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Hartoonian S, Hosseini M, Yousefi I, Mahdian M, Ghazizadeh Ahsaie M. Applications of artificial intelligence in dentomaxillofacial imaging-a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol 2024:S2212-4403(23)01566-3. [PMID: 38637235 DOI: 10.1016/j.oooo.2023.12.790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/02/2023] [Accepted: 12/22/2023] [Indexed: 04/20/2024]
Abstract
BACKGROUND Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities. STUDY DESIGN A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another. RESULTS The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection. CONCLUSION Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.
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Affiliation(s)
- Serlie Hartoonian
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Matine Hosseini
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Iman Yousefi
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mina Mahdian
- Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Mitra Ghazizadeh Ahsaie
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Artificial intelligence models for clinical usage in dentistry with a focus on dentomaxillofacial CBCT: a systematic review. Oral Radiol 2023; 39:18-40. [PMID: 36269515 DOI: 10.1007/s11282-022-00660-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 09/29/2022] [Indexed: 01/05/2023]
Abstract
This study aimed at performing a systematic review of the literature on the application of artificial intelligence (AI) in dental and maxillofacial cone beam computed tomography (CBCT) and providing comprehensive descriptions of current technical innovations to assist future researchers and dental professionals. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA) Statement was followed. The study's protocol was prospectively registered. Following databases were searched, based on MeSH and Emtree terms: PubMed/MEDLINE, Embase and Web of Science. The search strategy enrolled 1473 articles. 59 publications were included, which assessed the use of AI on CBCT images in dentistry. According to the PROBAST guidelines for study design, seven papers reported only external validation and 11 reported both model building and validation on an external dataset. 40 studies focused exclusively on model development. The AI models employed mainly used deep learning models (42 studies), while other 17 papers used conventional approaches, such as statistical-shape and active shape models, and traditional machine learning methods, such as thresholding-based methods, support vector machines, k-nearest neighbors, decision trees, and random forests. Supervised or semi-supervised learning was utilized in the majority (96.62%) of studies, and unsupervised learning was used in two (3.38%). 52 publications included studies had a high risk of bias (ROB), two papers had a low ROB, and four papers had an unclear rating. Applications based on AI have the potential to improve oral healthcare quality, promote personalized, predictive, preventative, and participatory dentistry, and expedite dental procedures.
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Evaluating the Relationship between Mandibular Third Molar and Mandibular Canal with Semiautomatic Segmentation: A Pilot Study on CBCT Datasets. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12010502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Inferior alveolar nerve injury is the main complication in mandibular third molar surgery. In this context, cone-beam computed tomography (CBCT) has become of crucial importance in evaluating the relationship between mandibular third molar and inferior alveolar nerve. Due to the growing interest in preoperative planning in oral surgery, several post-processing techniques have been implemented to obtain three-dimensional reconstructions of a volume of interest. In the present study, segmentation techniques were retrospectively applied to CBCT images in order to evaluate whether post-processing could offer better visualization of the structures of interest. Forty CBCT examinations performed for inferior third molar impaction were analyzed. Segmentation and volumetric reconstructions were performed. A dataset composed of multiplanar reconstructions for each study case, including segmented images, was submitted for evaluation to two oral surgeons, two general practitioners and four residents in oral surgery. The visualization of root morphology, canal course, and the relationship with mandibular cortical bone on both native CBCT and segmented images were assessed. Inter-rater agreement showed values of intraclass correlation coefficient (ICC) above 0.8 for all the examined parameters. Oral surgeons presented higher ICC values (p < 0.05). Segmented images can improve preoperative evaluation of the third molar and its relationship with the surrounding anatomical structures compared to native CBCT images. Further evaluation is needed to validate these preliminary results.
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Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K. Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofac Radiol 2022; 51:20210197. [PMID: 34233515 PMCID: PMC8693331 DOI: 10.1259/dmfr.20210197] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in the field of dental and maxillofacial radiology. Dental radiography, which is commonly used in daily practices, provides an incredibly rich resource for AI development and attracted many researchers to develop its application for various purposes. This study reviewed the applicability of AI for dental radiography from the current studies. Online searches on PubMed and IEEE Xplore databases, up to December 2020, and subsequent manual searches were performed. Then, we categorized the application of AI according to similarity of the following purposes: diagnosis of dental caries, periapical pathologies, and periodontal bone loss; cyst and tumor classification; cephalometric analysis; screening of osteoporosis; tooth recognition and forensic odontology; dental implant system recognition; and image quality enhancement. Current development of AI methodology in each aforementioned application were subsequently discussed. Although most of the reviewed studies demonstrated a great potential of AI application for dental radiography, further development is still needed before implementation in clinical routine due to several challenges and limitations, such as lack of datasets size justification and unstandardized reporting format. Considering the current limitations and challenges, future AI research in dental radiography should follow standardized reporting formats in order to align the research designs and enhance the impact of AI development globally.
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Affiliation(s)
| | - Chiaki Doi
- Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, 4–1 Seiryo-machi, Sendai, Japan
| | - Nobuhiro Yoda
- Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, 4–1 Seiryo-machi, Sendai, Japan
| | - Eha Renwi Astuti
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Jl. Mayjen Prof. Dr. Moestopo no 47, Surabaya, Indonesia
| | - Keiichi Sasaki
- Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, 4–1 Seiryo-machi, Sendai, Japan
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Wang X, Pastewait M, Wu TH, Lian C, Tejera B, Lee YT, Lin FC, Wang L, Shen D, Li S, Ko CC. 3D morphometric quantification of maxillae and defects for patients with unilateral cleft palate via deep learning-based CBCT image auto-segmentation. Orthod Craniofac Res 2021; 24 Suppl 2:108-116. [PMID: 33711187 DOI: 10.1111/ocr.12482] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 01/25/2021] [Accepted: 03/01/2021] [Indexed: 12/01/2022]
Abstract
OBJECTIVE This study aimed to quantify the 3D asymmetry of the maxilla in patients with unilateral cleft lip and palate (UCP) and investigate the defect factors responsible for the variability of the maxilla on the cleft side using a deep-learning-based CBCT image segmentation protocol. SETTING AND SAMPLE POPULATION Cone beam computed tomography (CBCT) images of 60 patients with UCP were acquired. The samples in this study consisted of 39 males and 21 females, with a mean age of 11.52 years (SD = 3.27 years; range of 8-18 years). MATERIALS AND METHODS The deep-learning-based protocol was used to segment the maxilla and defect initially, followed by manual refinement. Paired t-tests were performed to characterize the maxillary asymmetry. A multiple linear regression was carried out to investigate the relationship between the defect parameters and those of the cleft side of the maxilla. RESULTS The cleft side of the maxilla demonstrated a significant decrease in maxillary volume and length as well as alveolar length, anterior width, posterior width, anterior height and posterior height. A significant increase in maxillary anterior width was demonstrated on the cleft side of the maxilla. There was a close relationship between the defect parameters and those of the cleft side of the maxilla. CONCLUSIONS Based on the 3D volumetric segmentations, significant hypoplasia of the maxilla on the cleft side existed in the pyriform aperture and alveolar crest area near the defect. The defect structures appeared to contribute to the variability of the maxilla on the cleft side.
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Affiliation(s)
- Xiaoyu Wang
- Department of Orthodontics, Beijing Stomatological Hospital, Capital Medical University, Beijing, China.,Department of Stomatology, Beijing Tian Tan Hospital, Capital Medical University, Beijing, China
| | | | - Tai-Hsien Wu
- Division of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA
| | - Chunfeng Lian
- School of Mathematics and Statistics, Xi'an Jiaotong University, Shaanxi, China
| | - Beatriz Tejera
- Orthodontics, Nova Southeastern University, Ft. Lauderdale, FL, USA
| | - Yan-Ting Lee
- Oral and Craniofacial Health Sciences Research, Adam School of Dentistry, University of North Carolina, Chapel Hill, NC, USA
| | - Feng-Chang Lin
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.,School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.,Department of Artificial Intelligence, Korea University, Seoul, Korea
| | - Song Li
- Department of Orthodontics, Beijing Stomatological Hospital, Capital Medical University, Beijing, China
| | - Ching-Chang Ko
- Division of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA
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Hung K, Yeung AWK, Tanaka R, Bornstein MM. Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17124424. [PMID: 32575560 PMCID: PMC7345758 DOI: 10.3390/ijerph17124424] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 06/12/2020] [Accepted: 06/16/2020] [Indexed: 12/15/2022]
Abstract
The increasing use of three-dimensional (3D) imaging techniques in dental medicine has boosted the development and use of artificial intelligence (AI) systems for various clinical problems. Cone beam computed tomography (CBCT) and intraoral/facial scans are potential sources of image data to develop 3D image-based AI systems for automated diagnosis, treatment planning, and prediction of treatment outcome. This review focuses on current developments and performance of AI for 3D imaging in dentomaxillofacial radiology (DMFR) as well as intraoral and facial scanning. In DMFR, machine learning-based algorithms proposed in the literature focus on three main applications, including automated diagnosis of dental and maxillofacial diseases, localization of anatomical landmarks for orthodontic and orthognathic treatment planning, and general improvement of image quality. Automatic recognition of teeth and diagnosis of facial deformations using AI systems based on intraoral and facial scanning will very likely be a field of increased interest in the future. The review is aimed at providing dental practitioners and interested colleagues in healthcare with a comprehensive understanding of the current trend of AI developments in the field of 3D imaging in dental medicine.
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Affiliation(s)
- Kuofeng Hung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong 999077, China; (K.H.); (A.W.K.Y.); (R.T.)
| | - Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong 999077, China; (K.H.); (A.W.K.Y.); (R.T.)
| | - Ray Tanaka
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong 999077, China; (K.H.); (A.W.K.Y.); (R.T.)
| | - Michael M. Bornstein
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong 999077, China; (K.H.); (A.W.K.Y.); (R.T.)
- Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, 4058 Basel, Switzerland
- Correspondence: ; Tel.: +41-(0)61-267-25-45
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