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Xiang B, Lu J, Yu J. Evaluating tooth segmentation accuracy and time efficiency in CBCT images using artificial intelligence: A systematic review and Meta-analysis. J Dent 2024; 146:105064. [PMID: 38768854 DOI: 10.1016/j.jdent.2024.105064] [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: 11/10/2023] [Revised: 04/22/2024] [Accepted: 05/09/2024] [Indexed: 05/22/2024] Open
Abstract
OBJECTIVES This systematic review and meta-analysis aimed to assess the current performance of artificial intelligence (AI)-based methods for tooth segmentation in three-dimensional cone-beam computed tomography (CBCT) images, with a focus on their accuracy and efficiency compared to those of manual segmentation techniques. DATA The data analyzed in this review consisted of a wide range of research studies utilizing AI algorithms for tooth segmentation in CBCT images. Meta-analysis was performed, focusing on the evaluation of the segmentation results using the dice similarity coefficient (DSC). SOURCES PubMed, Embase, Scopus, Web of Science, and IEEE Explore were comprehensively searched to identify relevant studies. The initial search yielded 5642 entries, and subsequent screening and selection processes led to the inclusion of 35 studies in the systematic review. Among the various segmentation methods employed, convolutional neural networks, particularly the U-net model, are the most commonly utilized. The pooled effect of the DSC score for tooth segmentation was 0.95 (95 %CI 0.94 to 0.96). Furthermore, seven papers provided insights into the time required for segmentation, which ranged from 1.5 s to 3.4 min when utilizing AI techniques. CONCLUSIONS AI models demonstrated favorable accuracy in automatically segmenting teeth from CBCT images while reducing the time required for the process. Nevertheless, correction methods for metal artifacts and tooth structure segmentation using different imaging modalities should be addressed in future studies. CLINICAL SIGNIFICANCE AI algorithms have great potential for precise tooth measurements, orthodontic treatment planning, dental implant placement, and other dental procedures that require accurate tooth delineation. These advances have contributed to improved clinical outcomes and patient care in dental practice.
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Affiliation(s)
- Bilu Xiang
- School of Dentistry, Shenzhen University Medical School, Shenzhen University, Shenzhen 518000, China.
| | - Jiayi Lu
- Department of Stomatology, Shenzhen University General Hospital, Shenzhen University, Shenzhen 518000, China
| | - Jiayi Yu
- Department of Stomatology, Shenzhen University General Hospital, Shenzhen University, Shenzhen 518000, China
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Ji Y, Chen Y, Liu G, Long Z, Gao Y, Huang D, Zhang L. Construction and evaluation of an AI-based CBCT resolution optimization technique for extracted teeth. J Endod 2024:S0099-2399(24)00339-X. [PMID: 38848947 DOI: 10.1016/j.joen.2024.05.015] [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: 01/16/2024] [Revised: 04/01/2024] [Accepted: 05/23/2024] [Indexed: 06/09/2024]
Abstract
INTRODUCTION In dental clinical practice, cone-beam computed tomography (CBCT) is commonly used to assist practitioners to recognize the complex morphology of root canal systems. But due to its resolution limitations, certain small anatomical structures still cannot be accurately recognized on CBCT. The purpose of this study was to perform image super-resolution (SR) processing on CBCT images of extracted human teeth with the help of a deep learning model, and to compare the differences among CBCT, SRCT, and micro-computed tomography (Micro-CT) images through three-dimensional reconstruction. METHODS The deep learning model (Basicvsr++) was selected and modified. The dataset consisted of 171 extracted teeth that met inclusion criteria, with 40 maxillary first molars as training set and 40 maxillary first molars as well as 91 teeth from other tooth positions as external test set. The corresponding CBCT, SRCT, and Micro-CT images of each tooth in test sets were reconstructed using Mimics Research 17.0, and the root canal recognition rates in the three groups were recorded. The following parameters were measured: volume of hard tissue(V1), volume of pulp chamber and root canal system(V2), length of visible root canals under orifice(VL-X, where X represents the specific root canal), intersection angle between coronal axis of canal and long axis of tooth(∠X, where X represents the specific root canal). Data were statistically analyzed between CBCT and SRCT images using paired sample t-test and Wilcoxon test analysis, with the measurement from Micro-CT images as the gold standard. RESULTS Images from all tested teeth were successfully processed with super-resolution program. In 4-canal maxillary first molar, identification of MB2 was 72% (18/25) in CBCT group, 92% (23/25) in SRCT group, and 100% (25/25) in Micro-CT group. The difference of hard tissue volume between SRCT and Micro-CT was significantly smaller than that between CBCT and Micro-CT in all tested teeth except 4-canal mandibular first molar (P < 0.05). Similar results were obtained in volume of pulp chamber and root canal system in all tested teeth (P < 0.05). As for length of visible root canals under orifice, the difference between SRCT and Micro-CT was significantly smaller than that between CBCT and Micro-CT (P < 0.05) in most root canals. CONCLUSIONS The deep learning model developed in this study helps to optimize the root canal morphology of extracted teeth in CBCT. And it may be helpful for the identification of MB2 in the maxillary first molar.
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Affiliation(s)
- Yinfei Ji
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Operative Dentistry and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yunkai Chen
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Guanghui Liu
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Ziteng Long
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Operative Dentistry and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yuxuan Gao
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Operative Dentistry and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Dingming Huang
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Operative Dentistry and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China.
| | - Lan Zhang
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Operative Dentistry and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China.
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Revilla-León M, Gómez-Polo M, Sailer I, Kois JC, Rokhshad R. An overview of artificial intelligence based applications for assisting digital data acquisition and implant planning procedures. J ESTHET RESTOR DENT 2024. [PMID: 38757761 DOI: 10.1111/jerd.13249] [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: 03/22/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/18/2024]
Abstract
OBJECTIVES To provide an overview of the current artificial intelligence (AI) based applications for assisting digital data acquisition and implant planning procedures. OVERVIEW A review of the main AI-based applications integrated into digital data acquisitions technologies (facial scanners (FS), intraoral scanners (IOSs), cone beam computed tomography (CBCT) devices, and jaw trackers) and computer-aided static implant planning programs are provided. CONCLUSIONS The main AI-based application integrated in some FS's programs involves the automatic alignment of facial and intraoral scans for virtual patient integration. The AI-based applications integrated into IOSs programs include scan cleaning, assist scanning, and automatic alignment between the implant scan body with its corresponding CAD object while scanning. The more frequently AI-based applications integrated into the programs of CBCT units involve positioning assistant, noise and artifacts reduction, structures identification and segmentation, airway analysis, and alignment of facial, intraoral, and CBCT scans. Some computer-aided static implant planning programs include patient's digital files, identification, labeling, and segmentation of anatomical structures, mandibular nerve tracing, automatic implant placement, and surgical implant guide design.
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Affiliation(s)
- Marta Revilla-León
- Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Washington, USA
- Research and Digital Dentistry, Kois Center, Seattle, Washington, USA
- Department of Prosthodontics, School of Dental Medicine, Tufts University, Boston, Massachusetts, USA
| | - Miguel Gómez-Polo
- Department of Conservative Dentistry and Prosthodontics, Complutense University of Madrid, Madrid, Spain
- Advanced in Implant-Prosthodontics, School of Dentistry, Complutense University of Madrid, Madrid, Spain
| | - Irena Sailer
- Fixed Prosthodontics and Biomaterials, University Clinic of Dental Medicine, University of Geneva, Geneva, Switzerland
| | - John C Kois
- Kois Center, Seattle, Washington, USA
- Department of Restorative Dentistry, University of Washington, Seattle, Washington, USA
- Private Practice, Seattle, Washington, USA
| | - Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
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Elgarba BM, Fontenele RC, Tarce M, Jacobs R. Artificial intelligence serving pre-surgical digital implant planning: A scoping review. J Dent 2024; 143:104862. [PMID: 38336018 DOI: 10.1016/j.jdent.2024.104862] [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: 12/14/2023] [Revised: 01/22/2024] [Accepted: 01/24/2024] [Indexed: 02/12/2024] Open
Abstract
OBJECTIVES To conduct a scoping review focusing on artificial intelligence (AI) applications in presurgical dental implant planning. Additionally, to assess the automation degree of clinically available pre-surgical implant planning software. DATA AND SOURCES A systematic electronic literature search was performed in five databases (PubMed, Embase, Web of Science, Cochrane Library, and Scopus), along with exploring gray literature web-based resources until November 2023. English-language studies on AI-driven tools for digital implant planning were included based on an independent evaluation by two reviewers. An assessment of automation steps in dental implant planning software available on the market up to November 2023 was also performed. STUDY SELECTION AND RESULTS From an initial 1,732 studies, 47 met eligibility criteria. Within this subset, 39 studies focused on AI networks for anatomical landmark-based segmentation, creating virtual patients. Eight studies were dedicated to AI networks for virtual implant placement. Additionally, a total of 12 commonly available implant planning software applications were identified and assessed for their level of automation in pre-surgical digital implant workflows. Notably, only six of these featured at least one fully automated step in the planning software, with none possessing a fully automated implant planning protocol. CONCLUSIONS AI plays a crucial role in achieving accurate, time-efficient, and consistent segmentation of anatomical landmarks, serving the process of virtual patient creation. Additionally, currently available systems for virtual implant placement demonstrate different degrees of automation. It is important to highlight that, as of now, full automation of this process has not been documented nor scientifically validated. CLINICAL SIGNIFICANCE Scientific and clinical validation of AI applications for presurgical dental implant planning is currently scarce. The present review allows the clinician to identify AI-based automation in presurgical dental implant planning and assess the potential underlying scientific validation.
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Affiliation(s)
- Bahaaeldeen M Elgarba
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals, Campus Sint-Rafael, 3000 Leuven, Belgium & Department of Prosthodontics, Faculty of Dentistry, Tanta University, 31511 Tanta, Egypt.
| | - Rocharles Cavalcante Fontenele
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals, Campus Sint-Rafael, 3000 Leuven, Belgium
| | - Mihai Tarce
- Division of Periodontology & Implant Dentistry, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China & Periodontology and Oral Microbiology, Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals, Campus Sint-Rafael, 3000 Leuven, Belgium & Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden
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Cheung K, Cheung W, Liu Y, Ye H, Lv L, Zhou Y. Establishment of a 3D esthetic analysis workflow on 3D virtual patient and preliminary evaluation. BMC Oral Health 2024; 24:328. [PMID: 38475773 DOI: 10.1186/s12903-024-04085-0] [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: 08/31/2023] [Accepted: 02/28/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND In esthetic dentistry, a thorough esthetic analysis holds significant role in both diagnosing diseases and designing treatment plans. This study established a 3D esthetic analysis workflow based on 3D facial and dental models, and aimed to provide an imperative foundation for the artificial intelligent 3D analysis in future esthetic dentistry. METHODS The established 3D esthetic analysis workflow includes the following steps: 1) key point detection, 2) coordinate system redetermination and 3) esthetic parameter calculation. The accuracy and reproducibility of this established workflow were evaluated by a self-controlled experiment (n = 15) in which 2D esthetic analysis and direct measurement were taken as control. Measurement differences between 3D and 2D analysis were evaluated with paired t-tests. RESULTS 3D esthetic analysis demonstrated high consistency and reliability (0.973 < ICC < 1.000). Compared with 2D measurements, the results from 3D esthetic measurements were closer to direct measurements regarding tooth-related esthetic parameters (P<0.05). CONCLUSIONS The 3D esthetic analysis workflow established for 3D virtual patients demonstrated a high level of consistency and reliability, better than 2D measurements in the precision of tooth-related parameter analysis. These findings indicate a highly promising outlook for achieving an objective, precise, and efficient esthetic analysis in the future, which is expected to result in a more streamlined and user-friendly digital design process. This study was registered with the Ethics Committee of Peking University School of Stomatology in September 2021 with the registration number PKUSSIRB-202168136.
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Affiliation(s)
- Kwantong Cheung
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Disease & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, China
| | - Waisze Cheung
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Disease & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, China
| | - Yunsong Liu
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Disease & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, China
| | - Hongqiang Ye
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Disease & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, China
| | - Longwei Lv
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Disease & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, China.
| | - Yongsheng Zhou
- Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Disease & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, No. 22, Zhongguancun South Avenue, Haidian District, Beijing, 100081, China.
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Alam MK, Alftaikhah SAA, Issrani R, Ronsivalle V, Lo Giudice A, Cicciù M, Minervini G. Applications of artificial intelligence in the utilisation of imaging modalities in dentistry: A systematic review and meta-analysis of in-vitro studies. Heliyon 2024; 10:e24221. [PMID: 38317889 PMCID: PMC10838702 DOI: 10.1016/j.heliyon.2024.e24221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 01/02/2024] [Accepted: 01/04/2024] [Indexed: 02/07/2024] Open
Abstract
Background In the past, dentistry heavily relied on manual image analysis and diagnostic procedures, which could be time-consuming and prone to human error. The advent of artificial intelligence (AI) has brought transformative potential to the field, promising enhanced accuracy and efficiency in various dental imaging tasks. This systematic review and meta-analysis aimed to comprehensively evaluate the applications of AI in dental imaging modalities, focusing on in-vitro studies. Methods A systematic literature search was conducted, in accordance with the PRISMA guidelines. The following databases were systematically searched: PubMed/MEDLINE, Embase, Web of Science, Scopus, IEEE Xplore, Cochrane Library, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Google Scholar. The meta-analysis employed fixed-effects models to assess AI accuracy, calculating odds ratios (OR) for true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) with 95 % confidence intervals (CI). Heterogeneity and overall effect tests were applied to ensure the reliability of the findings. Results 9 studies were selected that encompassed various objectives, such as tooth segmentation and classification, caries detection, maxillofacial bone segmentation, and 3D surface model creation. AI techniques included convolutional neural networks (CNNs), deep learning algorithms, and AI-driven tools. Imaging parameters assessed in these studies were specific to the respective dental tasks. The analysis of combined ORs indicated higher odds of accurate dental image assessments, highlighting the potential for AI to improve TPR, TNR, PPV, and NPV. The studies collectively revealed a statistically significant overall effect in favor of AI in dental imaging applications. Conclusion In summary, this systematic review and meta-analysis underscore the transformative impact of AI on dental imaging. AI has the potential to revolutionize the field by enhancing accuracy, efficiency, and time savings in various dental tasks. While further research in clinical settings is needed to validate these findings and address study limitations, the future implications of integrating AI into dental practice hold great promise for advancing patient care and the field of dentistry.
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Affiliation(s)
- Mohammad Khursheed Alam
- Preventive Dentistry Department, College of Dentistry, Jouf University, Sakaka, 72345, Saudi Arabia
- Department of Dental Research Cell, Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College and Hospitals, Chennai, 600077, India
- Department of Public Health, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, 1207, Bangladesh
| | | | - Rakhi Issrani
- Preventive Dentistry Department, College of Dentistry, Jouf University, Sakaka, 72345, Saudi Arabia
| | - Vincenzo Ronsivalle
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, Italy
| | - Antonino Lo Giudice
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, Italy
| | - Marco Cicciù
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, Italy
| | - Giuseppe Minervini
- Multidisciplinary Department of Medical-Surgical and Odontostomatological Specialties, University of Campania “Luigi Vanvitelli”, 80121, Naples, Italy
- Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Science (SIMATS), Saveetha University, Chennai, Tamil Nadu, India
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Swaity A, Elgarba BM, Morgan N, Ali S, Shujaat S, Borsci E, Chilvarquer I, Jacobs R. Deep learning driven segmentation of maxillary impacted canine on cone beam computed tomography images. Sci Rep 2024; 14:369. [PMID: 38172136 PMCID: PMC10764895 DOI: 10.1038/s41598-023-49613-0] [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: 07/19/2023] [Accepted: 12/10/2023] [Indexed: 01/05/2024] Open
Abstract
The process of creating virtual models of dentomaxillofacial structures through three-dimensional segmentation is a crucial component of most digital dental workflows. This process is typically performed using manual or semi-automated approaches, which can be time-consuming and subject to observer bias. The aim of this study was to train and assess the performance of a convolutional neural network (CNN)-based online cloud platform for automated segmentation of maxillary impacted canine on CBCT image. A total of 100 CBCT images with maxillary canine impactions were randomly allocated into two groups: a training set (n = 50) and a testing set (n = 50). The training set was used to train the CNN model and the testing set was employed to evaluate the model performance. Both tasks were performed on an online cloud-based platform, 'Virtual patient creator' (Relu, Leuven, Belgium). The performance was assessed using voxel- and surface-based comparison between automated and semi-automated ground truth segmentations. In addition, the time required for segmentation was also calculated. The automated tool showed high performance for segmenting impacted canines with a dice similarity coefficient of 0.99 ± 0.02. Moreover, it was 24 times faster than semi-automated approach. The proposed CNN model achieved fast, consistent, and precise segmentation of maxillary impacted canines.
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Affiliation(s)
- Abdullah Swaity
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Prosthodontic Department, King Hussein Medical Center, Jordanian Royal Medical Services, Amman, Jordan
| | - Bahaaeldeen M Elgarba
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Department of Prosthodontics, Tanta University, Tanta, Egypt
| | - Nermin Morgan
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Department of Oral Medicine, Faculty of Dentistry, Mansoura University, Mansoura, Egypt
| | - Saleem Ali
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- Restorative Dentistry Department, King Hussein Medical Center, Jordanian Royal Medical Services, Amman, Jordan
| | - Sohaib Shujaat
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium
- King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia
| | - Elena Borsci
- Oral Diagnostic Clinic, Karolinska Institute, Stockholm, Sweden
| | - Israel Chilvarquer
- Department of Oral Radiology, School of Dentistry, University of São Paulo (USP), São Paulo, Brazil
| | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium.
- Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden.
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Yoo YS, Kim D, Yang S, Kang SR, Kim JE, Huh KH, Lee SS, Heo MS, Yi WJ. Comparison of 2D, 2.5D, and 3D segmentation networks for maxillary sinuses and lesions in CBCT images. BMC Oral Health 2023; 23:866. [PMID: 37964229 PMCID: PMC10647072 DOI: 10.1186/s12903-023-03607-6] [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: 06/29/2023] [Accepted: 11/01/2023] [Indexed: 11/16/2023] Open
Abstract
BACKGROUND The purpose of this study was to compare the segmentation performances of the 2D, 2.5D, and 3D networks for maxillary sinuses (MSs) and lesions inside the maxillary sinus (MSL) with variations in sizes, shapes, and locations in cone beam CT (CBCT) images under the same constraint of memory capacity. METHODS The 2D, 2.5D, and 3D networks were compared comprehensively for the segmentation of the MS and MSL in CBCT images under the same constraint of memory capacity. MSLs were obtained by subtracting the prediction of the air region of the maxillary sinus (MSA) from that of the MS. RESULTS The 2.5D network showed the highest segmentation performances for the MS and MSA compared to the 2D and 3D networks. The performances of the Jaccard coefficient, Dice similarity coefficient, precision, and recall by the 2.5D network of U-net + + reached 0.947, 0.973, 0.974, and 0.971 for the MS, respectively, and 0.787, 0.875, 0.897, and 0.858 for the MSL, respectively. CONCLUSIONS The 2.5D segmentation network demonstrated superior segmentation performance for various MSLs with an ensemble learning approach of combining the predictions from three orthogonal planes.
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Affiliation(s)
- Yeon-Sun Yoo
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, Korea
| | - DaEl Kim
- Interdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University, Seoul, Korea
| | - Su Yang
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
| | - Se-Ryong Kang
- Department of Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea
| | - Jo-Eun Kim
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, Korea
| | - Kyung-Hoe Huh
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, Korea
| | - Sam-Sun Lee
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, Korea
| | - Min-Suk Heo
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, Korea
| | - Won-Jin Yi
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, Korea.
- Interdisciplinary Program in Bioengineering, Graduate School of Engineering, Seoul National University, Seoul, Korea.
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea.
- Department of Biomedical Radiation Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea.
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Elgarba BM, Van Aelst S, Swaity A, Morgan N, Shujaat S, Jacobs R. Deep learning-based segmentation of dental implants on cone-beam computed tomography images: A validation study. J Dent 2023; 137:104639. [PMID: 37517787 DOI: 10.1016/j.jdent.2023.104639] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/21/2023] [Accepted: 07/26/2023] [Indexed: 08/01/2023] Open
Abstract
OBJECTIVES To train and validate a cloud-based convolutional neural network (CNN) model for automated segmentation (AS) of dental implant and attached prosthetic crown on cone-beam computed tomography (CBCT) images. METHODS A total dataset of 280 maxillomandibular jawbone CBCT scans was acquired from patients who underwent implant placement with or without coronal restoration. The dataset was randomly divided into three subsets: training set (n = 225), validation set (n = 25) and testing set (n = 30). A CNN model was developed and trained using expert-based semi-automated segmentation (SS) of the implant and attached prosthetic crown as the ground truth. The performance of AS was assessed by comparing with SS and manually corrected automated segmentation referred to as refined-automated segmentation (R-AS). Evaluation metrics included timing, voxel-wise comparison based on confusion matrix and 3D surface differences. RESULTS The average time required for AS was 60 times faster (<30 s) than the SS approach. The CNN model was highly effective in segmenting dental implants both with and without coronal restoration, achieving a high dice similarity coefficient score of 0.92±0.02 and 0.91±0.03, respectively. Moreover, the root mean square deviation values were also found to be low (implant only: 0.08±0.09 mm, implant+restoration: 0.11±0.07 mm) when compared with R-AS, implying high AI segmentation accuracy. CONCLUSIONS The proposed cloud-based deep learning tool demonstrated high performance and time-efficient segmentation of implants on CBCT images. CLINICAL SIGNIFICANCE AI-based segmentation of implants and prosthetic crowns can minimize the negative impact of artifacts and enhance the generalizability of creating dental virtual models. Furthermore, incorporating the suggested tool into existing CNN models specialized for segmenting anatomical structures can improve pre-surgical planning for implants and post-operative assessment of peri‑implant bone levels.
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Affiliation(s)
- Bahaaeldeen M Elgarba
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium, 3000 Leuven, Belgium; Department of Prosthodontics, Faculty of Dentistry, Tanta University, 31511 Tanta, Egypt
| | - Stijn Van Aelst
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium, 3000 Leuven, Belgium
| | - Abdullah Swaity
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium, 3000 Leuven, Belgium; Prosthodontic Department, King Hussein Medical Center, Royal Medical Services, Amman, Jordan
| | - Nermin Morgan
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium, 3000 Leuven, Belgium; Department of Oral Medicine, Faculty of Dentistry, Mansoura University, Mansoura, Egypt
| | - Sohaib Shujaat
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium, 3000 Leuven, Belgium; King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Kingdom of Saudi Arabia
| | - Reinhilde Jacobs
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium, 3000 Leuven, Belgium; Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden.
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Gardiyanoğlu E, Ünsal G, Akkaya N, Aksoy S, Orhan K. Automatic Segmentation of Teeth, Crown-Bridge Restorations, Dental Implants, Restorative Fillings, Dental Caries, Residual Roots, and Root Canal Fillings on Orthopantomographs: Convenience and Pitfalls. Diagnostics (Basel) 2023; 13:diagnostics13081487. [PMID: 37189586 DOI: 10.3390/diagnostics13081487] [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: 02/26/2023] [Accepted: 03/01/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND The aim of our study is to provide successful automatic segmentation of various objects on orthopantomographs (OPGs). METHODS 8138 OPGs obtained from the archives of the Department of Dentomaxillofacial Radiology were included. OPGs were converted into PNGs and transferred to the segmentation tool's database. All teeth, crown-bridge restorations, dental implants, composite-amalgam fillings, dental caries, residual roots, and root canal fillings were manually segmented by two experts with the manual drawing semantic segmentation technique. RESULTS The intra-class correlation coefficient (ICC) for both inter- and intra-observers for manual segmentation was excellent (ICC > 0.75). The intra-observer ICC was found to be 0.994, while the inter-observer reliability was 0.989. No significant difference was detected amongst observers (p = 0.947). The calculated DSC and accuracy values across all OPGs were 0.85 and 0.95 for the tooth segmentation, 0.88 and 0.99 for dental caries, 0.87 and 0.99 for dental restorations, 0.93 and 0.99 for crown-bridge restorations, 0.94 and 0.99 for dental implants, 0.78 and 0.99 for root canal fillings, and 0.78 and 0.99 for residual roots, respectively. CONCLUSIONS Thanks to faster and automated diagnoses on 2D as well as 3D dental images, dentists will have higher diagnosis rates in a shorter time even without excluding cases.
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Affiliation(s)
- Emel Gardiyanoğlu
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, 99138 Nicosia, Cyprus
| | - Gürkan Ünsal
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, 99138 Nicosia, Cyprus
- DESAM Institute, Near East University, 99138 Nicosia, Cyprus
| | - Nurullah Akkaya
- Department of Computer Engineering, Applied Artificial Intelligence Research Centre, Near East University, 99138 Nicosia, Cyprus
| | - Seçil Aksoy
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, 99138 Nicosia, Cyprus
| | - Kaan Orhan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, 06560 Ankara, Turkey
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