 10.1007/s11548-015-1173-6] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Accepted: 03/10/2015] [Indexed: 11/25/2022]
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Shahidi S, Bahrampour E, Soltanimehr E, Zamani A, Oshagh M, Moattari M, Mehdizadeh A. The accuracy of a designed software for automated localization of craniofacial landmarks on CBCT images. BMC Med Imaging 2014; 14:32. [PMID: 25223399 PMCID: PMC4171715 DOI: 10.1186/1471-2342-14-32] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2014] [Accepted: 09/08/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Two-dimensional projection radiographs have been traditionally considered the modality of choice for cephalometric analysis. To overcome the shortcomings of two-dimensional images, three-dimensional computed tomography (CT) has been used to evaluate craniofacial structures. However, manual landmark detection depends on medical expertise, and the process is time-consuming. The present study was designed to produce software capable of automated localization of craniofacial landmarks on cone beam (CB) CT images based on image registration and to evaluate its accuracy. METHODS The software was designed using MATLAB programming language. The technique was a combination of feature-based (principal axes registration) and voxel similarity-based methods for image registration. A total of 8 CBCT images were selected as our reference images for creating a head atlas. Then, 20 CBCT images were randomly selected as the test images for evaluating the method. Three experts twice located 14 landmarks in all 28 CBCT images during two examinations set 6 weeks apart. The differences in the distances of coordinates of each landmark on each image between manual and automated detection methods were calculated and reported as mean errors. RESULTS The combined intraclass correlation coefficient for intraobserver reliability was 0.89 and for interobserver reliability 0.87 (95% confidence interval, 0.82 to 0.93). The mean errors of all 14 landmarks were <4 mm. Additionally, 63.57% of landmarks had a mean error of <3 mm compared with manual detection (gold standard method). CONCLUSION The accuracy of our approach for automated localization of craniofacial landmarks, which was based on combining feature-based and voxel similarity-based methods for image registration, was acceptable. Nevertheless we recommend repetition of this study using other techniques, such as intensity-based methods.
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Affiliation(s)
| | - Ehsan Bahrampour
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.
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Shahidi S, Shahidi S, Oshagh M, Gozin F, Salehi P, Danaei SM. Accuracy of computerized automatic identification of cephalometric landmarks by a designed software. Dentomaxillofac Radiol 2013; 42:20110187. [PMID: 23236215 DOI: 10.1259/dmfr.20110187] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES The purpose of this study was to design software for localization of cephalometric landmarks and to evaluate its accuracy in finding landmarks. METHODS 40 digital cephalometric radiographs were randomly selected. 16 landmarks which were important in most cephalometric analyses were chosen to be identified. Three expert orthodontists manually identified landmarks twice. The mean of two measurements of each landmark was defined as the baseline landmark. The computer was then able to compare the automatic system's estimate of a landmark with the baseline landmark. The software was designed using Delphi and Matlab programming languages. The techniques were template matching, edge enhancement and some accessory techniques. RESULTS The total mean error between manually identified and automatically identified landmarks was 2.59 mm. 12.5% of landmarks had mean errors less than 1 mm. 43.75% of landmarks had mean errors less than 2 mm. The mean errors of all landmarks except the anterior nasal spine were less than 4 mm. CONCLUSIONS This software had significant accuracy for localization of cephalometric landmarks and could be used in future applications. It seems that the accuracy obtained with the software which was developed in this study is better than previous automated systems that have used model-based and knowledge-based approaches.
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Affiliation(s)
- Sh Shahidi
- Shiraz Biomaterial [corrected] Research Center, Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran. [corrected]
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Chen L, Lan Z, Xu X, Lin J, Hu H. Accuracy and repeatability of computer aided cervical vertebra landmarking in cephalogram. JOURNAL OF HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY. MEDICAL SCIENCES = HUA ZHONG KE JI DA XUE XUE BAO. YI XUE YING DE WEN BAN = HUAZHONG KEJI DAXUE XUEBAO. YIXUE YINGDEWEN BAN 2012; 32:119-123. [PMID: 22282257 DOI: 10.1007/s11596-012-0021-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2011] [Indexed: 05/31/2023]
Abstract
The accuracy and repeatability of computer aided cervical vertebra landmarking (CACVL) were investigated in cephalogram. 120 adolescents (60 boys, 60 girls) aged from 9.1 to 17.2 years old were randomly selected. Twenty-seven landmarks from the second to fifth cervical vertebrae on the lateral cephalogram were identified. In this study, the system of CACVL was developed and used to identify and calculate the landmarks by fast marching method and parabolic curve fitting. The accuracy and repeatability in CACVL group were compared with those in two manual landmarking groups [orthodontic experts (OE) group and orthodontic novices (ON) group]. The results showed that, as for the accuracy, there was no significant difference between CACVL group and OE group no matter in x-axis or y-axis (P>0.05), but there was significant difference between CACVL group and ON group, as well as OE group and ON group in both axes (P<0.05). As for the repeatability, CACVL group was more reliable than OE group and ON group in both axes. It is concluded that CACVL has the same or higher accuracy, better repeatability and less workload than manual landmarking methods. It's reliable for cervical parameters identification on the lateral cephalogram and cervical vertebral maturation prediction in orthodontic practice and research.
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Affiliation(s)
- Lili Chen
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Zhicong Lan
- Department of Stomatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Xiangyang Xu
- Key Laboratory of Education Ministry for Image Processing and Intelligent Control, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430071, China
| | - Jiuxiang Lin
- Department of Orthodontics, Peking University School and Hospital of Stomatology and Director, Research Center of Craniofacial Growth and Development, Beijing, 100081, China
| | - Huaifei Hu
- Key Laboratory of Education Ministry for Image Processing and Intelligent Control, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430071, China
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Mondal T, Jain A, Sardana HK. Automatic craniofacial structure detection on cephalometric images. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2011; 20:2606-2614. [PMID: 21435982 DOI: 10.1109/tip.2011.2131662] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Anatomical structure tracing on cephalograms is a significant way to obtain cephalometric analysis. Cephalometric analysis is divided in two categories, manual and automatic approaches. The manual approach is limited in accuracy and repeatability due to differences in inter- and intra-personal marking. In this paper, we have attempted to develop and test a novel method for automatic localization of craniofacial structures based on the detected edges in the region of interest. Before edge detection of the particular region, the region was filtered by adaptive non local filter for noise removal by keeping the edge information undisturbed. According to the gray-scale feature at the different regions of the cephalograms, modified Canny edge detection algorithm for obtaining tissue contour was proposed. With the application of morphological opening and edge linking approaches, an improved bidirectional contour tracing methodology was proposed by an interactive selection of the starting edge pixels, the tracking process searches repetitively for an edge pixel at the neighborhood of previously searched edge pixel to segment images, and then craniofacial structures are obtained. The effectiveness of the algorithm is demonstrated by the preliminary experimental results obtained with the proposed method.
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Affiliation(s)
- Tanmoy Mondal
- Computational Instrumentation Unit, Central Scientific Instruments Organisation (CSIO), Chandigarh, 160030, India.
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Tanikawa C, Yagi M, Takada K. Automated cephalometry: system performance reliability using landmark-dependent criteria. Angle Orthod 2010; 79:1037-46. [PMID: 19852592 DOI: 10.2319/092908-508r.1] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE The purpose of the present study was to evaluate reliability of a system that performs automatic recognition of anatomic landmarks and adjacent structures on lateral cephalograms using landmark-dependent criteria unique to each landmark. MATERIALS AND METHODS To evaluate the reliability of the system, the system was used to examine 65 lateral cephalograms. The area of each system-identified anatomic structure surrounding the landmark and the position of each system-identified landmark were compared with norms using confidence ellipses with alpha = .01, which were derived from the scattergrams of 100 estimates obtained according to the method reported by Baumrind and Frantz. When the system-identified area overlapped with the norm area, anatomic structure recognition was considered successful. In addition, when the system-identified point was located within the norm area, landmark identification was considered successful. Based on these judgment criteria, success rates were calculated for all landmarks. RESULTS The system successfully identified all specified anatomic structures in all the images and determined the positions of the landmarks with a mean success rate of 88% (range, 77%- 100%). CONCLUSION With the incorporation of the rational assessment criteria provided by confidence ellipses, the proposed system was confirmed to be reliable.
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Affiliation(s)
- Chihiro Tanikawa
- Department of Orthodontics and Dentofacial Orthopedics, Graduate School of Dentistry, Osaka, Japan
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Wolf L, Yedidya T, Ganor R, Chertok M, Nachmani A, Finkelstein Y. Automatic cephalometric evaluation of patients suffering from sleep-disordered breathing. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2010; 13:642-649. [PMID: 20879455 DOI: 10.1007/978-3-642-15711-0_80] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
We address the problem of automatically analyzing lateral cephalometric images as a diagnostic tool for patients suffering from Sleep Disordered Breathing (SDB). First, multiple landmarks and anatomical structures that were previously associated with SDB are localized. Then statistical regression is applied in order to estimate the Respiratory Disturbance Index (RDI), which is the standard measure for the severity of obstructive sleep apnea. The landmark localization employs a new registration method that is based on Local Affine Frames (LAF). Multiple LAFs are sampled per image based on random selection of triplets of keypoints, and are used to register the input image to the training images. The landmarks are then projected from the training images to the query image. Following a refinement step, the tongue, velum and pharyngeal wall are localized. We collected a dataset of 70 images and compare the accuracy of the anatomical landmarks with recent publications, showing preferable performance in localizing most of the anatomical points. Furthermore, we are able to show that the location of the anatomical landmarks and structures predicts the severity of the disorder, obtaining an error of less than 7.5 RDI units for 44% of the patients.
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Affiliation(s)
- Lior Wolf
- School of Computer Science, Tel-Aviv University
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An evaluation of cellular neural networks for the automatic identification of cephalometric landmarks on digital images. J Biomed Biotechnol 2009; 2009:717102. [PMID: 19753320 PMCID: PMC2742650 DOI: 10.1155/2009/717102] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2009] [Revised: 05/16/2009] [Accepted: 06/18/2009] [Indexed: 11/17/2022] Open
Abstract
Several efforts have been made to completely automate cephalometric analysis by automatic landmark search. However, accuracy obtained was worse than manual identification in every study. The analogue-to-digital conversion of X-ray has been claimed to be the main problem. Therefore the aim of this investigation was to evaluate the accuracy of the Cellular Neural Networks approach for automatic location of cephalometric landmarks on softcopy of direct digital cephalometric X-rays. Forty-one, direct-digital lateral cephalometric radiographs were obtained by a Siemens Orthophos DS Ceph and were used in this study and 10 landmarks (N, A Point, Ba, Po, Pt, B Point, Pg, PM, UIE, LIE) were the object of automatic landmark identification. The mean errors and standard deviations from the best estimate of cephalometric points were calculated for each landmark. Differences in the mean errors of automatic and manual landmarking were compared with a 1-way analysis of variance. The analyses indicated that the differences were very small, and they were found at most within 0.59 mm. Furthermore, only few of these differences were statistically significant, but differences were so small to be in most instances clinically meaningless. Therefore the use of X-ray files with respect to scanned X-ray improved landmark accuracy of automatic detection. Investigations on softcopy of digital cephalometric X-rays, to search more landmarks in order to enable a complete automatic cephalometric analysis, are strongly encouraged.
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Leonardi R, Giordano D, Maiorana F, Spampinato C. Automatic cephalometric analysis. Angle Orthod 2008; 78:145-51. [PMID: 18193970 DOI: 10.2319/120506-491.1] [Citation(s) in RCA: 59] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2006] [Accepted: 02/01/2007] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE To describe the techniques used for automatic landmarking of cephalograms, highlighting the strengths and weaknesses of each one and reviewing the percentage of success in locating each cephalometric point. MATERIALS AND METHODS The literature survey was performed by searching the Medline, the Institute of Electrical and Electronics Engineers, and the ISI Web of Science Citation Index databases. The survey covered the period from January 1966 to August 2006. Abstracts that appeared to fulfill the initial selection criteria were selected by consensus. The original articles were then retrieved. Their references were also hand-searched for possible missing articles. The search strategy resulted in 118 articles of which eight met the inclusion criteria. Many articles were rejected for different reasons; among these, the most frequent was that results of accuracy for automatic landmark recognition were presented as a percentage of success. RESULTS A marked difference in results was found between the included studies consisting of heterogeneity in the performance of techniques to detect the same landmark. All in all, hybrid approaches detected cephalometric points with a higher accuracy in contrast to the results for the same points obtained by the model-based, image filtering plus knowledge-based landmark search and "soft-computing" approaches. CONCLUSIONS The systems described in the literature are not accurate enough to allow their use for clinical purposes. Errors in landmark detection were greater than those expected with manual tracing and, therefore, the scientific evidence supporting the use of automatic landmarking is low.
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Affiliation(s)
- Rosalia Leonardi
- Department of Orthodontics, University of Catania, University of Catania, Catania, Italy.
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