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Setzer FC, Li J, Khan AA. The Use of Artificial Intelligence in Endodontics. J Dent Res 2024:220345241255593. [PMID: 38822561 DOI: 10.1177/00220345241255593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2024] Open
Abstract
Endodontics is the dental specialty foremost concerned with diseases of the pulp and periradicular tissues. Clinicians often face patients with varying symptoms, must critically assess radiographic images in 2 and 3 dimensions, derive complex diagnoses and decision making, and deliver sophisticated treatment. Paired with low intra- and interobserver agreement for radiographic interpretation and variations in treatment outcome resulting from nonstandardized clinical techniques, there exists an unmet need for support in the form of artificial intelligence (AI), providing automated biomedical image analysis, decision support, and assistance during treatment. In the past decade, there has been a steady increase in AI studies in endodontics but limited clinical application. This review focuses on critically assessing the recent advancements in endodontic AI research for clinical applications, including the detection and diagnosis of endodontic pathologies such as periapical lesions, fractures and resorptions, as well as clinical treatment outcome predictions. It discusses the benefits of AI-assisted diagnosis, treatment planning and execution, and future directions including augmented reality and robotics. It critically reviews the limitations and challenges imposed by the nature of endodontic data sets, AI transparency and generalization, and potential ethical dilemmas. In the near future, AI will significantly affect the everyday endodontic workflow, education, and continuous learning.
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Affiliation(s)
- F C Setzer
- Department of Endodontics, University of Pennsylvania, Philadelphia, PA, USA
| | - J Li
- School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - A A Khan
- Department of Endodontics, University of Texas Health, San Antonio, TX, USA
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Aminoshariae A, Nosrat A, Nagendrababu V, Dianat O, Mohammad-Rahimi H, O'Keefe AW, Setzer FC. Artificial Intelligence in Endodontic Education. J Endod 2024; 50:562-578. [PMID: 38387793 DOI: 10.1016/j.joen.2024.02.011] [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: 10/23/2023] [Revised: 01/15/2024] [Accepted: 02/12/2024] [Indexed: 02/24/2024]
Abstract
AIMS The future dental and endodontic education must adapt to the current digitalized healthcare system in a hyper-connected world. The purpose of this scoping review was to investigate the ways an endodontic education curriculum could benefit from the implementation of artificial intelligence (AI) and overcome the limitations of this technology in the delivery of healthcare to patients. METHODS An electronic search was carried out up to December 2023 using MEDLINE, Web of Science, Cochrane Library, and a manual search of reference literature. Grey literature, ongoing clinical trials were also searched using ClinicalTrials.gov. RESULTS The search identified 251 records, of which 35 were deemed relevant to artificial intelligence (AI) and Endodontic education. Areas in which AI might aid students with their didactic and clinical endodontic education were identified as follows: 1) radiographic interpretation; 2) differential diagnosis; 3) treatment planning and decision-making; 4) case difficulty assessment; 5) preclinical training; 6) advanced clinical simulation and case-based training, 7) real-time clinical guidance; 8) autonomous systems and robotics; 9) progress evaluation and personalized education; 10) calibration and standardization. CONCLUSIONS AI in endodontic education will support clinical and didactic teaching through individualized feedback; enhanced, augmented, and virtually generated training aids; automated detection and diagnosis; treatment planning and decision support; and AI-based student progress evaluation, and personalized education. Its implementation will inarguably change the current concept of teaching Endodontics. Dental educators would benefit from introducing AI in clinical and didactic pedagogy; however, they must be aware of AI's limitations and challenges to overcome.
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Affiliation(s)
| | - Ali Nosrat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland Baltimore, Baltimore, Maryland; Private Practice, Centreville Endodontics, Centreville, Virginia
| | - Venkateshbabu Nagendrababu
- Department of Preventive and Restorative Dentistry, University of Sharjah, College of Dental Medicine, Sharjah, United Arab Emirates
| | - Omid Dianat
- Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, School of Dentistry, University of Maryland Baltimore, Baltimore, Maryland; Private Practice, Centreville Endodontics, Centreville, Virginia
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Federal Republic of Germany
| | | | - Frank C Setzer
- Department of Endodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Wei X, Du Y, Zhou X, Yue L, Yu Q, Hou B, Chen Z, Liang J, Chen W, Qiu L, Huang X, Meng L, Huang D, Wang X, Tian Y, Tang Z, Zhang Q, Miao L, Zhao J, Yang D, Yang J, Ling J. Expert consensus on digital guided therapy for endodontic diseases. Int J Oral Sci 2023; 15:54. [PMID: 38052782 DOI: 10.1038/s41368-023-00261-0] [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: 10/15/2023] [Revised: 11/12/2023] [Accepted: 11/12/2023] [Indexed: 12/07/2023] Open
Abstract
Digital guided therapy (DGT) has been advocated as a contemporary computer-aided technique for treating endodontic diseases in recent decades. The concept of DGT for endodontic diseases is categorized into static guided endodontics (SGE), necessitating a meticulously designed template, and dynamic guided endodontics (DGE), which utilizes an optical triangulation tracking system. Based on cone-beam computed tomography (CBCT) images superimposed with or without oral scan (OS) data, a virtual template is crafted through software and subsequently translated into a 3-dimensional (3D) printing for SGE, while the system guides the drilling path with a real-time navigation in DGE. DGT was reported to resolve a series of challenging endodontic cases, including teeth with pulp obliteration, teeth with anatomical abnormalities, teeth requiring retreatment, posterior teeth needing endodontic microsurgery, and tooth autotransplantation. Case reports and basic researches all demonstrate that DGT stand as a precise, time-saving, and minimally invasive approach in contrast to conventional freehand method. This expert consensus mainly introduces the case selection, general workflow, evaluation, and impact factor of DGT, which could provide an alternative working strategy in endodontic treatment.
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Affiliation(s)
- Xi Wei
- Department of Operative Dentistry and Endodontics, Hospital of Stomatology, Guanghua, School of Stomatology, Sun Yat-Sen University & Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Yu Du
- Department of Operative Dentistry and Endodontics, Hospital of Stomatology, Guanghua, School of Stomatology, Sun Yat-Sen University & Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Xuedong Zhou
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Cariology and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Lin Yue
- Department of Cariology and Endodontology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, Beijing, China
| | - Qing Yu
- Department of Operative Dentistry & Endodontics, School of Stomatology, The Fourth Military Medical University, Xi'an, China
| | - Benxiang Hou
- Department of Endodontics, Beijing Stomatological Hospital, School of Stomatology, Capital Medical University, Beijing, China
| | - Zhi Chen
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Jingping Liang
- Department of Endodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Clinical Research Center for Oral Diseases; National Center for Stomatology; Shanghai Key Laboratory of Stomatology, Shanghai, China
| | - Wenxia Chen
- College of Stomatology, Hospital of Stomatology, Guangxi Medical University, Nanning, China
| | - Lihong Qiu
- Department of Endodontics, School of Stomatology, China Medical University, Shenyang, China
| | - Xiangya Huang
- Department of Operative Dentistry and Endodontics, Hospital of Stomatology, Guanghua, School of Stomatology, Sun Yat-Sen University & Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Liuyan Meng
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Dingming Huang
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Cariology and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Xiaoyan Wang
- Department of Cariology and Endodontology, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Laboratory for Digital and Material Technology of Stomatology & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, Beijing, China
| | - Yu Tian
- Department of Operative Dentistry & Endodontics, School of Stomatology, The Fourth Military Medical University, Xi'an, China
| | - Zisheng Tang
- Department of Stomatology, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Qi Zhang
- Department of Endodontics, Stomatological Hospital and Dental School of Tongji University, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, Shanghai, China
| | - Leiying Miao
- Department of Cariology and Endodontics, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China
| | - Jin Zhao
- Department of Endodontics, First Affiliated Hospital of Xinjiang Medical University, and College of Stomatology of Xinjiang Medical University, Urumqi, China
| | - Deqin Yang
- Department of Endodontics, Stomatological Hospital of Chongqing Medical University, Chongqing, China
| | - Jian Yang
- Department of Endodontics, The Affiliated Stomatological Hospital of Nanchang University, Nanchang, China
| | - Junqi Ling
- Department of Operative Dentistry and Endodontics, Hospital of Stomatology, Guanghua, School of Stomatology, Sun Yat-Sen University & Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China.
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Fan X, Tao B, Tu P, Shen Y, Wu Y, Chen X. A novel mixed reality-guided dental implant placement navigation system based on virtual-actual registration. Comput Biol Med 2023; 166:107560. [PMID: 37847946 DOI: 10.1016/j.compbiomed.2023.107560] [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: 05/15/2023] [Revised: 09/14/2023] [Accepted: 10/10/2023] [Indexed: 10/19/2023]
Abstract
BACKGROUNDS The key to successful dental implant surgery is to place the implants accurately along the pre-operative planned paths. The application of surgical navigation systems can significantly improve the safety and accuracy of implantation. However, the frequent shift of the views of the surgeon between the surgical site and the computer screen causes troubles, which is expected to be solved by the introduction of mixed-reality technology through the wearing of HoloLens devices by enabling the alignment of the virtual three-dimensional (3D) image with the actual surgical site in the same field of view. METHODS This study utilized mixed reality technology to enhance dental implant surgery navigation. Our first step was reconstructing a virtual 3D model from pre-operative cone-beam CT (CBCT) images. We then obtained the relative position between objects using the navigation device and HoloLens camera. Via the algorithms of virtual-actual registration, the transformation matrixes between the HoloLens devices and the navigation tracker were acquired through the HoloLens-tracker registration, and the transformation matrixes between the virtual model and the patient phantom through the image-phantom registration. In addition, the algorithm of surgical drill calibration assisted in acquiring transformation matrixes between the surgical drill and the patient phantom. These algorithms allow real-time tracking of the surgical drill's location and orientation relative to the patient phantom under the navigation device. With the aid of the HoloLens 2, virtual 3D images and actual patient phantoms can be aligned accurately, providing surgeons with a clear visualization of the implant path. RESULTS Phantom experiments were conducted using 30 patient phantoms, with a total of 102 dental implants inserted. Comparisons between the actual implant paths and the pre-operatively planned implant paths showed that our system achieved a coronal deviation of 1.507 ± 0.155 mm, an apical deviation of 1.542 ± 0.143 mm, and an angular deviation of 3.468 ± 0.339°. The deviation was not significantly different from that of the navigation-guided dental implant placement but better than the freehand dental implant placement. CONCLUSION Our proposed system realizes the integration of the pre-operative planned dental implant paths and the patient phantom, which helps surgeons achieve adequate accuracy in traditional dental implant surgery. Furthermore, this system is expected to be applicable to animal and cadaveric experiments in further studies.
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Affiliation(s)
- Xingqi Fan
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Baoxin Tao
- Department of Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Puxun Tu
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yihan Shen
- Department of Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Yiqun Wu
- Department of Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Xiaojun Chen
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.
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Novel method for augmented reality guided endodontics: an in vitro study. J Dent 2023; 132:104476. [PMID: 36905949 DOI: 10.1016/j.jdent.2023.104476] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 02/02/2023] [Accepted: 02/28/2023] [Indexed: 03/11/2023] Open
Abstract
OBJECTIVE The aim of this study is to evaluate the accuracy in endodontics of a novel augmented reality (AR) method for guided access cavity preparation in 3D-printed jaws. METHODS Two operators with different levels of experience in endodontics performed pre-planned virtually guided access cavities through a novel markerless AR system developed by a team among the authors on three sets of 3D-printed jaw models using a 3D printer (Objet Connex 350, Stratasys) mounted on a phantom. After the treatment, a post-operative high-resolution CBCT scan (NewTom VGI Evo, Cefla) was taken for each model and registered to the pre-operative model. All the access cavities were then digitally reconstructed by filling the cavity area using 3D medical software (3-Matic 15.0, Materialise). For the anterior teeth and the premolars, the deviation at the coronal and apical entry points as well as the angular deviation of the access cavity were compared to the virtual plan. For the molars, the deviation at the coronal entry point was compared to the virtual plan. Additionally, the surface area of all access cavities at the entry point was measured and compared to the virtual plan. Descriptive statistics for each parameter were performed. A 95% confidence interval was calculated. RESULTS A total of 90 access cavities were drilled up to a depth of 4 mm inside the tooth. The mean deviation in the frontal teeth and in the premolars at the entry point was 0.51 mm and 0.77 mm at the apical point, with a mean angular deviation of 8.5° and a mean surface overlap of 57%. The mean deviation for the molars at the entry point was 0.63 mm, with a mean surface overlap of 82%. CONCLUSION The use of AR as a digital guide for endodontic access cavity drilling on different teeth showed promising results and might have potential for clinical use. However, further development and research might be needed before in vivo validation to overcome the limitations of the study.
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