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Silva TP, Pinheiro MCR, Freitas DQ, Gaêta-Araujo H, Oliveira-Santos C. Assessment of accuracy and reproducibility of cephalometric identification performed by 2 artificial intelligence-driven tracing applications and human examiners. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 137:431-440. [PMID: 38365543 DOI: 10.1016/j.oooo.2024.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 01/04/2024] [Accepted: 01/13/2024] [Indexed: 02/18/2024]
Abstract
OBJECTIVE To assess the accuracy and reproducibility of cephalometric landmark identification performed by 2 artificial intelligence (AI)-driven applications (CefBot and WebCeph) and human examiners. STUDY DESIGN Lateral cephalometric radiographs of 10 skulls containing 0.5 mm lead spheres directly placed at 10 cephalometric landmarks were obtained as the reference standard. Ten radiographs without spheres were obtained from the same skulls for identification of cephalometric points performed by the AI applications and 10 examiners. The x- and y-coordinate values of the cephalometric points identified by the AI applications and examiners were compared with those from the reference standard images using one-way analysis of variance and the Dunnet post-hoc test. The intraclass correlation coefficient (ICC) was used to evaluate reproducibility. Mean radial error (MRE) in identification was calculated with respect to the reference standard. Statistical significance was established at P < .05. RESULTS Landmark identification by CefBot and the examiners did not exhibit significant differences from the reference standard on either axis (P > .05). WebCeph produced a significant difference (P < .05) in 4 and 6 points on the x- and y-axes, respectively. Reproducibility was excellent for CefBot and the examiners (ICC ≥ 0.9943) and good for WebCeph (ICC ≥ 0.7868). MREs of CefBot and the examiners were similar. CONCLUSION With results similar to those of human examiners, CefBot demonstrated excellent reliability and can aid in cephalometric applications. WebCeph produced significant errors.
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Affiliation(s)
- Thaísa Pinheiro Silva
- Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, Sao Paulo, Brazil.
| | - Maria Clara Rodrigues Pinheiro
- Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, Sao Paulo, Brazil
| | - Deborah Queiroz Freitas
- Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, Sao Paulo, Brazil
| | - Hugo Gaêta-Araujo
- Department of Stomatology, Public Oral Health, Forensic Dentistry, Division of Oral Radiology, School of Dentistry of Ribeirao Preto, University of Sao Paulo (USP), Ribeirao Preto, Sao Paulo, Brazil
| | - Christiano Oliveira-Santos
- Department of Diagnosis and Oral Health, University of Louisville School of Dentistry, Louisville, KY, USA
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Park JA, Kim D, Yang S, Kang JH, Kim JE, Huh KH, Lee SS, Yi WJ, Heo MS. Automatic detection of posterior superior alveolar artery in dental cone-beam CT images using a deeply supervised multi-scale 3D network. Dentomaxillofac Radiol 2024; 53:22-31. [PMID: 38214942 PMCID: PMC11003607 DOI: 10.1093/dmfr/twad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 09/15/2023] [Accepted: 10/18/2023] [Indexed: 01/13/2024] Open
Abstract
OBJECTIVES This study aimed to develop a robust and accurate deep learning network for detecting the posterior superior alveolar artery (PSAA) in dental cone-beam CT (CBCT) images, focusing on the precise localization of the centre pixel as a critical centreline pixel. METHODS PSAA locations were manually labelled on dental CBCT data from 150 subjects. The left maxillary sinus images were horizontally flipped. In total, 300 datasets were created. Six different deep learning networks were trained, including 3D U-Net, deeply supervised 3D U-Net (3D U-Net DS), multi-scale deeply supervised 3D U-Net (3D U-Net MSDS), 3D Attention U-Net, 3D V-Net, and 3D Dense U-Net. The performance evaluation involved predicting the centre pixel of the PSAA. This was assessed using mean absolute error (MAE), mean radial error (MRE), and successful detection rate (SDR). RESULTS The 3D U-Net MSDS achieved the best prediction performance among the tested networks, with an MAE measurement of 0.696 ± 1.552 mm and MRE of 1.101 ± 2.270 mm. In comparison, the 3D U-Net showed the lowest performance. The 3D U-Net MSDS demonstrated a SDR of 95% within a 2 mm MAE. This was a significantly higher result than other networks that achieved a detection rate of over 80%. CONCLUSIONS This study presents a robust deep learning network for accurate PSAA detection in dental CBCT images, emphasizing precise centre pixel localization. The method achieves high accuracy in locating small vessels, such as the PSAA, and has the potential to enhance detection accuracy and efficiency, thus impacting oral and maxillofacial surgery planning and decision-making.
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Affiliation(s)
- Jae-An Park
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - DaEl Kim
- Interdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Su Yang
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea
| | - Ju-Hee Kang
- Department of Oral and Maxillofacial Radiology, Seoul National University Dental Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Jo-Eun Kim
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Kyung-Hoe Huh
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Sam-Sun Lee
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Won-Jin Yi
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
| | - Min-Suk Heo
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea
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Dipalma G, Inchingolo AD, Inchingolo AM, Piras F, Carpentiere V, Garofoli G, Azzollini D, Campanelli M, Paduanelli G, Palermo A, Inchingolo F. Artificial Intelligence and Its Clinical Applications in Orthodontics: A Systematic Review. Diagnostics (Basel) 2023; 13:3677. [PMID: 38132261 PMCID: PMC10743240 DOI: 10.3390/diagnostics13243677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/05/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
This review aims to analyze different strategies that make use of artificial intelligence to enhance diagnosis, treatment planning, and monitoring in orthodontics. Orthodontics has seen significant technological advancements with the introduction of digital equipment, including cone beam computed tomography, intraoral scanners, and software coupled to these devices. The use of deep learning in software has sped up image processing processes. Deep learning is an artificial intelligence technology that trains computers to analyze data like the human brain does. Deep learning models are capable of recognizing complex patterns in photos, text, audio, and other data to generate accurate information and predictions. MATERIALS AND METHODS Pubmed, Scopus, and Web of Science were used to discover publications from 1 January 2013 to 18 October 2023 that matched our topic. A comparison of various artificial intelligence applications in orthodontics was generated. RESULTS A final number of 33 studies were included in the review for qualitative analysis. CONCLUSIONS These studies demonstrate the effectiveness of AI in enhancing orthodontic diagnosis, treatment planning, and assessment. A lot of articles emphasize the integration of artificial intelligence into orthodontics and its potential to revolutionize treatment monitoring, evaluation, and patient outcomes.
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Affiliation(s)
- Gianna Dipalma
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Alessio Danilo Inchingolo
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Angelo Michele Inchingolo
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Fabio Piras
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Vincenzo Carpentiere
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Grazia Garofoli
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Daniela Azzollini
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Merigrazia Campanelli
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Gregorio Paduanelli
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
| | - Andrea Palermo
- Implant Dentistry College of Medicine and Dentistry Birmingham, University of Birmingham, Birmingham B46BN, UK;
| | - Francesco Inchingolo
- Department of Interdisciplinary Medicine, University of Bari “Aldo Moro”, 70124 Bari, Italy; (A.M.I.); (F.P.); (V.C.); (G.G.); (D.A.); (M.C.); (G.P.); (F.I.)
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Lee HS, Yang S, Han JY, Kang JH, Kim JE, Huh KH, Yi WJ, Heo MS, Lee SS. Automatic detection and classification of nasopalatine duct cyst and periapical cyst on panoramic radiographs using deep convolutional neural networks. Oral Surg Oral Med Oral Pathol Oral Radiol 2023:S2212-4403(23)00691-0. [PMID: 38158267 DOI: 10.1016/j.oooo.2023.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/01/2023] [Accepted: 09/15/2023] [Indexed: 01/03/2024]
Abstract
OBJECTIVE The aim of this study is to evaluate a deep convolutional neural network (DCNN) method for the detection and classification of nasopalatine duct cysts (NPDC) and periapical cysts (PAC) on panoramic radiographs. STUDY DESIGN A total of 1,209 panoramic radiographs with 606 NPDC and 603 PAC were labeled with a bounding box and divided into training, validation, and test sets with an 8:1:1 ratio. The networks used were EfficientDet-D3, Faster R-CNN, YOLO v5, RetinaNet, and SSD. Mean average precision (mAP) was used to assess performance. Sixty images with no lesion in the anterior maxilla were added to the previous test set and were tested on 2 dentists with no training in radiology (GP) and on EfficientDet-D3. The performances were comparatively examined. RESULTS The mAP for each DCNN was EfficientDet-D3 93.8%, Faster R-CNN 90.8%, YOLO v5 89.5%, RetinaNet 79.4%, and SSD 60.9%. The classification performance of EfficientDet-D3 was higher than that of the GPs' with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 94.4%, 94.4%, 97.2%, 94.6%, and 97.2%, respectively. CONCLUSIONS The proposed method achieved high performance for the detection and classification of NPDC and PAC compared with the GPs and presented promising prospects for clinical application.
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Affiliation(s)
- Han-Sol Lee
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Su Yang
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - Ji-Yong Han
- Interdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, South Korea
| | - Ju-Hee Kang
- Department of Oral and Maxillofacial Radiology, Seoul National University Dental Hospital, Seoul, South Korea
| | - Jo-Eun Kim
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Kyung-Hoe Huh
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Won-Jin Yi
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea; Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea; Interdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, South Korea.
| | - Min-Suk Heo
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea.
| | - Sam-Sun Lee
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Kunz F, Stellzig-Eisenhauer A, Widmaier LM, Zeman F, Boldt J. Assessment of the quality of different commercial providers using artificial intelligence for automated cephalometric analysis compared to human orthodontic experts. J Orofac Orthop 2023:10.1007/s00056-023-00491-1. [PMID: 37642657 DOI: 10.1007/s00056-023-00491-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 06/28/2023] [Indexed: 08/31/2023]
Abstract
PURPOSE The aim of this investigation was to evaluate the accuracy of various skeletal and dental cephalometric parameters as produced by different commercial providers that make use of artificial intelligence (AI)-assisted automated cephalometric analysis and to compare their quality to a gold standard established by orthodontic experts. METHODS Twelve experienced orthodontic examiners pinpointed 15 radiographic landmarks on a total of 50 cephalometric X‑rays. The landmarks were used to generate 9 parameters for orthodontic treatment planning. The "humans' gold standard" was defined by calculating the median value of all 12 human assessments for each parameter, which in turn served as reference values for comparisons with results given by four different commercial providers of automated cephalometric analyses (DentaliQ.ortho [CellmatiQ GmbH, Hamburg, Germany], WebCeph [AssembleCircle Corp, Seongnam-si, Korea], AudaxCeph [Audax d.o.o., Ljubljana, Slovenia], CephX [Orca Dental AI, Herzliya, Israel]). Repeated measures analysis of variances (ANOVAs) were calculated and Bland-Altman plots were generated for comparisons. RESULTS The results of the repeated measures ANOVAs indicated significant differences between the commercial providers' predictions and the humans' gold standard for all nine investigated parameters. However, the pairwise comparisons also demonstrate that there were major differences among the four commercial providers. While there were no significant mean differences between the values of DentaliQ.ortho and the humans' gold standard, the predictions of AudaxCeph showed significant deviations in seven out of nine parameters. Also, the Bland-Altman plots demonstrate that a reduced precision of AI predictions must be expected especially for values attributed to the inclination of the incisors. CONCLUSION Fully automated cephalometric analyses are promising in terms of timesaving and avoidance of individual human errors. At present, however, they should only be used under supervision of experienced clinicians.
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Affiliation(s)
- Felix Kunz
- Department of Orthodontics, University Hospital of Würzburg, Pleicherwall 2, 97070, Würzburg, Germany.
| | | | - Lisa Marie Widmaier
- Department of Orthodontics, University Hospital of Würzburg, Pleicherwall 2, 97070, Würzburg, Germany
| | - Florian Zeman
- Centre for Clinical Studies, University Hospital of Regensburg, Regensburg, Germany
| | - Julian Boldt
- Department of Prosthetic Dentistry, University Hospital of Würzburg, Würzburg, Germany
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Kiełczykowski M, Kamiński K, Perkowski K, Zadurska M, Czochrowska E. Application of Artificial Intelligence (AI) in a Cephalometric Analysis: A Narrative Review. Diagnostics (Basel) 2023; 13:2640. [PMID: 37627899 PMCID: PMC10453867 DOI: 10.3390/diagnostics13162640] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/04/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
In recent years, the application of artificial intelligence (AI) has become more and more widespread in medicine and dentistry. It may contribute to improved quality of health care as diagnostic methods are getting more accurate and diagnostic errors are rarer in daily medical practice. The aim of this paper was to present data from the literature on the effectiveness of AI in orthodontic diagnostics based on the analysis of lateral cephalometric radiographs. A review of the literature from 2009 to 2023 has been performed using PubMed, Medline, Scopus and Dentistry & Oral Sciences Source databases. The accuracy of determining cephalometric landmarks using widely available commercial AI-based software and advanced AI algorithms was presented and discussed. Most AI algorithms used for the automated positioning of landmarks on cephalometric radiographs had relatively high accuracy. At the same time, the effectiveness of using AI in cephalometry varies depending on the algorithm or the application type, which has to be accounted for during the interpretation of the results. In conclusion, artificial intelligence is a promising tool that facilitates the identification of cephalometric landmarks in everyday clinical practice, may support orthodontic treatment planning for less experienced clinicians and shorten radiological examination in orthodontics. In the future, AI algorithms used for the automated localisation of cephalometric landmarks may be more accurate than manual analysis.
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Affiliation(s)
| | | | | | | | - Ewa Czochrowska
- Department of Orthodontics, Medical University in Warsaw, 02-097 Warsaw, Poland; (M.K.); (K.K.); (K.P.); (M.Z.)
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Zhao C, Yuan Z, Luo S, Wang W, Ren Z, Yao X, Wu T. Automatic recognition of cephalometric landmarks via multi-scale sampling strategy. Heliyon 2023; 9:e17459. [PMID: 37416642 PMCID: PMC10320076 DOI: 10.1016/j.heliyon.2023.e17459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 06/08/2023] [Accepted: 06/19/2023] [Indexed: 07/08/2023] Open
Abstract
The identification of head landmarks in cephalometric analysis significantly contributes in the anatomical localization of maxillofacial tissues for orthodontic and orthognathic surgery. However, the existing methods face the limitations of low accuracy and cumbersome identification process. In this pursuit, the present study proposed an automatic target recognition algorithm called Multi-Scale YOLOV3 (MS-YOLOV3) for the detection of cephalometric landmarks. It was characterized by multi-scale sampling strategies for shallow and deep features at varied resolutions, and especially contained the module of spatial pyramid pooling (SPP) for highest resolution. The proposed method was quantitatively and qualitatively compared with the classical YOLOV3 algorithm on the two data sets of public lateral cephalograms, undisclosed anterior-posterior (AP) cephalograms, respectively, for evaluating the performance. The proposed MS-YOLOV3 algorithm showed better robustness with successful detection rates (SDR) of 80.84% within 2 mm, 93.75% within 3 mm, and 98.14% within 4 mm for lateral cephalograms, and 85.75% within 2 mm, 92.87% within 3 mm, and 96.66% within 4 mm for AP cephalograms, respectively. It was concluded that the proposed model could be robustly used to label the cephalometric landmarks on both lateral and AP cephalograms for the clinical application in orthodontic and orthognathic surgery.
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Affiliation(s)
- Congyi Zhao
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Zengbei Yuan
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Shichang Luo
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Wenjie Wang
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Zhe Ren
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- College of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Xufeng Yao
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
| | - Tao Wu
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
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Esfehani M, Ghasemi M, Katiraee A, Tofangchiha M, Alizadeh A, Taghavi-Damghani F, Testarelli L, Reda R. Forensic Gender Determination by Using Mandibular Morphometric Indices an Iranian Population: A Panoramic Radiographic Cross-Sectional Study. J Imaging 2023; 9. [PMID: 36826959 DOI: 10.3390/jimaging9020040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 01/27/2023] [Accepted: 02/06/2023] [Indexed: 02/15/2023] Open
Abstract
Gender determination is the first step in forensic identification, followed by age and height determination, which are both affected by gender. This study assessed the accuracy of gender estimation using mandibular morphometric indices on panoramic radiographs of an Iranian population. This retrospective study evaluated 290 panoramic radiographs (145 males and 145 females). The maximum and minimum ramus width, coronoid height, condylar height, antegonial angle, antegonial depth, gonial angle, and the superior border of mental foramen were bilaterally measured as well as bicondylar and bigonial breadths using Scanora Lite. Correlation of parameters with gender was analyzed by univariate, multiple, and best models. All indices except for gonial angle were significantly different between males and females and can be used for gender determination according to univariate model. Condylar height, coronoid height, and superior border of mental foramen and ramus were still significantly greater in males than in females after controlling for the effect of confounders (p < 0.05). Based on the best model, a formula including five indices of bicondylar breadth, condylar height, coronoid height, minimum ramus width, and superior border of mental foramen was used for gender determination. Values higher than 56% indicate male gender, while lower values indicate female gender, with 81.38% specificity for correct detection of females and 88.97% sensitivity for correct detection of males. Despite the satisfactory results, future research should focus on larger populations to verify the accuracy of the present findings.
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Jiang F, Guo Y, Yang C, Zhou Y, Lin Y, Cheng F, Quan S, Feng Q, Li J. Artificial intelligence system for automated landmark localization and analysis of cephalometry. Dentomaxillofac Radiol 2023; 52:20220081. [PMID: 36279185 PMCID: PMC9793451 DOI: 10.1259/dmfr.20220081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 10/15/2022] [Accepted: 10/16/2022] [Indexed: 01/11/2023] Open
Abstract
OBJECTIVES Cephalometric analysis is essential for diagnosis, treatment planning and outcome assessment of orthodontics and orthognathic surgery. Utilizing artificial intelligence (AI) to achieve automated landmark localization has proved feasible and convenient. However, current systems remain insufficient for clinical application, as patients exhibit various malocclusions in cephalograms produced by different manufacturers while limited cephalograms were applied to train AI in these systems. METHODS A robust and clinically applicable AI system was proposed for automatic cephalometric analysis. First, 9870 cephalograms taken by different radiography machines with various malocclusions of patients were collected from 20 medical institutions. Then 30 landmarks of all these cephalogram samples were manually annotated to train an AI system, composed of a two-stage convolutional neural network and a software-as-a-service system. Further, more than 100 orthodontists participated to refine the AI-output landmark localizations and retrain this system. RESULTS The average landmark prediction error of this system was as low as 0.94 ± 0.74 mm and the system achieved an average classification accuracy of 89.33%. CONCLUSIONS An automatic cephalometric analysis system based on convolutional neural network was proposed, which can realize automatic landmark location and cephalometric measurements classification. This system showed promise in improving diagnostic efficiency in clinical circumstances.
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Affiliation(s)
- Fulin Jiang
- Department of Orthodontics, State Key Laboratory of Oral Diseases, West China School of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
- Chengdu Boltzmann Intelligence Technology Co., Ltd, Chengdu, China
| | - Yutong Guo
- Department of Orthodontics, State Key Laboratory of Oral Diseases, West China School of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Cai Yang
- Department of Orthodontics, State Key Laboratory of Oral Diseases, West China School of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Yimei Zhou
- Department of Orthodontics, State Key Laboratory of Oral Diseases, West China School of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Yucheng Lin
- Chengdu Boltzmann Intelligence Technology Co., Ltd, Chengdu, China
| | - Fangyuan Cheng
- Chengdu Boltzmann Intelligence Technology Co., Ltd, Chengdu, China
| | - Shuqi Quan
- Department of Orthodontics, State Key Laboratory of Oral Diseases, West China School of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Qingchen Feng
- Department of Orthodontics, State Key Laboratory of Oral Diseases, West China School of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Juan Li
- Department of Orthodontics, State Key Laboratory of Oral Diseases, West China School of Stomatology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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Junaid N, Khan N, Ahmed N, Abbasi MS, Das G, Maqsood A, Ahmed AR, Marya A, Alam MK, Heboyan A. Development, Application, and Performance of Artificial Intelligence in Cephalometric Landmark Identification and Diagnosis: A Systematic Review. Healthcare (Basel) 2022; 10. [PMID: 36553978 DOI: 10.3390/healthcare10122454] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/26/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022] Open
Abstract
This study aimed to analyze the existing literature on how artificial intelligence is being used to support the identification of cephalometric landmarks. The systematic analysis of literature was carried out by performing an extensive search in PubMed/MEDLINE, Google Scholar, Cochrane, Scopus, and Science Direct databases. Articles published in the last ten years were selected after applying the inclusion and exclusion criteria. A total of 17 full-text articles were systematically appraised. The Cochrane Handbook for Systematic Reviews of Interventions (CHSRI) and Newcastle-Ottawa quality assessment scale (NOS) were adopted for quality analysis of the included studies. The artificial intelligence systems were mainly based on deep learning-based convolutional neural networks (CNNs) in the included studies. The majority of the studies proposed that AI-based automatic cephalometric analyses provide clinically acceptable diagnostic performance. They have worked remarkably well, with accuracy and precision similar to the trained orthodontist. Moreover, they can simplify cephalometric analysis and provide a quick outcome in practice. Therefore, they are of great benefit to orthodontists, as with these systems they can perform tasks more efficiently.
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