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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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Shi YJ, Li JP, Wang Y, Ma RH, Wang YL, Guo Y, Li G. Deep learning in the diagnosis for cystic lesions of the jaws: a review of recent progress. Dentomaxillofac Radiol 2024; 53:271-280. [PMID: 38814810 PMCID: PMC11211683 DOI: 10.1093/dmfr/twae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 05/06/2024] [Accepted: 05/09/2024] [Indexed: 06/01/2024] Open
Abstract
Cystic lesions of the gnathic bones present challenges in differential diagnosis. In recent years, artificial intelligence (AI) represented by deep learning (DL) has rapidly developed and emerged in the field of dental and maxillofacial radiology (DMFR). Dental radiography provides a rich resource for the study of diagnostic analysis methods for cystic lesions of the jaws and has attracted many researchers. The aim of the current study was to investigate the diagnostic performance of DL for cystic lesions of the jaws. Online searches were done on Google Scholar, PubMed, and IEEE Xplore databases, up to September 2023, with subsequent manual screening for confirmation. The initial search yielded 1862 titles, and 44 studies were ultimately included. All studies used DL methods or tools for the identification of a variable number of maxillofacial cysts. The performance of algorithms with different models varies. Although most of the reviewed studies demonstrated that DL methods have better discriminative performance than clinicians, further development is still needed before routine clinical implementation due to several challenges and limitations such as lack of model interpretability, multicentre data validation, etc. Considering the current limitations and challenges, future studies for the differential diagnosis of cystic lesions of the jaws should follow actual clinical diagnostic scenarios to coordinate study design and enhance the impact of AI in the diagnosis of oral and maxillofacial diseases.
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Affiliation(s)
- Yu-Jie Shi
- School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
| | - Ju-Peng Li
- School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
| | - Yue Wang
- School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
| | - Ruo-Han Ma
- Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, Beijing, 100081, China
| | - Yan-Lin Wang
- Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, Beijing, 100081, China
| | - Yong Guo
- School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
| | - Gang Li
- Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, Beijing, 100081, China
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Socol Y, Richardson A, Garali-Zineddine I, Grison S, Vares G, Klokov D. Artificial intelligence in biology and medicine, and radioprotection research: perspectives from Jerusalem. Front Artif Intell 2024; 6:1291136. [PMID: 38282906 PMCID: PMC10812117 DOI: 10.3389/frai.2023.1291136] [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: 09/08/2023] [Accepted: 12/15/2023] [Indexed: 01/30/2024] Open
Abstract
While AI is widely used in biomedical research and medical practice, its use is constrained to few specific practical areas, e.g., radiomics. Participants of the workshop on "Artificial Intelligence in Biology and Medicine" (Jerusalem, Feb 14-15, 2023), both researchers and practitioners, aimed to build a holistic picture by exploring AI advancements, challenges and perspectives, as well as to suggest new fields for AI applications. Presentations showcased the potential of large language models (LLMs) in generating molecular structures, predicting protein-ligand interactions, and promoting democratization of AI development. Ethical concerns in medical decision making were also addressed. In biological applications, AI integration of multi-omics and clinical data elucidated the health relevant effects of low doses of ionizing radiation. Bayesian latent modeling identified statistical associations between unobserved variables. Medical applications highlighted liquid biopsy methods for non-invasive diagnostics, routine laboratory tests to identify overlooked illnesses, and AI's role in oral and maxillofacial imaging. Explainable AI and diverse image processing tools improved diagnostics, while text classification detected anorexic behavior in blog posts. The workshop fostered knowledge sharing, discussions, and emphasized the need for further AI development in radioprotection research in support of emerging public health issues. The organizers plan to continue the initiative as an annual event, promoting collaboration and addressing issues and perspectives in AI applications with a focus on low-dose radioprotection research. Researchers involved in radioprotection research and experts in relevant public policy domains are invited to explore the utility of AI in low-dose radiation research at the next workshop.
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Affiliation(s)
- Yehoshua Socol
- Department of Electrical and Electronics Engineering, Jerusalem College of Technology, Jerusalem, Israel
| | - Ariella Richardson
- Department of Data Mining, Jerusalem College of Technology, Jerusalem, Israel
| | - Imene Garali-Zineddine
- Health and Environnent Division, Institut de Radioprotection et de Sûreté Nucléaire (IRSN), Fontenay-aux-Roses, France
| | - Stephane Grison
- Health and Environnent Division, Institut de Radioprotection et de Sûreté Nucléaire (IRSN), Fontenay-aux-Roses, France
| | - Guillaume Vares
- Health and Environnent Division, Institut de Radioprotection et de Sûreté Nucléaire (IRSN), Fontenay-aux-Roses, France
| | - Dmitry Klokov
- Health and Environnent Division, Institut de Radioprotection et de Sûreté Nucléaire (IRSN), Fontenay-aux-Roses, France
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, ON, Canada
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Ghamri M, Dritsas K, Probst J, Jäggi M, Psomiadis S, Schulze R, Verna C, Katsaros C, Halazonetis D, Gkantidis N. Accuracy of facial skeletal surfaces segmented from CT and CBCT radiographs. Sci Rep 2023; 13:21002. [PMID: 38017262 PMCID: PMC10684569 DOI: 10.1038/s41598-023-48320-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 11/24/2023] [Indexed: 11/30/2023] Open
Abstract
The accuracy of three-dimensional (3D) facial skeletal surface models derived from radiographic volumes has not been extensively investigated yet. For this, ten human dry skulls were scanned with two Cone Beam Computed Tomography (CBCT) units, a CT unit, and a highly accurate optical surface scanner that provided the true reference models. Water-filled head shells were used for soft tissue simulation during radiographic imaging. The 3D surface models that were repeatedly segmented from the radiographic volumes through a single-threshold approach were used for reproducibility testing. Additionally, they were compared to the true reference model for trueness measurement. Comparisons were performed through 3D surface approximation techniques, using an iterative closest point algorithm. Differences between surface models were assessed through the calculation of mean absolute distances (MAD) between corresponding surfaces and through visual inspection of facial surface colour-coded distance maps. There was very high reproducibility (approximately 0.07 mm) and trueness (0.12 mm on average, with deviations extending locally to 0.5 mm), and no difference between radiographic scanners or settings. The present findings establish the validity of lower radiation CBCT imaging protocols at a similar level to the conventional CT images, when 3D surface models are required for the assessment of facial morphology.
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Affiliation(s)
- Mohammed Ghamri
- Department of Orthodontics and Dentofacial Orthopedics, School of Dental Medicine, University of Bern, 3010, Bern, Switzerland
- Jeddah Second Health Cluster, Ministry of Health, Riyadh, Saudi Arabia
| | - Konstantinos Dritsas
- Department of Orthodontics and Dentofacial Orthopedics, School of Dental Medicine, University of Bern, 3010, Bern, Switzerland
| | - Jannis Probst
- Department of Orthodontics and Dentofacial Orthopedics, School of Dental Medicine, University of Bern, 3010, Bern, Switzerland
| | - Maurus Jäggi
- Department of Orthodontics and Dentofacial Orthopedics, School of Dental Medicine, University of Bern, 3010, Bern, Switzerland
| | - Symeon Psomiadis
- Department of Oral and Maxillofacial Surgery, School of Dentistry, National and Kapodistrian University of Athens, 11527, Athens, Greece
| | - Ralf Schulze
- Division of Oral Diagnostic Sciences, Department of Oral Surgery and Stomatology, School of Dental Medicine, University of Bern, 3010, Bern, Switzerland
| | - Carlalberta Verna
- Department of Pediatric Oral Health and Orthodontics, UZB-University Center for Dental Medicine, University of Basel, 4058, Basel, Switzerland
| | - Christos Katsaros
- Department of Orthodontics and Dentofacial Orthopedics, School of Dental Medicine, University of Bern, 3010, Bern, Switzerland
| | - Demetrios Halazonetis
- Department of Orthodontics, School of Dentistry, National and Kapodistrian University of Athens, 11527, Athens, Greece
| | - Nikolaos Gkantidis
- Department of Orthodontics and Dentofacial Orthopedics, School of Dental Medicine, University of Bern, 3010, Bern, Switzerland.
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