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Dot G, Chaurasia A, Dubois G, Savoldelli C, Haghighat S, Azimian S, Taramsari AR, Sivaramakrishnan G, Issa J, Dubey A, Schouman T, Gajny L. DentalSegmentator: Robust open source deep learning-based CT and CBCT image segmentation. J Dent 2024; 147:105130. [PMID: 38878813 DOI: 10.1016/j.jdent.2024.105130] [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: 04/19/2024] [Revised: 06/08/2024] [Accepted: 06/12/2024] [Indexed: 06/30/2024] Open
Abstract
OBJECTIVES Segmentation of anatomical structures on dento-maxillo-facial (DMF) computed tomography (CT) or cone beam computed tomography (CBCT) scans is increasingly needed in digital dentistry. The main aim of this research was to propose and evaluate a novel open source tool called DentalSegmentator for fully automatic segmentation of five anatomical structures on DMF CT and CBCT scans: maxilla/upper skull, mandible, upper teeth, lower teeth, and the mandibular canal. METHODS A retrospective sample of 470 CT and CBCT scans was used as a training/validation set. The performance and generalizability of the tool was evaluated by comparing segmentations provided by experts and automatic segmentations in two hold-out test datasets: an internal dataset of 133 CT and CBCT scans acquired before orthognathic surgery and an external dataset of 123 CBCT scans randomly sampled from routine examinations in 5 institutions. RESULTS The mean overall results in the internal test dataset (n = 133) were a Dice similarity coefficient (DSC) of 92.2 ± 6.3 % and a normalised surface distance (NSD) of 98.2 ± 2.2 %. The mean overall results on the external test dataset (n = 123) were a DSC of 94.2 ± 7.4 % and a NSD of 98.4 ± 3.6 %. CONCLUSIONS The results obtained from this highly diverse dataset demonstrate that this tool can provide fully automatic and robust multiclass segmentation for DMF CT and CBCT scans. To encourage the clinical deployment of DentalSegmentator, the pre-trained nnU-Net model has been made publicly available along with an extension for the 3D Slicer software. CLINICAL SIGNIFICANCE DentalSegmentator open source 3D Slicer extension provides a free, robust, and easy-to-use approach to obtaining patient-specific three-dimensional models from CT and CBCT scans. These models serve various purposes in a digital dentistry workflow, such as visualization, treatment planning, intervention, and follow-up.
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Affiliation(s)
- Gauthier Dot
- UFR Odontologie, Universite Paris Cité, Paris, France; Service de Medecine Bucco-Dentaire, AP-HP, Hopital Pitie-Salpetriere, Paris, France; Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers Institute of Technology, Paris, France.
| | - Akhilanand Chaurasia
- Department of Oral Medicine and Radiology, Faculty of Dental Sciences, King George Medical University, Lucknow, Uttar Pradesh, India
| | - Guillaume Dubois
- Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers Institute of Technology, Paris, France; Materialise France, Malakoff, France
| | - Charles Savoldelli
- Department of Oral and Maxillofacial Surgery, Head and Neck Institute, University Hospital of Nice, France
| | - Sara Haghighat
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI On Health, Berlin, Germany
| | - Sarina Azimian
- Research Committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | | | - Julien Issa
- Department of Diagnostics, Chair of Practical Clinical Dentistry, Poznan University of Medical Sciences, Poznan, Poland; Doctoral School, Poznan University of Medical Sciences, Poznan, Poland
| | - Abhishek Dubey
- Department of Oral Medicine and Radiology, Maharana Pratap Dental College, Kanpur, India
| | - Thomas Schouman
- Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers Institute of Technology, Paris, France; AP-HP, Hopital Pitie-Salpetriere, Service de Chirurgie Maxillo-Faciale, Medecine Sorbonne Universite, Paris, France
| | - Laurent Gajny
- Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers Institute of Technology, Paris, France
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Lahoud P, Jacobs R, Elahi SA, Ducret M, Lauwers W, van Lenthe GH, Richert R, EzEldeen M. Developing Advanced Patient-Specific In Silico Models: A New Era in Biomechanical Analysis of Tooth Autotransplantation. J Endod 2024; 50:820-826. [PMID: 38452866 DOI: 10.1016/j.joen.2024.02.022] [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: 10/27/2023] [Revised: 01/20/2024] [Accepted: 02/25/2024] [Indexed: 03/09/2024]
Abstract
INTRODUCTION As personalized medicine advances, there is an escalating need for sophisticated tools to understand complex biomechanical phenomena in clinical research. Recognizing a significant gap, this study pioneers the development of patient-specific in silico models for tooth autotransplantation (TAT), setting a new standard for predictive accuracy and reliability in evaluating TAT outcomes. METHODS Development of the models relied on 6 consecutive cases of young patients (mean age 11.66 years ± 0.79), all undergoing TAT procedures. The development process involved creating detailed in silico replicas of patient oral structures, focusing on transplanting upper premolars to central incisors. These models underpinned finite element analysis simulations, testing various masticatory and traumatic scenarios. RESULTS The models highlighted critical biomechanical insights. The finite element models indicated homogeneous stress distribution in control teeth, contrasted by shape-dependent stress patterns in transplanted teeth. The surface deviation in the postoperative year for the transplanted elements showed a mean deviation of 0.33 mm (±0.28), significantly higher than their contralateral counterparts at 0.05 mm (±0.04). CONCLUSIONS By developing advanced patient-specific in silico models, we are ushering in a transformative era in TAT research and practice. These models are not just analytical tools; they are predictive instruments capturing patient uniqueness, including anatomical, masticatory, and tissue variables, essential for understanding biomechanical responses in TAT. This foundational work paves the way for future studies, where applying these models to larger cohorts will further validate their predictive capabilities and influence on TAT success parameters.
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Affiliation(s)
- Pierre Lahoud
- Department of Oral and Maxillofacial Surgery & Imaging and Pathology, OMFS-IMPATH Research Group, University Hospitals Leuven, KU Leuven, Belgium; Division of Periodontology & Oral Microbiology, Department of Oral Health Sciences-University Hospitals Leuven, KU Leuven, Belgium.
| | - Reinhilde Jacobs
- Department of Oral and Maxillofacial Surgery & Imaging and Pathology, OMFS-IMPATH Research Group, University Hospitals Leuven, KU Leuven, Belgium; Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Seyed Ali Elahi
- Department of Movement Sciences, Human Movement Biomechanics Research Group, KU Leuven, Leuven, Belgium; Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | - Maxime Ducret
- Laboratoire de Biologie Tissulaire et Ingénierie thérapeutique, UMR 5305 CNRS/Université Claude Bernard Lyon 1, UMS 3444 BioSciences Gerland- Lyon Sud, Lyon, France; Service d'Odontologie, Hospices Civils de Lyon, Lyon, France
| | - Wout Lauwers
- Department of Oral and Maxillofacial Surgery & Imaging and Pathology, OMFS-IMPATH Research Group, University Hospitals Leuven, KU Leuven, Belgium
| | | | - Raphaël Richert
- Service d'Odontologie, Hospices Civils de Lyon, Lyon, France; Univ Lyon, INSA Lyon, CNRS, LaMCoS, UMR5259, Villeurbanne, France
| | - Mostafa EzEldeen
- Department of Oral and Maxillofacial Surgery & Imaging and Pathology, OMFS-IMPATH Research Group, University Hospitals Leuven, KU Leuven, Belgium; Department of Oral Health Sciences, KU Leuven and Paediatric Dentistry and Special Dental Care, University Hospitals Leuven, KU Leuven, Leuven, Belgium
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Tyndall DA, Price JB, Gaalaas L, Spin-Neto R. Surveying the landscape of diagnostic imaging in dentistry's future: Four emerging technologies with promise. J Am Dent Assoc 2024; 155:364-378. [PMID: 38520421 DOI: 10.1016/j.adaj.2024.01.005] [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: 05/24/2023] [Revised: 01/04/2024] [Accepted: 01/07/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Advances in digital radiography for both intraoral and panoramic imaging and cone-beam computed tomography have led the way to an increase in diagnostic capabilities for the dental care profession. In this article, the authors provide information on 4 emerging technologies with promise. TYPES OF STUDIES REVIEWED The authors feature the following: artificial intelligence in the form of deep learning using convolutional neural networks, dental magnetic resonance imaging, stationary intraoral tomosynthesis, and second-generation cone-beam computed tomography sources based on carbon nanotube technology and multispectral imaging. The authors review and summarize articles featuring these technologies. RESULTS The history and background of these emerging technologies are previewed along with their development and potential impact on the practice of dental diagnostic imaging. The authors conclude that these emerging technologies have the potential to have a substantial influence on the practice of dentistry as these systems mature. The degree of influence most likely will vary, with artificial intelligence being the most influential of the 4. CONCLUSIONS AND PRACTICAL IMPLICATIONS The readers are informed about these emerging technologies and the potential effects on their practice going forward, giving them information on which to base decisions on adopting 1 or more of these technologies. The 4 technologies reviewed in this article have the potential to improve imaging diagnostics in dentistry thereby leading to better patient care and heightened professional satisfaction.
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Picoli FF, Fontenele RC, Van der Cruyssen F, Ahmadzai I, Trigeminal Nerve Injuries Research Group, Politis C, Silva MAG, Jacobs R. Risk assessment of inferior alveolar nerve injury after wisdom tooth removal using 3D AI-driven models: A within-patient study. J Dent 2023; 139:104765. [PMID: 38353315 DOI: 10.1016/j.jdent.2023.104765] [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: 07/20/2023] [Revised: 10/10/2023] [Accepted: 10/26/2023] [Indexed: 02/16/2024] Open
Abstract
OBJECTIVE To compare a three-dimensional (3D) artificial intelligence (AI)- driven model with panoramic radiography (PANO) and cone-beam computed tomography (CBCT) in assessing the risk of inferior alveolar nerve (IAN) injury after mandibular wisdom tooth (M3M) removal through a within-patient controlled trial. METHODS From a database of 6,010 patients undergoing M3M surgery, 25 patients met the inclusion criteria of bilateral M3M removal with postoperative unilateral IAN injury. In this within-patient controlled trial, preoperative PANO and CBCT images were available, while 3D-AI models of the mandibular canal and teeth were generated from the CBCT images using the Virtual Patient Creator AI platform (Relu BV, Leuven, Belgium). Five examiners, who were blinded to surgical outcomes, assessed the imaging modalities and assigned scores indicating the risk level of IAN injury (high, medium, or low risk). Sensitivity, specificity, and area under receiver operating curve (AUC) for IAN risk assessment were calculated for each imaging modality. RESULTS For IAN injury risk assessment after M3M removal, sensitivity was 0.87 for 3D-AI, 0.89 for CBCT versus 0.73 for PANO. Furthermore, the AUC and specificity values were 0.63 and 0.39 for 3D-AI, 0.58 and 0.28 for CBCT, and 0.57 and 0.41 for PANO, respectively. There was no statistically significant difference (p>0.05) among the imaging modalities for any diagnostic parameters. CONCLUSION This within-patient controlled trial study revealed that risk assessment for IAN injury after M3M removal was rather similar for 3D-AI, PANO, and CBCT, with a sensitivity for injury prediction reaching up to 0.87 for 3D-AI and 0.89 for CBCT. CLINICAL SIGNIFICANCE This within-patient trial is pioneering in exploring the application of 3D AI-driven models for assessing IAN injury risk after M3M removal. The present results indicate that AI-powered 3D models based on CBCT might facilitate IAN risk assessment of M3M removal.
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Affiliation(s)
- Fernando Fortes Picoli
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven and Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven, Kapucijnenvoer 7, 3000, Leuven, Belgium; School of Dentistry, Federal University of Goiás, Goiânia, GO, Brazil
| | - Rocharles Cavalcante Fontenele
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven and Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven, Kapucijnenvoer 7, 3000, Leuven, Belgium; Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, Sao Paulo, Brazil
| | - Frederic Van der Cruyssen
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven and Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven, Kapucijnenvoer 7, 3000, Leuven, Belgium; Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
| | - Iraj Ahmadzai
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven and Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven, Kapucijnenvoer 7, 3000, Leuven, Belgium
| | | | - Constantinus Politis
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven and Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven, Kapucijnenvoer 7, 3000, Leuven, Belgium; Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
| | | | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven and Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven, Kapucijnenvoer 7, 3000, Leuven, Belgium; Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden.
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Bonny T, Al Nassan W, Obaideen K, Al Mallahi MN, Mohammad Y, El-damanhoury HM. Contemporary Role and Applications of Artificial Intelligence in Dentistry. F1000Res 2023; 12:1179. [PMID: 37942018 PMCID: PMC10630586 DOI: 10.12688/f1000research.140204.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/24/2023] [Indexed: 11/10/2023] Open
Abstract
Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.
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Affiliation(s)
- Talal Bonny
- Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Wafaa Al Nassan
- Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Khaled Obaideen
- Sustainable Energy and Power Systems Research Centre, RISE, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Maryam Nooman Al Mallahi
- Department of Mechanical and Aerospace Engineering, United Arab Emirates University, Al Ain City, Abu Dhabi, 27272, United Arab Emirates
| | - Yara Mohammad
- College of Engineering and Information Technology, Ajman University, Ajman University, Ajman, Ajman, United Arab Emirates
| | - Hatem M. El-damanhoury
- Department of Preventive and Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, 27272, United Arab Emirates
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Rokhshad R, Ducret M, Chaurasia A, Karteva T, Radenkovic M, Roganovic J, Hamdan M, Mohammad-Rahimi H, Krois J, Lahoud P, Schwendicke F. Ethical considerations on artificial intelligence in dentistry: A framework and checklist. J Dent 2023; 135:104593. [PMID: 37355089 DOI: 10.1016/j.jdent.2023.104593] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/13/2023] [Accepted: 06/14/2023] [Indexed: 06/26/2023] Open
Abstract
OBJECTIVE Artificial Intelligence (AI) refers to the ability of machines to perform cognitive and intellectual human tasks. In dentistry, AI offers the potential to enhance diagnostic accuracy, improve patient outcomes and streamline workflows. The present study provides a framework and a checklist to evaluate AI applications in dentistry from this perspective. METHODS Lending from existing guidance documents, an initial draft of the checklist and an explanatory paper were derived and discussed among the groups members. RESULTS The checklist was consented to in an anonymous voting process by 29 Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health's members. Overall, 11 principles were identified (diversity, transparency, wellness, privacy protection, solidarity, equity, prudence, law and governance, sustainable development, accountability, and responsibility, respect of autonomy, decision-making). CONCLUSIONS Providers, patients, researchers, industry, and other stakeholders should consider these principles when developing, implementing, or receiving AI applications in dentistry. CLINICAL SIGNIFICANCE While AI has become increasingly commonplace in dentistry, there are ethical concerns around its usage, and users (providers, patients, and other stakeholders), as well as the industry should consider these when developing, implementing, or receiving AI applications based on comprehensive framework to address the associated ethical challenges.
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Affiliation(s)
- Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
| | - Maxime Ducret
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Faculty of Odontology, University Claude Bernard Lyon Il, University of Lyon, Lyon, France
| | - Akhilanand Chaurasia
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Medicine and Radiology, King George's Medical University, Lucknow, India; Faculty of Dentistry, University of Puthisashtra, Combodia
| | - Teodora Karteva
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Operative Dentistry and Endodontics, Faculty of Dental Medicine, Medical University Plovdiv, Bulgaria
| | - Miroslav Radenkovic
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Pharmacology, Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Belgrade, Serbia
| | - Jelena Roganovic
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Pharmacology in Dentistry, School of Dental medicine, University of Belgrade, Serbia
| | - Manal Hamdan
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; General Dental Sciences Department, Marquette University School of Dentistry, USA
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Joachim Krois
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Pierre Lahoud
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany; Department of Oral and MaxilloFacial Surgery & Imaging and Pathology- OMFS-IMPATH Research Group, KU Leuven, Belgium; Division of Periodontology and Oral Microbiology, Department of Oral Health Sciences, KU Leuven, Belgium
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
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Lahoud P, Badrou A, Ducret M, Farges JC, Jacobs R, Bel-Brunon A, EzEldeen M, Blal N, Richert R. Real-time simulation of the transplanted tooth using model order reduction. Front Bioeng Biotechnol 2023; 11:1201177. [PMID: 37456726 PMCID: PMC10339382 DOI: 10.3389/fbioe.2023.1201177] [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: 04/06/2023] [Accepted: 06/19/2023] [Indexed: 07/18/2023] Open
Abstract
The biomechanics of transplanted teeth remain poorly understood due to a lack of models. In this context, finite element (FE) analysis has been used to evaluate the influence of occlusal morphology and root form on the biomechanical behavior of the transplanted tooth, but the construction of a FE model is extremely time-consuming. Model order reduction (MOR) techniques have been used in the medical field to reduce computing time, and the present study aimed to develop a reduced model of a transplanted tooth using the higher-order proper generalized decomposition method. The FE model of a previous study was used to learn von Mises root stress, and axial and lateral forces were used to simulate different occlusions between 75 and 175N. The error of the reduced model varied between 0.1% and 5.9% according to the subdomain, and was the highest for the highest lateral forces. The time for the FE simulation varied between 2.3 and 7.2 h. In comparison, the reduced model was built in 17s and interpolation of new results took approximately 2.10-2s. The use of MOR reduced the time for delivering the root stresses by a mean 5.9 h. The biomechanical behavior of a transplanted tooth simulated by FE models was accurately captured with a significant decrease of computing time. Future studies could include using jaw tracking devices for clinical use and the development of more realistic real-time simulations of tooth autotransplantation surgery.
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Affiliation(s)
- Pierre Lahoud
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Division of Periodontology and Oral Microbiology, Department of Oral Health Sciences, KU Leuven, Leuven, Belgium
| | - Arif Badrou
- Laboratoire de Mécanique Des Contacts Et Structures, CNRS/INSA, Villeurbanne, France
| | - Maxime Ducret
- Laboratoire de Biologie Tissulaire Et Ingénierie Thérapeutique, UMR5305 CNRS/UCBL, Lyon, France
- Hospices Civils de Lyon, Lyon, France
- Faculty of Odontology, Lyon 1 University, Lyon, France
| | - Jean-Christophe Farges
- Laboratoire de Biologie Tissulaire Et Ingénierie Thérapeutique, UMR5305 CNRS/UCBL, Lyon, France
- Hospices Civils de Lyon, Lyon, France
- Faculty of Odontology, Lyon 1 University, Lyon, France
| | - Reinhilde Jacobs
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Aline Bel-Brunon
- Laboratoire de Mécanique Des Contacts Et Structures, CNRS/INSA, Villeurbanne, France
| | - Mostafa EzEldeen
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Department of Oral Health Sciences, KU Leuven and Paediatric Dentistry and Special Dental Care, University Hospitals Leuven, Leuven, Belgium
| | - Nawfal Blal
- Laboratoire de Mécanique Des Contacts Et Structures, CNRS/INSA, Villeurbanne, France
| | - Raphaël Richert
- Laboratoire de Mécanique Des Contacts Et Structures, CNRS/INSA, Villeurbanne, France
- Hospices Civils de Lyon, Lyon, France
- Faculty of Odontology, Lyon 1 University, Lyon, France
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Kharouf N, Pedullà E, Plotino G, Jmal H, Alloui MEH, Simonis P, Laquerriere P, Macaluso V, Abdellatif D, Richert R, Haikel Y, Mancino D. Stronger than Ever: Multifilament Fiberglass Posts Boost Maxillary Premolar Fracture Resistance. J Clin Med 2023; 12:jcm12082975. [PMID: 37109310 PMCID: PMC10143755 DOI: 10.3390/jcm12082975] [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: 03/15/2023] [Revised: 04/11/2023] [Accepted: 04/18/2023] [Indexed: 04/29/2023] Open
Abstract
This paper investigates the influence of cavity configuration and post-endodontic restoration on the fracture resistance, failure mode and stress distribution of premolars by using a method of fracture failure test and finite elements analysis (FEA) coupled to Weibull analysis (WA). One hundred premolars were divided into one control group (Gcontr) (n = 10) and three experimental groups, according to the post-endodontic restoration (n = 30), G1, restored using composite, G2, restored using single fiber post and G3, restored using multifilament fiberglass posts (m-FGP) without post-space preparation. Each experimental group was divided into three subgroups according to the type of coronal cavity configuration (n = 10): G1O, G2O, and G3O with occlusal (O) cavity configuration; G1MO, G2MO, and G3MO with mesio-occlusal (MO); and G1MOD, G2MOD, and G3MOD with mesio-occluso-distal (MOD). After thermomechanical aging, all the specimens were tested under compression load, and failure mode was determined. FEA and WA supplemented destructive tests. Data were statistically analyzed. Irrespective of residual tooth substance, G1 and G2 exhibited lower fracture resistance than Gcontr (p < 0.05), whereas G3 showed no difference compared to Gcontr (p > 0.05). Regarding the type of restoration, no difference was highlighted between G1O and G2O, G1MO and G2MO, or G1MOD and G2MOD (p > 0.05), whereas G3O, G3MO, and G3MOD exhibit higher fracture resistance (p < 0.05) than G1O and G2O, G1MO and G2MO, and G1MOD and G2MOD, respectively. Regarding cavity configuration: in G1 and G2, G1O and G2O exhibited higher fracture resistance than G1MOD and G2MOD, respectively (p < 0.05). In G3, there was no difference among G3O, G3MO and G3MOD (p > 0.05). No difference was found among the different groups and subgroups regarding the failure mode. After aging, premolars restored with multifilament fiberglass posts demonstrated fracture resistance values comparable to those of an intact tooth, irrespective of the different type of cavity configuration.
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Affiliation(s)
- Naji Kharouf
- Department of Biomaterials and Bioengineering, INSERM UMR_S, Strasbourg University, 67000 Strasbourg, France
- Department of Endodontics, Faculty of Dental Medicine, Strasbourg University, 67000 Strasbourg, France
| | - Eugenio Pedullà
- Department of General Surgery and Medical Surgical Specialties, University of Catania, 95128 Catania, Italy
| | - Gianluca Plotino
- Private Practice, Grande Plotino & Torsello-Studio di Odontoiatria, 00187 Rome, Italy
| | - Hamdi Jmal
- ICube Laboratory, UMR 7357 CNRS, Mechanics Department, University of Strasbourg, 67000 Strasbourg, France
| | - Mohammed-El-Habib Alloui
- Department of Biomaterials and Bioengineering, INSERM UMR_S, Strasbourg University, 67000 Strasbourg, France
| | - Philippine Simonis
- Department of Endodontics, Faculty of Dental Medicine, Strasbourg University, 67000 Strasbourg, France
| | | | | | - Dina Abdellatif
- Department of Endodontics, Alexandria University, Alexandria 5424041, Egypt
| | - Raphaël Richert
- Hospices Civils de Lyon, PAM Odontologie, 69100 Lyon, France
- Laboratoire de Mécanique des Contacts et Structures, UMR 5259 CNRS/INSA Lyon, 69100 Lyon, France
| | - Youssef Haikel
- Department of Biomaterials and Bioengineering, INSERM UMR_S, Strasbourg University, 67000 Strasbourg, France
- Department of Endodontics, Faculty of Dental Medicine, Strasbourg University, 67000 Strasbourg, France
- Pôle de Médecine et Chirurgie Bucco-Dentaire, Hôpital Civil, Hôpitaux Universitaire de Strasbourg, 67000 Strasbourg, France
| | - Davide Mancino
- Department of Biomaterials and Bioengineering, INSERM UMR_S, Strasbourg University, 67000 Strasbourg, France
- Department of Endodontics, Faculty of Dental Medicine, Strasbourg University, 67000 Strasbourg, France
- Pôle de Médecine et Chirurgie Bucco-Dentaire, Hôpital Civil, Hôpitaux Universitaire de Strasbourg, 67000 Strasbourg, France
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9
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Ashi T, Richert R, Mancino D, Jmal H, Alkhouri S, Addiego F, Kharouf N, Haïkel Y. Do the Mechanical Properties of Calcium-Silicate-Based Cements Influence the Stress Distribution of Different Retrograde Cavity Preparations? MATERIALS (BASEL, SWITZERLAND) 2023; 16:ma16083111. [PMID: 37109947 PMCID: PMC10145818 DOI: 10.3390/ma16083111] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/06/2023] [Accepted: 04/13/2023] [Indexed: 05/13/2023]
Abstract
The aim of the present study was to investigate the influence of the mechanical properties of three different calcium-silicate-based cements on the stress distribution of three different retrograde cavity preparations. Biodentine™ "BD", MTA Biorep "BR", and Well-Root™ PT "WR" were used. The compression strengths of ten cylindrical samples of each material were tested. The porosity of each cement was investigated by using micro-computed X-ray tomography. Finite element analysis (FEA) was used to simulate three retrograde conical cavity preparations with an apical diameter of 1 mm (Tip I), 1.4 mm (Tip II), and 1.8 mm (Tip III) after an apical 3 mm resection. BR demonstrated the lowest compression strength values (17.6 ± 5.5 MPa) and porosity percentages (0.57 ± 0.14%) compared to BD (80 ± 17 MPa-1.22 ± 0.31%) and WR (90 ± 22 MPa-1.93 ± 0.12%) (p < 0.05). FEA demonstrated that the larger cavity preparation demonstrated higher stress distribution in the root whereas stiffer cement demonstrated lower stress in the root but higher stress in the material. We can conclude that a respected root end preparation associated with cement with good stiffness could offer optimal endodontic microsurgery. Further studies are needed to define the adapted cavity diameter and cement stiffness in order to have optimal mechanical resistance with less stress distribution in the root.
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Affiliation(s)
- Tarek Ashi
- Department of Biomaterials and Bioengineering, INSERM UMR_S, Strasbourg University, 67000 Strasbourg, France; (T.A.); (D.M.)
| | - Raphaël Richert
- Hospices Civils de Lyon, PAM Odontologie, 69100 Lyon, France;
- Laboratoire de Mécanique des Contacts et Structures, UMR 5259 CNRS/INSA Lyon, 69100 Lyon, France
| | - Davide Mancino
- Department of Biomaterials and Bioengineering, INSERM UMR_S, Strasbourg University, 67000 Strasbourg, France; (T.A.); (D.M.)
- Department of Endodontics, Faculty of Dental Medicine, Strasbourg University, 67000 Strasbourg, France
- Pôle de Médecine et Chirurgie Bucco-Dentaire, Hôpital Civil, Hôpitaux Universitaire de Strasbourg, 67000 Strasbourg, France
| | - Hamdi Jmal
- ICube Laboratory, Mechanics Department, UMR 7357 CNRS, University of Strasbourg, 67000 Strasbourg, France;
| | | | - Frédéric Addiego
- Department Materials Research and Technology (MRT), Luxembourg Institute of Science and Technology (LIST), ZAE Robert Steichen, 5 Rue Bommel, L-4940 Hautcharage, Luxembourg;
| | - Naji Kharouf
- Department of Biomaterials and Bioengineering, INSERM UMR_S, Strasbourg University, 67000 Strasbourg, France; (T.A.); (D.M.)
- Department of Endodontics, Faculty of Dental Medicine, Strasbourg University, 67000 Strasbourg, France
- Correspondence: (N.K.); (Y.H.); Tel.: +33-(0)6-6752-2841 (N.K.)
| | - Youssef Haïkel
- Department of Biomaterials and Bioengineering, INSERM UMR_S, Strasbourg University, 67000 Strasbourg, France; (T.A.); (D.M.)
- Department of Endodontics, Faculty of Dental Medicine, Strasbourg University, 67000 Strasbourg, France
- Pôle de Médecine et Chirurgie Bucco-Dentaire, Hôpital Civil, Hôpitaux Universitaire de Strasbourg, 67000 Strasbourg, France
- Correspondence: (N.K.); (Y.H.); Tel.: +33-(0)6-6752-2841 (N.K.)
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10
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Limjeerajarus N, Sratong-on P, Dhammayannarangsi P, Tompkins KA, Kamolratanakul P, Phannarus K, Osathanon T, Limjeerajarus CN. Determination of the compressive modulus of elasticity of periodontal ligament derived from human first premolars. Heliyon 2023; 9:e14276. [PMID: 36938472 PMCID: PMC10018569 DOI: 10.1016/j.heliyon.2023.e14276] [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: 12/22/2022] [Revised: 01/23/2023] [Accepted: 03/01/2023] [Indexed: 03/06/2023] Open
Abstract
Purpose There are two commonly cited modulus of elasticity of the human periodontal ligament (EPDL), i.e., 6.89 ✕ 10-5 GPa (E1) and 6.89 ✕ 10-2 GPa (E2), which are exactly 1000-fold different from each other. This study aims to clarify the ambiguity of the two EPDL used for simulations and determine a more accurate EPDL value of human first premolars using experimental and simulation approaches. Methods Numerical simulations using finite element analysis were performed to analyze PDL deformation under an average Asian occlusal force. To confirm the results, simple and multi-component, true-scale 3D models of a human first premolar were used in the simulations. Finally, a compression test using a universal testing machine on PDL specimens was conducted to identify the compressive EPDL of human first premolars. Results The simulation results from both models revealed that E1 was inaccurate, because it resulted in excessive PDL deformation under the average occlusal force, which should not occur during mastication. Although the E2 did not lead to excessive PDL deformation, it was obtained by an error in unit conversion with no scientific backing. In contrast, the compression test results indicated that the compressive EPDL was 9.64 ✕ 10-4 GPa (E3). In the simulation, E3 did not cause excessive PDL deformation. Conclusion The simulation results demonstrated that both commonly cited EPDL values (E1 and E2) were incorrect. Based on the experimental and simulation results, the average compressive EPDL of 9.64 ✕ 10-4 GPa is proposed as a more accurate value for human first premolars. Clinical significance The proposed more accurate EPDL would contribute to more precise and reliable FEA simulation results and provide a better understanding of the stress distribution and deformation of dental materials, which will be beneficial to precision dentistry, orthodontics and restoration designs.
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Affiliation(s)
- Nuttapol Limjeerajarus
- Office of Research Affairs, Chulalongkorn University, Bangkok, 10330, Thailand
- Research Center for Advanced Energy Technology, Thai-Nichi Institute of Technology, Bangkok, 10250, Thailand
| | - Pimpet Sratong-on
- Research Center for Advanced Energy Technology, Thai-Nichi Institute of Technology, Bangkok, 10250, Thailand
| | | | - Kevin A. Tompkins
- Office of Research Affairs, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Paksinee Kamolratanakul
- Department of Oral and Maxillofacial Surgery, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Krisadi Phannarus
- Dental Department, Queen Sirikit National Institute of Child Health, Bangkok, 10400, Thailand
| | - Thanaphum Osathanon
- Dental Stem Cell Biology Research Unit and Department of Anatomy, Chulalongkorn University, Bangkok, 10330, Thailand
| | - Chalida Nakalekha Limjeerajarus
- Center of Excellence in Genomics and Precision Dentistry and Department of Physiology, Chulalongkorn University, Bangkok, 10330, Thailand
- Excellence Center in Regenerative Dentistry, Chulalongkorn University, Bangkok, Thailand
- Corresponding author. Department of Physiology, Faculty of Dentistry, Chulalongkorn University, Bangkok, 10330, Thailand.
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11
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Ducret M, Mörch CM, Karteva T, Fisher J, Schwendicke F. Artificial intelligence for sustainable oral healthcare. J Dent 2022; 127:104344. [PMID: 36273625 DOI: 10.1016/j.jdent.2022.104344] [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: 12/30/2021] [Revised: 08/24/2022] [Accepted: 10/19/2022] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVES Oral health is grounded in the United National (UN) 2030 Agenda for Sustainable Developement and its 17 Goals (SDGs), in particular SDG 3 (Ensure healthy lives and promote well-being for all at all ages). The World Health Organization (WHO) Global Strategy on Oral Health calls for prioritizing environmentally sustainable and less invasive oral health care, and planetary health. Artificial Intelligence (AI) has the potential to power the next generation of oral health services and care, however its relationship with the broader UN and WHO concepts of sustainability remains poorly defined and articulated. We review the double-edged relationships between AI and oral health, to suggest actions that promote a sustainable deployment of AI for oral health. DATA Concepts regarding AI, sustainability and sustainable development were identified and defined. A review of several double-edged relationship between AI and SDGs were exposed for the field of Oral Health. SOURCES Medline and international declarations of the WHO, the UN and the World Dental Federation (FDI) were screened. STUDY SELECTION One the one hand, AI may reduce transportation, optimize care delivery (SDG 3 "Good Health and Well-Being", SDG 13 "Climate Action"), and increase accessibility of services and reduce inequality (SDG 10 "Reduced Inequalities", SDG 4 "Quality Education"). On the other hand, the deployment, implementation and maintenance of AI require significant resources (SDG 12 "Responsible Consumption and Production"), and costs for AI may aggravate inequalities. Also, AI may be biased, reinforcing inequalities (SDG 10) and discrimination (SDG 5), and may violate principles of security, privacy and confidentiality of personal information (SDG 16). CONCLUSIONS Systematic assessment of the positive impact and adverse effects of AI on sustainable oral health may help to foster the former and curb the latter based on evidence. CLINICAL SIGNIFICANCE If sustainability imperatives are actively taken into consideration, the community of oral health professionals should then employ AI for improving effectiveness, efficiency, and safety of oral healthcare; strengthen oral health surveillance; foster education and accessibility of care; ensure fairness, transparency and governance of AI for oral health; develop legislation and infrastructure to expand the use of digital health technologies including AI.
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Affiliation(s)
- Maxime Ducret
- Institut de Biologie et Chimie des Protéines, Laboratoire de Biologie Tissulaire et Ingénierie Thérapeutique, UMR 5305 CNRS, Université Lyon 1, Lyon, France; Faculté d'Odontologie, Université Lyon 1, Lyon, France; Hospices Civils de Lyon, Centre de soins Dentaires, Lyon, France.
| | - Carl-Maria Mörch
- FARI - AI for the Common Good Institute, Free University of Brussels, Brussels, Belgium
| | - Teodora Karteva
- Department of Operative Dentistry and Endodontics, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Julian Fisher
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin, Berlin, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin, Berlin, Germany
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12
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Multifactorial Analysis of Endodontic Microsurgery Using Finite Element Models. J Pers Med 2022; 12:jpm12061012. [PMID: 35743798 PMCID: PMC9224708 DOI: 10.3390/jpm12061012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 06/17/2022] [Accepted: 06/18/2022] [Indexed: 12/02/2022] Open
Abstract
Background: The present study aimed to classify the relative contributions of four biomechanical factors—the root-end filling material, the apical preparation, the root resection length, and the bone height—on the root stresses of the resected premolar. Methods: A design of experiments approach based on a defined subset of factor combinations was conducted to calculate the influence of each factor and their interactions. Sixteen finite element models were created and analyzed using the von Mises stress criterion. The robustness of the design of experiments was evaluated with nine supplementary models. Results: The current study showed that the factors preparation and bone height had a high influence on root stresses. However, it also revealed that nearly half of the biomechanical impact was missed without considering interactions between factors, particularly between resection and preparation. Conclusions: Design of experiments appears to be a valuable strategy to classify the contributions of biomechanical factors related to endodontics. Imagining all possible interactions and their clinical impact is difficult and can require relying on one’s own experience. This study proposed a statistical method to quantify the mechanical risk when planning apicoectomy. A perspective could be to integrate the equation defined herein in future software to support decision-making.
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