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Șalgău CA, Morar A, Zgarta AD, Ancuța DL, Rădulescu A, Mitrea IL, Tănase AO. Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review. Ann Biomed Eng 2024:10.1007/s10439-024-03559-0. [PMID: 38884831 DOI: 10.1007/s10439-024-03559-0] [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: 04/03/2024] [Accepted: 06/05/2024] [Indexed: 06/18/2024]
Abstract
Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes: periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.
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Affiliation(s)
- Cristiana Adina Șalgău
- University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
| | - Anca Morar
- National University of Science and Technology Politehnica Bucharest, Bucharest, Romania.
| | | | - Diana-Larisa Ancuța
- University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
- Cantacuzino National Medical-Military Institute for Research and Development, Bucharest, Romania
| | - Alexandros Rădulescu
- University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
| | - Ioan Liviu Mitrea
- University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
| | - Andrei Ovidiu Tănase
- University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
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Al-Asali M, Alqutaibi AY, Al-Sarem M, Saeed F. Deep learning-based approach for 3D bone segmentation and prediction of missing tooth region for dental implant planning. Sci Rep 2024; 14:13888. [PMID: 38880802 PMCID: PMC11180661 DOI: 10.1038/s41598-024-64609-0] [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: 09/27/2023] [Accepted: 06/11/2024] [Indexed: 06/18/2024] Open
Abstract
Recent studies have shown that dental implants have high long-term survival rates, indicating their effectiveness compared to other treatments. However, there is still a concern regarding treatment failure. Deep learning methods, specifically U-Net models, have been effectively applied to analyze medical and dental images. This study aims to utilize U-Net models to segment bone in regions where teeth are missing in cone-beam computerized tomography (CBCT) scans and predict the positions of implants. The proposed models were applied to a CBCT dataset of Taibah University Dental Hospital (TUDH) patients between 2018 and 2023. They were evaluated using different performance metrics and validated by a domain expert. The experimental results demonstrated outstanding performance in terms of dice, precision, and recall for bone segmentation (0.93, 0.94, and 0.93, respectively) with a low volume error (0.01). The proposed models offer promising automated dental implant planning for dental implantologists.
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Affiliation(s)
- Mohammed Al-Asali
- College of Computer Science and Engineering, Taibah University, 42353, Medina, Saudi Arabia
| | - Ahmed Yaseen Alqutaibi
- Substitutive Dental Sciences Department (Prosthodontics), College of Dentistry, Taibah University, 41311, Al Madinah, Saudi Arabia.
- Department of Prosthodontics, College of Dentistry, Ibb University, 70270, Ibb, Yemen.
| | - Mohammed Al-Sarem
- College of Computer Science and Engineering, Taibah University, 42353, Medina, Saudi Arabia
- Department of Computer Science, Sheba Region University, Marib, Yemen
| | - Faisal Saeed
- College of Computing and Digital Technology, Birmingham City University, Birmingham, B4 7XG, UK.
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Xu Q, Zhou LL, Xing C, Xu X, Feng Y, Lv H, Zhao F, Chen YC, Cai Y. Tinnitus classification based on resting-state functional connectivity using a convolutional neural network architecture. Neuroimage 2024; 290:120566. [PMID: 38467345 DOI: 10.1016/j.neuroimage.2024.120566] [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: 10/05/2023] [Revised: 03/02/2024] [Accepted: 03/04/2024] [Indexed: 03/13/2024] Open
Abstract
OBJECTIVES Many studies have investigated aberrant functional connectivity (FC) using resting-state functional MRI (rs-fMRI) in subjective tinnitus patients. However, no studies have verified the efficacy of resting-state FC as a diagnostic imaging marker. We established a convolutional neural network (CNN) model based on rs-fMRI FC to distinguish tinnitus patients from healthy controls, providing guidance and fast diagnostic tools for the clinical diagnosis of subjective tinnitus. METHODS A CNN architecture was trained on rs-fMRI data from 100 tinnitus patients and 100 healthy controls using an asymmetric convolutional layer. Additionally, a traditional machine learning model and a transfer learning model were included for comparison with the CNN, and each of the three models was tested on three different brain atlases. RESULTS Of the three models, the CNN model outperformed the other two models with the highest area under the curve, especially on the Dos_160 atlas (AUC = 0.944). Meanwhile, the model with the best classification performance highlights the crucial role of the default mode network, salience network, and sensorimotor network in distinguishing between normal controls and patients with subjective tinnitus. CONCLUSION Our CNN model could appropriately tackle the diagnosis of tinnitus patients using rs-fMRI and confirmed the diagnostic value of FC as measured by rs-fMRI.
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Affiliation(s)
- Qianhui Xu
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou, Guangdong Province 510120, China
| | - Lei-Lei Zhou
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Chunhua Xing
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Xiaomin Xu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Yuan Feng
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Fei Zhao
- Department of Speech and Language Therapy and Hearing Science, Cardiff Metropolitan University, Cardiff, UK
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing 210006, China.
| | - Yuexin Cai
- Department of Otolaryngology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 West Yanjiang Road, Guangzhou, Guangdong Province 510120, China.
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Ibraheem WI. Accuracy of Artificial Intelligence Models in Dental Implant Fixture Identification and Classification from Radiographs: A Systematic Review. Diagnostics (Basel) 2024; 14:806. [PMID: 38667452 PMCID: PMC11049199 DOI: 10.3390/diagnostics14080806] [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: 02/24/2024] [Revised: 04/04/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024] Open
Abstract
Background and Objectives: The availability of multiple dental implant systems makes it difficult for the treating dentist to identify and classify the implant in case of inaccessibility or loss of previous records. Artificial intelligence (AI) is reported to have a high success rate in medical image classification and is effectively used in this area. Studies have reported improved implant classification and identification accuracy when AI is used with trained dental professionals. This systematic review aims to analyze various studies discussing the accuracy of AI tools in implant identification and classification. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the study was registered with the International Prospective Register of Systematic Reviews (PROSPERO). The focused PICO question for the current study was "What is the accuracy (outcome) of artificial intelligence tools (Intervention) in detecting and/or classifying the type of dental implant (Participant/population) using X-ray images?" Web of Science, Scopus, MEDLINE-PubMed, and Cochrane were searched systematically to collect the relevant published literature. The search strings were based on the formulated PICO question. The article search was conducted in January 2024 using the Boolean operators and truncation. The search was limited to articles published in English in the last 15 years (January 2008 to December 2023). The quality of all the selected articles was critically analyzed using the Quality Assessment and Diagnostic Accuracy Tool (QUADAS-2). Results: Twenty-one articles were selected for qualitative analysis based on predetermined selection criteria. Study characteristics were tabulated in a self-designed table. Out of the 21 studies evaluated, 14 were found to be at risk of bias, with high or unclear risk in one or more domains. The remaining seven studies, however, had a low risk of bias. The overall accuracy of AI models in implant detection and identification ranged from a low of 67% to as high as 98.5%. Most included studies reported mean accuracy levels above 90%. Conclusions: The articles in the present review provide considerable evidence to validate that AI tools have high accuracy in identifying and classifying dental implant systems using 2-dimensional X-ray images. These outcomes are vital for clinical diagnosis and treatment planning by trained dental professionals to enhance patient treatment outcomes.
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Affiliation(s)
- Wael I Ibraheem
- Department of Preventive Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia
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Lubbad MAH, Kurtulus IL, Karaboga D, Kilic K, Basturk A, Akay B, Nalbantoglu OU, Yilmaz OMD, Ayata M, Yilmaz S, Pacal I. A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01086-x. [PMID: 38565730 DOI: 10.1007/s10278-024-01086-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 02/28/2024] [Accepted: 03/12/2024] [Indexed: 04/04/2024]
Abstract
This study aims to provide an effective solution for the autonomous identification of dental implant brands through a deep learning-based computer diagnostic system. It also seeks to ascertain the system's potential in clinical practices and to offer a strategic framework for improving diagnosis and treatment processes in implantology. This study employed a total of 28 different deep learning models, including 18 convolutional neural network (CNN) models (VGG, ResNet, DenseNet, EfficientNet, RegNet, ConvNeXt) and 10 vision transformer models (Swin and Vision Transformer). The dataset comprises 1258 panoramic radiographs from patients who received implant treatments at Erciyes University Faculty of Dentistry between 2012 and 2023. It is utilized for the training and evaluation process of deep learning models and consists of prototypes from six different implant systems provided by six manufacturers. The deep learning-based dental implant system provided high classification accuracy for different dental implant brands using deep learning models. Furthermore, among all the architectures evaluated, the small model of the ConvNeXt architecture achieved an impressive accuracy rate of 94.2%, demonstrating a high level of classification success.This study emphasizes the effectiveness of deep learning-based systems in achieving high classification accuracy in dental implant types. These findings pave the way for integrating advanced deep learning tools into clinical practice, promising significant improvements in patient care and treatment outcomes.
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Affiliation(s)
- Mohammed A H Lubbad
- Department of Computer Engineering, Engineering Faculty, Erciyes University, 38039, Kayseri, Turkey.
- Artificial Intelligence and Big Data Application and Research Center, Erciyes University, Kayseri, Turkey.
| | | | - Dervis Karaboga
- Department of Computer Engineering, Engineering Faculty, Erciyes University, 38039, Kayseri, Turkey
- Artificial Intelligence and Big Data Application and Research Center, Erciyes University, Kayseri, Turkey
| | - Kerem Kilic
- Department of Prosthodontics, Dentistry Faculty, Erciyes University, Kayseri, Turkey
| | - Alper Basturk
- Department of Computer Engineering, Engineering Faculty, Erciyes University, 38039, Kayseri, Turkey
- Artificial Intelligence and Big Data Application and Research Center, Erciyes University, Kayseri, Turkey
| | - Bahriye Akay
- Department of Computer Engineering, Engineering Faculty, Erciyes University, 38039, Kayseri, Turkey
- Artificial Intelligence and Big Data Application and Research Center, Erciyes University, Kayseri, Turkey
| | - Ozkan Ufuk Nalbantoglu
- Department of Computer Engineering, Engineering Faculty, Erciyes University, 38039, Kayseri, Turkey
- Artificial Intelligence and Big Data Application and Research Center, Erciyes University, Kayseri, Turkey
| | | | - Mustafa Ayata
- Department of Prosthodontics, Dentistry Faculty, Erciyes University, Kayseri, Turkey
| | - Serkan Yilmaz
- Department of Dentomaxillofacial Radiology, Dentistry Faculty, Erciyes University, Kayseri, Turkey
| | - Ishak Pacal
- Department of Computer Engineering, Engineering Faculty, Igdir University, Igdir, Turkey
- Artificial Intelligence and Big Data Application and Research Center, Erciyes University, Kayseri, Turkey
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Leblebicioglu Kurtulus I, Lubbad M, Yilmaz OMD, Kilic K, Karaboga D, Basturk A, Akay B, Nalbantoglu U, Yilmaz S, Ayata M, Pacal I. A robust deep learning model for the classification of dental implant brands. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2024:101818. [PMID: 38462066 DOI: 10.1016/j.jormas.2024.101818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 02/26/2024] [Accepted: 03/07/2024] [Indexed: 03/12/2024]
Abstract
OBJECTIVE In cases where the brands of implants are not known, treatment options can be significantly limited in potential complications arising from implant procedures. This research aims to explore the application of deep learning techniques for the classification of dental implant systems using panoramic radiographs. The primary objective is to assess the superiority of the proposed model in achieving accurate and efficient dental implant classification. MATERIAL AND METHODS A comprehensive analysis was conducted using a diverse set of 25 convolutional neural network (CNN) models, including popular architectures such as VGG16, ResNet-50, EfficientNet, and ConvNeXt. The dataset of 1258 panoramic radiographs from patients who underwent implant treatment at faculty of dentistry was utilized for training and evaluation. Six different dental implant systems were employed as prototypes for the classification task. The precision, recall, F1 score, and support scores for each class have included in the classification accuracy report to ensure accurate and reliable results from the model. RESULTS The experimental results demonstrate that the proposed model consistently outperformed the other evaluated CNN architectures in terms of accuracy, precision, recall, and F1-score. With an impressive accuracy of 95.74 % and high precision and recall rates, the ConvNeXt model showcased its superiority in accurately classifying dental implant systems. Notably, the model's performance was achieved with a relatively smaller number of parameters, indicating its efficiency and speed during inference. CONCLUSION The findings highlight the effectiveness of deep learning techniques, particularly the proposed model, in accurately classifying dental implant systems from panoramic radiographs.
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Affiliation(s)
| | - Mohammed Lubbad
- Department of Computer Engineering, Faculty of Engineering, Erciyes University, Kayseri, Turkey
| | | | - Kerem Kilic
- Department of Prosthodontics, Faculty of Dentistry, Erciyes University, Kayseri, Turkey
| | - Dervis Karaboga
- Department of Computer Engineering, Faculty of Engineering, Erciyes University, Kayseri, Turkey
| | - Alper Basturk
- Department of Computer Engineering, Faculty of Engineering, Erciyes University, Kayseri, Turkey
| | - Bahriye Akay
- Department of Computer Engineering, Faculty of Engineering, Erciyes University, Kayseri, Turkey
| | - Ufuk Nalbantoglu
- Department of Computer Engineering, Faculty of Engineering, Erciyes University, Kayseri, Turkey
| | - Serkan Yilmaz
- Department of Dentomaxillofacial Radiology, Ministry of Health, Mersin Oral and Dental Health Hospital, Mersin, Turkey
| | - Mustafa Ayata
- Dentos Oral and Dental Health Polyclinic, Kayseri, Turkey
| | - Ishak Pacal
- Department of Computer Engineering, Faculty of Engineering, Igdir University, Igdir, Turkey
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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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Chaurasia A, Namachivayam A, Koca-Ünsal RB, Lee JH. Deep-learning performance in identifying and classifying dental implant systems from dental imaging: a systematic review and meta-analysis. J Periodontal Implant Sci 2024; 54:3-12. [PMID: 37154107 PMCID: PMC10901682 DOI: 10.5051/jpis.2300160008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 02/10/2023] [Accepted: 02/21/2023] [Indexed: 05/10/2023] Open
Abstract
Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%-75.9%) and no higher than 98.19 (95% CI, 97.8%-98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%-93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.
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Affiliation(s)
- Akhilanand Chaurasia
- Department of Oral Medicine & Radiology, King George's Medical University, Lucknow, Uttar Pradesh, India
| | - Arunkumar Namachivayam
- Department of Biostatistics, Bapuji Dental College & Hospital, Davengere, Karnataka, India
| | - Revan Birke Koca-Ünsal
- Department of Periodontology, Faculty of Dentistry, University of Kyrenia, Kyrenia, Cyprus
| | - Jae-Hong Lee
- Department of Periodontology, College of Dentistry and Institute of Oral Bioscience, Jeonbuk National University, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea.
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Dashti M, Londono J, Ghasemi S, Tabatabaei S, Hashemi S, Baghaei K, Palma PJ, Khurshid Z. Evaluation of accuracy of deep learning and conventional neural network algorithms in detection of dental implant type using intraoral radiographic images: A systematic review and meta-analysis. J Prosthet Dent 2024:S0022-3913(23)00812-0. [PMID: 38176985 DOI: 10.1016/j.prosdent.2023.11.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 11/23/2023] [Accepted: 11/28/2023] [Indexed: 01/06/2024]
Abstract
STATEMENT OF PROBLEM With the growing importance of implant brand detection in clinical practice, the accuracy of machine learning algorithms in implant brand detection has become a subject of research interest. Recent studies have shown promising results for the use of machine learning in implant brand detection. However, despite these promising findings, a comprehensive evaluation of the accuracy of machine learning in implant brand detection is needed. PURPOSE The purpose of this systematic review and meta-analysis was to assess the accuracy, sensitivity, and specificity of deep learning algorithms in implant brand detection using 2-dimensional images such as from periapical or panoramic radiographs. MATERIAL AND METHODS Electronic searches were conducted in PubMed, Embase, Scopus, Scopus Secondary, and Web of Science databases. Studies that met the inclusion criteria were assessed for quality using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Meta-analyses were performed using the random-effects model to estimate the pooled performance measures and the 95% confidence intervals (CIs) using STATA v.17. RESULTS Thirteen studies were selected for the systematic review, and 3 were used in the meta-analysis. The meta-analysis of the studies found that the overall accuracy of CNN algorithms in detecting dental implants in radiographic images was 95.63%, with a sensitivity of 94.55% and a specificity of 97.91%. The highest reported accuracy was 99.08% for CNN Multitask ResNet152 algorithm, and sensitivity and specificity were 100.00% and 98.70% respectively for the deep CNN (Neuro-T version 2.0.1) algorithm with the Straumann SLActive BLT implant brand. All studies had a low risk of bias. CONCLUSIONS The highest accuracy and sensitivity were reported in studies using CNN Multitask ResNet152 and deep CNN (Neuro-T version 2.0.1) algorithms.
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Affiliation(s)
- Mahmood Dashti
- Researcher, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Jimmy Londono
- Professor and Director of the Prosthodontics Residency Program and the Ronald Goldstein Center for Esthetics and Implant Dentistry, The Dental College of Georgia at Augusta University, Augusta, GA
| | - Shohreh Ghasemi
- Graduate Student, MSc of Trauma and Craniofacial Reconstrution, Faculty of Medicine and Dentistry, Queen Mary College, London, England
| | | | - Sara Hashemi
- Graduate student, Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Quebec, Canada
| | - Kimia Baghaei
- Researcher, Dental Students' Research Committee, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Paulo J Palma
- Researcher, Center for Innovation and Research in Oral Sciences (CIROS), Faculty of Medicine, University of Coimbra, Coimbra, Portugal; and Professor, Institute of Endodontics, Faculty of Medicine, University of Coimbra, Coimbra, Portugal.
| | - Zohaib Khurshid
- Lecturer, Prosthodontics, Department of Prosthodontics and Dental Implantology, King Faisal University, Al-Ahsa, Saudi Arabia; and Professor, Center of Excellence for Regenerative Dentistry, Department of Anatomy, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand
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Hartoonian S, Hosseini M, Yousefi I, Mahdian M, Ghazizadeh Ahsaie M. Applications of artificial intelligence in dentomaxillofacial imaging-a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol 2024:S2212-4403(23)01566-3. [PMID: 38637235 DOI: 10.1016/j.oooo.2023.12.790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/02/2023] [Accepted: 12/22/2023] [Indexed: 04/20/2024]
Abstract
BACKGROUND Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities. STUDY DESIGN A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another. RESULTS The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection. CONCLUSION Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.
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Affiliation(s)
- Serlie Hartoonian
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Matine Hosseini
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Iman Yousefi
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mina Mahdian
- Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Mitra Ghazizadeh Ahsaie
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Alqutaibi AY, Algabri RS, Elawady D, Ibrahim WI. Advancements in artificial intelligence algorithms for dental implant identification: A systematic review with meta-analysis. J Prosthet Dent 2023:S0022-3913(23)00783-7. [PMID: 38158266 DOI: 10.1016/j.prosdent.2023.11.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 01/03/2024]
Abstract
STATEMENT OF PROBLEM The evidence regarding the application of artificial intelligence (AI) in identifying dental implant systems is currently inconclusive. The available studies present varying results and methodologies, making it difficult to draw definitive conclusions. PURPOSE The purpose of this systematic review with meta-analysis was to comprehensively analyze and evaluate articles that investigate the application of AI in identifying and classifying dental implant systems. MATERIAL AND METHODS An electronic systematic review was conducted across 3 databases: MEDLINE/PubMed, Cochrane, and Scopus. Additionally, a manual search was performed. The inclusion criteria consisted of peer-reviewed studies investigating the accuracy of AI-based diagnostic tools on dental radiographs for identifying and classifying dental implant systems and comparing the results with those obtained by expert judges using manual techniques-the search strategy encompassed articles published until September 2023. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the quality of included articles. RESULTS Twenty-two eligible articles were included in this review. These articles described the use of AI in detecting dental implants through conventional radiographs. The pooled data showed that dental implant identification had an overall accuracy of 92.56% (range 90.49% to 94.63%). Eleven studies showed a low risk of bias, 6 demonstrated some concern risk, and 5 showed a high risk of bias. CONCLUSIONS AI models using panoramic and periapical radiographs can accurately identify and categorize dental implant systems. However, additional well-conducted research is recommended to identify the most common implant systems.
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Affiliation(s)
- Ahmed Yaseen Alqutaibi
- Associate Professor, Department of Prosthodontics and Implant Dentistry, College of Dentistry, Taibah University, Al Madinah, Saudi Arabia; and Associate Professor, Department of Prosthodontics, College of Dentistry, Ibb University, Ibb, Yemen.
| | - Radhwan S Algabri
- Assistant professor, Department of Prosthodontics, Faculty of Dentistry, Ibb University, Ibb, Yemen; and Assistant professor, Department of Prosthodontics, Faculty of Dentistry, National University, Ibb, Yemen
| | - Dina Elawady
- Associate Professor, Department of Prosthodontics, Faculty of Dentistry, MSA University, 6th of October City, Egypt
| | - Wafaa Ibrahim Ibrahim
- Associate Professor, Department of Prosthodontics, Faculty of Oral and Dental Medicine, Delta University for Science and Technology, Mansoura, Egypt
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Huang Z, Shi J, Gao G, Shi M, Gong Z, Liu H, Zeng P, Chen S, Gan X, Ding J, Wang Y, Chen Z. Quantification of the apical palatal bone index for maxillary incisor immediate implant assessment: A retrospective cross-sectional study. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2023; 124:101634. [PMID: 37709143 DOI: 10.1016/j.jormas.2023.101634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 07/26/2023] [Accepted: 09/11/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Apical palatal bone is important in immediate implant evaluation. Current consensus gives qualitative suggestions regarding it, limiting its clinical decision-making value. OBJECTIVES To quantify the apical palatal bone dimension in maxillary incisors and reveal its quantitative correlation with other implant-related hard tissue indices to give practical advice for pre-immediate implant evaluation and design. MATERIAL AND METHODS A retrospective analysis of immediate implant-related hard tissue indices in maxillary incisors obtained by cone beam computed tomography (CBCT) was conducted. Palatal bone thickness at the apex level (Apical-P) on the sagittal section was selected as a parameter reflecting the apical palatal bone. Its quantitative correlation with other immediate implant-related hard tissue indices was revealed. Clinical advice of pre-immediate implant assessment was given based on the quantitative classification of Apical-P and its other correlated immediate implant-related hard tissue indices. RESULTS Apical-P positively correlated with cervical palatal bone, whole cervical buccal-palatal bone, sagittal root angle, and basal bone width indices. while negatively correlated with apical buccal bone, cervical buccal bone, and basal bone length indices. Six quantitative categories of Apical-P are proposed. Cases with Apical-P below 4 mm had an insufficient apical bone thickness to accommodate the implant placement, while Apical-P beyond 12 mm should be cautious about the severe implant inclination. Cases with Apical-P of 4-12 mm can generally achieve satisfying immediate implant outcomes via regulating the implant inclination. CONCLUSIONS Quantification of the apical palatal bone index for maxillary incisor immediate implant assessment can be achieved, providing a quantitative guide for immediate implant placement in the maxillary incisor zone.
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Affiliation(s)
- Zhuwei Huang
- Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, 510055, China
| | - Jiamin Shi
- Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, 510055, China
| | - Guangqi Gao
- Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, 510055, China
| | - Mengru Shi
- Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, 510055, China
| | - Zhuohong Gong
- Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, 510055, China
| | - Haiwen Liu
- Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, 510055, China
| | - Peisheng Zeng
- Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, 510055, China
| | - Shijie Chen
- Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, 510055, China
| | - Xuejing Gan
- Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, 510055, China
| | - Jianfeng Ding
- Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, 510055, China
| | - Yan Wang
- Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, 510055, China.
| | - Zetao Chen
- Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, 510055, China.
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Jaiswal M, Sharma M, Khandnor P, Goyal A, Belokar R, Harit S, Sood T, Goyal K, Dua P. Deep Learning Models for Classification of Deciduous and Permanent Teeth From Digital Panoramic Images. Cureus 2023; 15:e49937. [PMID: 38179345 PMCID: PMC10765069 DOI: 10.7759/cureus.49937] [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] [Accepted: 12/04/2023] [Indexed: 01/06/2024] Open
Abstract
INTRODUCTION Dental radiographs are essential in the diagnostic process in dentistry. They serve various purposes, including determining age, analyzing patterns of tooth eruption/shedding, and treatment planning and prognosis. The emergence of digital radiography technology has piqued interest in using artificial intelligence systems to assist and guide dental professionals. These cutting-edge technologies assist in streamlining decision-making processes by enabling entity classification and localization tasks. With the integration of artificial Intelligence algorithms tailored for pediatric dentistry applications and utilizing automated tools, there is an optimistic outlook on improving diagnostic capabilities while reducing stress and fatigue among clinicians. METHODOLOGY The dataset comprised 620 images (mixed dentition: 314, permanent dentition: 306). Panoramic radiographs taken were within the age range of 4-16 years. The classification of deciduous and permanent teeth involved training CNN-based models using different architectures such as Resnet, AlexNet, and EfficientNet, among others. A ratio of 70:15:15 was utilized for training, validation, and testing, respectively. RESULT AND CONCLUSION The findings indicated that among the models proposed, EfficientNetB0 and EfficientNetB3 exhibited superior performance. Both EfficientNetB0 and EfficientNetB3 achieved an accuracy rate, precision, recall, and F1 scores of 98% in classifying teeth as either deciduous or permanent. This implies that these models were highly accurate in identifying patterns/features within the dataset used for evaluation.
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Affiliation(s)
- Manoj Jaiswal
- Pedodontics and Preventive Dentistry, Postgraduate Institute of Medical Education and Research, Chandigarh, IND
| | - Megha Sharma
- Computer Science and Engineering, Punjab Engineering College, Chandigarh, IND
| | - Padmavati Khandnor
- Computer Science and Engineering, Punjab Engineering College, Chandigarh, IND
| | - Ashima Goyal
- Pedodontics and Preventive Dentistry, Postgraduate Institute of Medical Education and Research, Chandigarh, IND
| | - Rajendra Belokar
- Production and Industrial Engineering, Punjab Engineering College, Chandigarh, IND
| | - Sandeep Harit
- Computer Science and Engineering, Punjab Engineering College, Chandigarh, IND
| | - Tamanna Sood
- Computer Science and Engineering, Punjab Engineering College, Chandigarh, IND
| | - Kanav Goyal
- Mechanical Engineering, Punjab Engineering College, Chandigarh, IND
| | - Pallavi Dua
- Computer Science and Engineering, Punjab Engineering College, Chandigarh, IND
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Kong HJ. Classification of dental implant systems using cloud-based deep learning algorithm: an experimental study. JOURNAL OF YEUNGNAM MEDICAL SCIENCE 2023; 40:S29-S36. [PMID: 37491843 DOI: 10.12701/jyms.2023.00465] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 06/19/2023] [Indexed: 07/27/2023]
Abstract
BACKGROUND This study aimed to evaluate the accuracy and clinical usability of implant system classification using automated machine learning on a Google Cloud platform. METHODS Four dental implant systems were selected: Osstem TSIII, Osstem USII, Biomet 3i Os-seotite External, and Dentsply Sirona Xive. A total of 4,800 periapical radiographs (1,200 for each implant system) were collected and labeled based on electronic medical records. Regions of interest were manually cropped to 400×800 pixels, and all images were uploaded to Google Cloud storage. Approximately 80% of the images were used for training, 10% for validation, and 10% for testing. Google automated machine learning (AutoML) Vision automatically executed a neural architecture search technology to apply an appropriate algorithm to the uploaded data. A single-label image classification model was trained using AutoML. The performance of the mod-el was evaluated in terms of accuracy, precision, recall, specificity, and F1 score. RESULTS The accuracy, precision, recall, specificity, and F1 score of the AutoML Vision model were 0.981, 0.963, 0.961, 0.985, and 0.962, respectively. Osstem TSIII had an accuracy of 100%. Osstem USII and 3i Osseotite External were most often confused in the confusion matrix. CONCLUSION Deep learning-based AutoML on a cloud platform showed high accuracy in the classification of dental implant systems as a fine-tuned convolutional neural network. Higher-quality images from various implant systems will be required to improve the performance and clinical usability of the model.
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Affiliation(s)
- Hyun Jun Kong
- Department of Prosthodontics, College of Dentistry, Wonkwang University, Iksan, Korea
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15
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Ou-Yang S, Han S, Sun D, Wu H, Chen J, Cai Y, Yin D, Ou-Yang H, Liao L. The preliminary in vitro study and application of deep learning algorithm in cone beam computed tomography image implant recognition. Sci Rep 2023; 13:18467. [PMID: 37891408 PMCID: PMC10611753 DOI: 10.1038/s41598-023-45757-1] [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: 06/11/2023] [Accepted: 10/23/2023] [Indexed: 10/29/2023] Open
Abstract
To properly repair and maintain implants, which are bone tissue implants that replace natural tooth roots, it is crucial to accurately identify their brand and specification. Deep learning has demonstrated outstanding capabilities in analysis, such as image identification and classification, by learning the inherent rules and degrees of representation of data models. The purpose of this study is to evaluate deep learning algorithms and their supporting application software for their ability to recognize and categorize three dimensional (3D) Cone Beam Computed Tomography (CBCT) images of dental implants. By using CBCT technology, the 3D imaging data of 27 implants of various sizes and brands were obtained. Following manual processing, the data were transformed into a data set that had 13,500 two-dimensional data. Nine deep learning algorithms including GoogleNet, InceptionResNetV2, InceptionV3, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152 and ResNet152V2 were used to perform the data. Accuracy rates, confusion matrix, ROC curve, AUC, number of model parameters and training times were used to assess the efficacy of these algorithms. These 9 deep learning algorithms achieved training accuracy rates of 100%, 99.3%, 89.3%, 99.2%, 99.1%, 99.5%, 99.4%, 99.5%, 98.9%, test accuracy rates of 98.3%, 97.5%, 94.8%, 85.4%, 92.5%, 80.7%, 93.6%, 93.2%, 99.3%, area under the curve (AUC) values of 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00. When used to identify implants, all nine algorithms perform satisfactorily, with ResNet152V2 achieving the highest test accuracy, classification accuracy, confusion matrix area under the curve, and receiver operating characteristic curve area under the curve area. The results showed that the ResNet152V2 has the best classification effect on identifying implants. The artificial intelligence identification system and application software based on this algorithm can efficiently and accurately identify the brands and specifications of 27 classified implants through processed 3D CBCT images in vitro, with high stability and low recognition cost.
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Affiliation(s)
- Shaobo Ou-Yang
- The Affiliated Stomatological Hospital of Nanchang University, The Key Laboratory of Oral Biomedicine, Jiangxi Province Clinical Research Centre for Oral Diseases, Nanchang, Jiangxi Province, China
| | - Shuqin Han
- The Affiliated Stomatological Hospital of Nanchang University, The Key Laboratory of Oral Biomedicine, Jiangxi Province Clinical Research Centre for Oral Diseases, Nanchang, Jiangxi Province, China
| | - Dan Sun
- Information Security Evaluation Section, Jiangxi Science and Technology Infrastructure Center, Nanchang, China
| | - Hongping Wu
- Vocational Teachers College, Jiangxi Agricultural University, Nanchang, China
| | - Jianping Chen
- Vocational Teachers College, Jiangxi Agricultural University, Nanchang, China
| | - Ying Cai
- The Affiliated Stomatological Hospital of Nanchang University, The Key Laboratory of Oral Biomedicine, Jiangxi Province Clinical Research Centre for Oral Diseases, Nanchang, Jiangxi Province, China
| | - Dongmei Yin
- The Affiliated Stomatological Hospital of Nanchang University, The Key Laboratory of Oral Biomedicine, Jiangxi Province Clinical Research Centre for Oral Diseases, Nanchang, Jiangxi Province, China
| | - Huidan Ou-Yang
- Vocational Teachers College, Jiangxi Agricultural University, Nanchang, China.
| | - Lan Liao
- The Affiliated Stomatological Hospital of Nanchang University, The Key Laboratory of Oral Biomedicine, Jiangxi Province Clinical Research Centre for Oral Diseases, Nanchang, Jiangxi Province, China.
- School of Stomatology, Nanchang University, The Key Laboratory of Oral Biomedicine, Jiangxi Province, Jiangxi Province Clinical Research Center for Oral Diseases, Nanchang, China.
- Clinical Medical Research Center, Affiliated Hospital of Jinggangshan University, Medical Department of Jinggangshan University, Ji'an, Jiangxi Province, People's Republic of China.
- The Key Laboratory of Oral Biomedicine, The Affiliated Stomatological Hospital of Nanchang University, The Affiliated Hospital of Jinggangshan University, Nanchang, Jiangxi Province, China.
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16
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Huang X, Chen X, Zhong X, Tian T. The CNN model aided the study of the clinical value hidden in the implant images. J Appl Clin Med Phys 2023; 24:e14141. [PMID: 37656066 PMCID: PMC10562019 DOI: 10.1002/acm2.14141] [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: 03/10/2023] [Revised: 08/14/2023] [Accepted: 08/16/2023] [Indexed: 09/02/2023] Open
Abstract
PURPOSE This article aims to construct a new method to evaluate radiographic image identification results based on artificial intelligence, which can complement the limited vision of researchers when studying the effect of various factors on clinical implantation outcomes. METHODS We constructed a convolutional neural network (CNN) model using the clinical implant radiographic images. Moreover, we used gradient-weighted class activation mapping (Grad-CAM) to obtain thermal maps to present identification differences before performing statistical analyses. Subsequently, to verify whether these differences presented by the Grad-CAM algorithm would be of value to clinical practices, we measured the bone thickness around the identified sites. Finally, we analyzed the influence of the implant type on the implantation according to the measurement results. RESULTS The thermal maps showed that the sites with significant differences between Straumann BL and Bicon implants as identified by the CNN model were mainly the thread and neck area. (2) The heights of the mesial, distal, buccal, and lingual bone of the Bicon implant post-op were greater than those of Straumann BL (P < 0.05). (3) Between the first and second stages of surgery, the amount of bone thickness variation at the buccal and lingual sides of the Bicon implant platform was greater than that of the Straumann BL implant (P < 0.05). CONCLUSION According to the results of this study, we found that the identified-neck-area of the Bicon implant was placed deeper than the Straumann BL implant, and there was more bone resorption on the buccal and lingual sides at the Bicon implant platform between the first and second stages of surgery. In summary, this study proves that using the CNN classification model can identify differences that complement our limited vision.
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Affiliation(s)
- Xinxu Huang
- State Key Laboratory of Oral DiseasesNational Clinical Research Center for Oral DiseasesWest China Hospital of StomatologySichuan UniversityChengduChina
| | - Xingyu Chen
- State Key Laboratory of Oral DiseasesNational Clinical Research Center for Oral DiseasesWest China Hospital of StomatologySichuan UniversityChengduChina
| | - Xinnan Zhong
- State Key Laboratory of Oral DiseasesNational Clinical Research Center for Oral DiseasesWest China Hospital of StomatologySichuan UniversityChengduChina
| | - Taoran Tian
- State Key Laboratory of Oral DiseasesNational Clinical Research Center for Oral DiseasesWest China Hospital of StomatologySichuan UniversityChengduChina
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Altalhi AM, Alharbi FS, Alhodaithy MA, Almarshedy BS, Al-Saaib MY, Al Jfshar RM, Aljohani AS, Alshareef AH, Muhayya M, Al-Harbi NH. The Impact of Artificial Intelligence on Dental Implantology: A Narrative Review. Cureus 2023; 15:e47941. [PMID: 38034167 PMCID: PMC10685062 DOI: 10.7759/cureus.47941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/30/2023] [Indexed: 12/02/2023] Open
Abstract
Implant dentistry has witnessed a transformative shift with the integration of artificial intelligence (AI) technologies. This article explores the role of AI in implant dentistry, emphasizing its impact on diagnostics, treatment planning, and patient outcomes. AI-driven image analysis and deep learning algorithms enhance the precision of implant placement, reducing risks and optimizing aesthetics. Moreover, AI-driven data analytics provide valuable insights into patient-specific treatment strategies, improving overall success rates. As AI continues to evolve, it promises to reshape the landscape of implant dentistry and lead in an era of personalized and efficient oral healthcare.
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Affiliation(s)
| | | | | | | | | | | | | | - Adeeb H Alshareef
- Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, SAU
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Vera M, Gómez-Silva MJ, Vera V, López-González CI, Aliaga I, Gascó E, Vera-González V, Pedrera-Canal M, Besada-Portas E, Pajares G. Artificial Intelligence Techniques for Automatic Detection of Peri-implant Marginal Bone Remodeling in Intraoral Radiographs. J Digit Imaging 2023; 36:2259-2277. [PMID: 37468696 PMCID: PMC10501983 DOI: 10.1007/s10278-023-00880-3] [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: 03/08/2023] [Revised: 06/30/2023] [Accepted: 07/01/2023] [Indexed: 07/21/2023] Open
Abstract
Peri-implantitis can cause marginal bone remodeling around implants. The aim is to develop an automatic image processing approach based on two artificial intelligence (AI) techniques in intraoral (periapical and bitewing) radiographs to assist dentists in determining bone loss. The first is a deep learning (DL) object-detector (YOLOv3) to roughly identify (no exact localization is required) two objects: prosthesis (crown) and implant (screw). The second is an image understanding-based (IU) process to fine-tune lines on screw edges and to identify significant points (intensity bone changes, intersections between screw and crown). Distances between these points are used to compute bone loss. A total of 2920 radiographs were used for training (50%) and testing (50%) the DL process. The mAP@0.5 metric is used for performance evaluation of DL considering periapical/bitewing and screws/crowns in upper and lower jaws, with scores ranging from 0.537 to 0.898 (sufficient because DL only needs an approximation). The IU performance is assessed with 50% of the testing radiographs through the t test statistical method, obtaining p values of 0.0106 (line fitting) and 0.0213 (significant point detection). The IU performance is satisfactory, as these values are in accordance with the statistical average/standard deviation in pixels for line fitting (2.75/1.01) and for significant point detection (2.63/1.28) according to the expert criteria of dentists, who establish the ground-truth lines and significant points. In conclusion, AI methods have good prospects for automatic bone loss detection in intraoral radiographs to assist dental specialists in diagnosing peri-implantitis.
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Affiliation(s)
- María Vera
- Department of Conservative Dentistry and Prostheses, Faculty of Dentistry, Complutense University of Madrid, Madrid, Spain
| | - María José Gómez-Silva
- Department of Computer Architecture and Automation, Faculty of Informatics, Complutense University of Madrid, Madrid, Spain
| | - Vicente Vera
- Department of Conservative Dentistry and Prostheses, Faculty of Dentistry, Complutense University of Madrid, Madrid, Spain
| | - Clara I. López-González
- Department of Software Engineering and Artificial Intelligence, Faculty of Informatics, Complutense University of Madrid, Madrid, Spain
| | - Ignacio Aliaga
- Department of Conservative Dentistry and Prostheses, Faculty of Dentistry, Complutense University of Madrid, Madrid, Spain
| | - Esther Gascó
- Department of Software Engineering and Artificial Intelligence, Faculty of Informatics, Complutense University of Madrid, Madrid, Spain
| | - Vicente Vera-González
- Department of Conservative Dentistry and Prostheses, Faculty of Dentistry, Complutense University of Madrid, Madrid, Spain
| | - María Pedrera-Canal
- Hospital Clínico San Carlos, Complutense University of Madrid, Madrid, Spain
| | - Eva Besada-Portas
- Department of Computer Architecture and Automation, Faculty of Informatics, Complutense University of Madrid, Madrid, Spain
| | - Gonzalo Pajares
- Instituto de Tecnología del Conocimiento (Institute of Knowledge Technology), Complutense University of Madrid, Madrid, Spain
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Tiryaki B, Ozdogan A, Guller MT, Miloglu O, Oral EA, Ozbek IY. Dental implant brand and angle identification using deep neural networks. J Prosthet Dent 2023:S0022-3913(23)00492-4. [PMID: 37716899 DOI: 10.1016/j.prosdent.2023.07.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/25/2023] [Accepted: 07/26/2023] [Indexed: 09/18/2023]
Abstract
STATEMENT OF PROBLEM Determining the brand and angle of an implant clinically or radiographically can be challenging. Whether artificial intelligence can assist is unclear. PURPOSE The purpose of the present study was to determine the brand and angle of implants from panoramic radiographs with artificial intelligence. MATERIAL AND METHODS Panoramic radiographs were used to classify the accuracy of different dental implant brands through deep convolutional neural networks (CNNs) with transfer-learning strategies. The implant classification performance of 5 deep CNN models was evaluated using a total of 11 904 images of 5 different implant types extracted from 2634 radiographs. In addition, the angle of implant images was estimated by calculating the angle of 2634 implant images by applying a regression model based on deep CNN. RESULTS Among the 5 deep CNN models, the highest performance was obtained in the Visual Geometry Group (VGG)-19 model with a 98.3% accuracy rate. By applying a fusion approach based on majority voting, the accuracy rate was slightly improved to 98.9%. In addition, the root mean square error value of 2.91 degrees was obtained as a result of the regression model used in the implant angle estimation problem. CONCLUSIONS Implant images from panoramic radiographs could be classified with a high accuracy, and their angles estimated with a low mean error.
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Affiliation(s)
- Burcu Tiryaki
- Research Assistant, Department of Electrical and Electronics Engineering, Faculty of Engineering, Atatürk University, Erzurum, Turkey
| | - Alper Ozdogan
- Associate Professor, Department of Prosthodontics, Faculty of Dentistry, Atatürk University, Erzurum, Turkey.
| | - Mustafa Taha Guller
- Lecturer, Department of Dentistry Services, Vocational School of Health Services, Erzincan Binali Yıldırım University, Erzincan, Turkey
| | - Ozkan Miloglu
- Professor, Department of Oral, Dental and Maxillofacial Radiology, Faculty of Dentistry, Atatürk University, Erzurum, Turkey
| | - Emin Argun Oral
- Associate Professor, Department of Electrical and Electronics Engineering, Faculty of Engineering, Atatürk University, Erzurum, Turkey
| | - Ibrahim Yucel Ozbek
- Professor, Department of Electrical and Electronics Engineering, Faculty of Engineering, Atatürk University, Erzurum, Turkey
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Tabatabaian F, Vora SR, Mirabbasi S. Applications, functions, and accuracy of artificial intelligence in restorative dentistry: A literature review. J ESTHET RESTOR DENT 2023; 35:842-859. [PMID: 37522291 DOI: 10.1111/jerd.13079] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 08/01/2023]
Abstract
OBJECTIVE The applications of artificial intelligence (AI) are increasing in restorative dentistry; however, the AI performance is unclear for dental professionals. The purpose of this narrative review was to evaluate the applications, functions, and accuracy of AI in diverse aspects of restorative dentistry including caries detection, tooth preparation margin detection, tooth restoration design, metal structure casting, dental restoration/implant detection, removable partial denture design, and tooth shade determination. OVERVIEW An electronic search was performed on Medline/PubMed, Embase, Web of Science, Cochrane, Scopus, and Google Scholar databases. English-language articles, published from January 1, 2000, to March 1, 2022, relevant to the aforementioned aspects were selected using the key terms of artificial intelligence, machine learning, deep learning, artificial neural networks, convolutional neural networks, clustering, soft computing, automated planning, computational learning, computer vision, and automated reasoning as inclusion criteria. A manual search was also performed. Therefore, 157 articles were included, reviewed, and discussed. CONCLUSIONS Based on the current literature, the AI models have shown promising performance in the mentioned aspects when being compared with traditional approaches in terms of accuracy; however, as these models are still in development, more studies are required to validate their accuracy and apply them to routine clinical practice. CLINICAL SIGNIFICANCE AI with its specific functions has shown successful applications with acceptable accuracy in diverse aspects of restorative dentistry. The understanding of these functions may lead to novel applications with optimal accuracy for AI in restorative dentistry.
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Affiliation(s)
- Farhad Tabatabaian
- Department of Oral Health Sciences, Faculty of Dentistry, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Siddharth R Vora
- Department of Oral Health Sciences, Faculty of Dentistry, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Shahriar Mirabbasi
- Department of Electrical and Computer Engineering, Faculty of Applied Science, The University of British Columbia, Vancouver, British Columbia, Canada
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Sukegawa S, Ono S, Tanaka F, Inoue Y, Hara T, Yoshii K, Nakano K, Takabatake K, Kawai H, Katsumitsu S, Nakai F, Nakai Y, Miyazaki R, Murakami S, Nagatsuka H, Miyake M. Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists. Sci Rep 2023; 13:11676. [PMID: 37468501 DOI: 10.1038/s41598-023-38343-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 07/06/2023] [Indexed: 07/21/2023] Open
Abstract
The study aims to identify histological classifiers from histopathological images of oral squamous cell carcinoma using convolutional neural network (CNN) deep learning models and shows how the results can improve diagnosis. Histopathological samples of oral squamous cell carcinoma were prepared by oral pathologists. Images were divided into tiles on a virtual slide, and labels (squamous cell carcinoma, normal, and others) were applied. VGG16 and ResNet50 with the optimizers stochastic gradient descent with momentum and spectral angle mapper (SAM) were used, with and without a learning rate scheduler. The conditions for achieving good CNN performances were identified by examining performance metrics. We used ROCAUC to statistically evaluate diagnostic performance improvement of six oral pathologists using the results from the selected CNN model for assisted diagnosis. VGG16 with SAM showed the best performance, with accuracy = 0.8622 and AUC = 0.9602. The diagnostic performances of the oral pathologists statistically significantly improved when the diagnostic results of the deep learning model were used as supplementary diagnoses (p-value = 0.031). By considering the learning results of deep learning model classifiers, the diagnostic accuracy of pathologists can be improved. This study contributes to the application of highly reliable deep learning models for oral pathological diagnosis.
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Affiliation(s)
- Shintaro Sukegawa
- Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine, 1750-1 Ikenobe, Miki, Kagawa, 761-0793, Japan.
- Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-Machi, Takamatsu, Kagawa, 760-8557, Japan.
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan.
| | - Sawako Ono
- Department of Pathology, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-Machi, Takamatsu, Kagawa, 760-8557, Japan
| | - Futa Tanaka
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
| | - Yuta Inoue
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
| | - Takeshi Hara
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
- Center for Healthcare Information Technology, Tokai National Higher Education and Research System, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
| | - Kazumasa Yoshii
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
| | - Keisuke Nakano
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Kiyofumi Takabatake
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Hotaka Kawai
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Shimada Katsumitsu
- Department of Oral Pathology, Graduate School of Oral Medicine, Matsumoto Dental University, 1780 Hirooka-Gobara, Shiojiri, Nagano, 399-0781, Japan
| | - Fumi Nakai
- Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine, 1750-1 Ikenobe, Miki, Kagawa, 761-0793, Japan
| | - Yasuhiro Nakai
- Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine, 1750-1 Ikenobe, Miki, Kagawa, 761-0793, Japan
| | - Ryo Miyazaki
- Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine, 1750-1 Ikenobe, Miki, Kagawa, 761-0793, Japan
| | - Satoshi Murakami
- Department of Oral Pathology, Graduate School of Oral Medicine, Matsumoto Dental University, 1780 Hirooka-Gobara, Shiojiri, Nagano, 399-0781, Japan
| | - Hitoshi Nagatsuka
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Minoru Miyake
- Department of Oral and Maxillofacial Surgery, Kagawa University Faculty of Medicine, 1750-1 Ikenobe, Miki, Kagawa, 761-0793, Japan
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Shafi I, Fatima A, Afzal H, Díez IDLT, Lipari V, Breñosa J, Ashraf I. A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health. Diagnostics (Basel) 2023; 13:2196. [PMID: 37443594 DOI: 10.3390/diagnostics13132196] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/14/2023] [Accepted: 06/23/2023] [Indexed: 07/15/2023] Open
Abstract
Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.
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Affiliation(s)
- Imran Shafi
- College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Anum Fatima
- National Centre for Robotics, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Hammad Afzal
- Military College of Signals (MCS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Isabel de la Torre Díez
- Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Vivian Lipari
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Fundación Universitaria Internacional de Colombia, Bogotá 111311, Colombia
| | - Jose Breñosa
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Universidade Internacional do Cuanza, Cuito EN250, Bié, Angola
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Universidad Internacional Iberoamericana Arecibo, Puerto Rico, PR 00613, USA
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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Rajaram Mohan K, Mathew Fenn S. Artificial Intelligence and Its Theranostic Applications in Dentistry. Cureus 2023; 15:e38711. [PMID: 37292569 PMCID: PMC10246515 DOI: 10.7759/cureus.38711] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/16/2022] [Indexed: 06/10/2023] Open
Abstract
As new technologies emerge, they continue to have an impact on our daily lives, and artificial intelligence (AI) covers a wide range of applications. Because of the advancements in AI, it is now possible to analyse large amounts of data, which results in more accurate data and more effective decision-making. This article explains the fundamentals of AI and examines its development and present use. AI technology has had an impact on the healthcare sector as a result of the need for accurate diagnosis and improved patient care. An overview of the existing AI applications in clinical dentistry was provided. Comprehensive care involving artificial intelligence aims to provide cutting-edge research and innovations, as well as high-quality patient care, by enabling sophisticated decision support tools. The cornerstone of AI advancement in dentistry is creative inter-professional coordination among medical professionals, scientists, and engineers. Artificial intelligence will continue to be associated with dentistry from a wide angle despite potential misconceptions and worries about patient privacy. This is because precise treatment methods and quick data sharing are both essential in dentistry. Additionally, these developments will make it possible for patients, academicians, and healthcare professionals to exchange large data on health as well as provide insights that enhance patient care.
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Affiliation(s)
- Karthik Rajaram Mohan
- Oral Medicine, Vinayaka Mission's Sankarachariyar Dental College, Vinayaka Mission's Research Foundation (Deemed to be University), Salem, IND
| | - Saramma Mathew Fenn
- Oral Medicine and Radiology, Vinayaka Mission's Sankarachariyar Dental College, Vinayaka Mission's Research Foundation (Deemed to be University), Salem, IND
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Park WS, Huh JK, Lee JH. Automated deep learning for classification of dental implant radiographs using a large multi-center dataset. Sci Rep 2023; 13:4862. [PMID: 36964171 PMCID: PMC10039053 DOI: 10.1038/s41598-023-32118-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 03/22/2023] [Indexed: 03/26/2023] Open
Abstract
This study aimed to evaluate the accuracy of automated deep learning (DL) algorithm for identifying and classifying various types of dental implant systems (DIS) using a large-scale multicenter dataset. Dental implant radiographs of pos-implant surgery were collected from five college dental hospitals and 10 private dental clinics, and validated by the National Information Society Agency and the Korean Academy of Oral and Maxillofacial Implantology. The dataset contained a total of 156,965 panoramic and periapical radiographic images and comprised 10 manufacturers and 27 different types of DIS. The accuracy, precision, recall, F1 score, and confusion matrix were calculated to evaluate the classification performance of the automated DL algorithm. The performance metrics of the automated DL based on accuracy, precision, recall, and F1 score for 116,756 panoramic and 40,209 periapical radiographic images were 88.53%, 85.70%, 82.30%, and 84.00%, respectively. Using only panoramic images, the DL algorithm achieved 87.89% accuracy, 85.20% precision, 81.10% recall, and 83.10% F1 score, whereas the corresponding values using only periapical images achieved 86.87% accuracy, 84.40% precision, 81.70% recall, and 83.00% F1 score, respectively. Within the study limitations, automated DL shows a reliable classification accuracy based on large-scale and comprehensive datasets. Moreover, we observed no statistically significant difference in accuracy performance between the panoramic and periapical images. The clinical feasibility of the automated DL algorithm requires further confirmation using additional clinical datasets.
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Affiliation(s)
- Won-Se Park
- Korean Academy of Oral and Maxillofacial Implantology (KAOMI) Implant Research Institute, Seoul, Korea
- Department of Advanced General Dentistry, Yonsei University College of Dentistry, Seoul, Korea
| | - Jong-Ki Huh
- Korean Academy of Oral and Maxillofacial Implantology (KAOMI) Implant Research Institute, Seoul, Korea.
- Department of Oral and Maxillofacial Surgery, Gangnam Severance Hospital, Yonsei University College of Dentistry, 211 Eonju-ro, Gangnam-gu, Seoul, 06273, Korea.
| | - Jae-Hong Lee
- Korean Academy of Oral and Maxillofacial Implantology (KAOMI) Implant Research Institute, Seoul, Korea.
- Department of Periodontology, College of Dentistry and Institute of Oral Bioscience, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju, 54896, Korea.
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Automated Evaluation of Upper Airway Obstruction Based on Deep Learning. BIOMED RESEARCH INTERNATIONAL 2023; 2023:8231425. [PMID: 36852295 PMCID: PMC9966825 DOI: 10.1155/2023/8231425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 10/31/2022] [Accepted: 01/25/2023] [Indexed: 02/20/2023]
Abstract
Objectives This study is aimed at developing a screening tool that could evaluate the upper airway obstruction on lateral cephalograms based on deep learning. Methods We developed a novel and practical convolutional neural network model to automatically evaluate upper airway obstruction based on ResNet backbone using the lateral cephalogram. A total of 1219 X-ray images were collected for model training and testing. Results In comparison with VGG16, our model showed a better performance with sensitivity of 0.86, specificity of 0.89, PPV of 0.90, NPV of 0.85, and F1-score of 0.88, respectively. The heat maps of cephalograms showed a deeper understanding of features learned by deep learning model. Conclusion This study demonstrated that deep learning could learn effective features from cephalograms and automated evaluate upper airway obstruction according to X-ray images. Clinical Relevance. A novel and practical deep convolutional neural network model has been established to relieve dentists' workload of screening and improve accuracy in upper airway obstruction.
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Bornes RS, Montero J, Correia ARM, Rosa NRDN. Use of bioinformatic strategies as a predictive tool in implant-supported oral rehabilitation: A scoping review. J Prosthet Dent 2023; 129:322.e1-322.e8. [PMID: 36710172 DOI: 10.1016/j.prosdent.2022.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/29/2022] [Accepted: 12/29/2022] [Indexed: 01/29/2023]
Abstract
STATEMENT OF PROBLEM The use of bioinformatic strategies is growing in dental implant protocols. The current expansion of Omics sciences and artificial intelligence (AI) algorithms in implant dentistry applications have not been documented and analyzed as a predictive tool for the success of dental implants. PURPOSE The purpose of this scoping review was to analyze how artificial intelligence algorithms and Omics technologies are being applied in the field of oral implantology as a predictive tool for dental implant success. MATERIAL AND METHODS The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist was followed. A search strategy was created at PubMed and Web of Science to answer the question "How is bioinformatics being applied in the area of oral implantology as a predictive tool for implant success?" RESULTS Thirteen articles were included in this review. Only 3 applied bioinformatic models combining AI algorithms and Omics technologies. These studies highlighted 2 key points for the creation of precision medicine: deep population phenotyping and the integration of Omics sciences in clinical protocols. Most of the studies identified applied AI only in the identification and classification of implant systems, quantification of peri-implant bone loss, and 3-dimensional bone analysis, planning implant placement. CONCLUSIONS The conventional criteria currently used as a technique for the diagnosis and monitoring of dental implants are insufficient and have low accuracy. Models that apply AI algorithms combined with precision methodologies-biomarkers-are extremely useful in the creation of precision medicine, allowing medical dentists to forecast the success of the implant. Tools that integrate the different types of data, including imaging, molecular, risk factor, and implant characteristics, are needed to make a more accurate and personalized prediction of implant success.
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Affiliation(s)
- Rita Silva Bornes
- Guest Lecturer, Universidade Católica Portuguesa, Faculty of Dental Medicine (FMD), Center for Interdisciplinary Research in Health (CIIS), Viseu, Portugal.
| | - Javier Montero
- Full professor in Prosthodontics, Department of Surgery, Faculty of Medicine, University of Salamanca, Salamanca, Spain
| | - André Ricardo Maia Correia
- Assistant Professor, Universidade Católica Portuguesa, Faculty of Dental Medicine (FMD), Center for Interdisciplinary Research in Health (CIIS), Viseu, Portugal
| | - Nuno Ricardo das Neves Rosa
- Assistant Professor, Universidade Católica Portuguesa, Faculty of Dental Medicine (FMD), Center for Interdisciplinary Research in Health (CIIS), Viseu, Portugal
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Artificial intelligence applications in implant dentistry: A systematic review. J Prosthet Dent 2023; 129:293-300. [PMID: 34144789 DOI: 10.1016/j.prosdent.2021.05.008] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 05/11/2021] [Accepted: 05/11/2021] [Indexed: 12/21/2022]
Abstract
STATEMENT OF PROBLEM Artificial intelligence (AI) applications are growing in dental implant procedures. The current expansion and performance of AI models in implant dentistry applications have not yet been systematically documented and analyzed. PURPOSE The purpose of this systematic review was to assess the performance of AI models in implant dentistry for implant type recognition, implant success prediction by using patient risk factors and ontology criteria, and implant design optimization combining finite element analysis (FEA) calculations and AI models. MATERIAL AND METHODS An electronic systematic review was completed in 5 databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. A manual search was also conducted. Peer-reviewed studies that developed AI models for implant type recognition, implant success prediction, and implant design optimization were included. The search strategy included articles published until February 21, 2021. Two investigators independently evaluated the quality of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus. RESULTS Seventeen articles were included: 7 investigations analyzed AI models for implant type recognition, 7 studies included AI prediction models for implant success forecast, and 3 studies evaluated AI models for optimization of implant designs. The AI models developed to recognize implant type by using periapical and panoramic images obtained an overall accuracy outcome ranging from 93.8% to 98%. The models to predict osteointegration success or implant success by using different input data varied among the studies, ranging from 62.4% to 80.5%. Finally, the studies that developed AI models to optimize implant designs seem to agree on the applicability of AI models to improve the design of dental implants. This improvement includes minimizing the stress at the implant-bone interface by 36.6% compared with the finite element model; optimizing the implant design porosity, length, and diameter to improve the finite element calculations; or accurately determining the elastic modulus of the implant-bone interface. CONCLUSIONS AI models for implant type recognition, implant success prediction, and implant design optimization have demonstrated great potential but are still in development. Additional studies are indispensable to the further development and assessment of the clinical performance of AI models for those implant dentistry applications reviewed.
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Machine Learning in Dentistry: A Scoping Review. J Clin Med 2023; 12:jcm12030937. [PMID: 36769585 PMCID: PMC9918184 DOI: 10.3390/jcm12030937] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/06/2023] [Accepted: 01/23/2023] [Indexed: 01/27/2023] Open
Abstract
Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies.
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Prediction of Bone Healing around Dental Implants in Various Boundary Conditions by Deep Learning Network. Int J Mol Sci 2023; 24:ijms24031948. [PMID: 36768272 PMCID: PMC9915893 DOI: 10.3390/ijms24031948] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/13/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
Tissue differentiation varies based on patients' conditions, such as occlusal force and bone properties. Thus, the design of the implants needs to take these conditions into account to improve osseointegration. However, the efficiency of the design procedure is typically not satisfactory and needs to be significantly improved. Thus, a deep learning network (DLN) is proposed in this study. A data-driven DLN consisting of U-net, ANN, and random forest models was implemented. It serves as a surrogate for finite element analysis and the mechano-regulation algorithm. The datasets include the history of tissue differentiation throughout 35 days with various levels of occlusal force and bone properties. The accuracy of day-by-day tissue differentiation prediction in the testing dataset was 82%, and the AUC value of the five tissue phenotypes (fibrous tissue, cartilage, immature bone, mature bone, and resorption) was above 0.86, showing a high prediction accuracy. The proposed DLN model showed the robustness for surrogating the complex, time-dependent calculations. The results can serve as a design guideline for dental implants.
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Das HS, Das A, Neog A, Mallik S, Bora K, Zhao Z. Breast cancer detection: Shallow convolutional neural network against deep convolutional neural networks based approach. Front Genet 2023; 13:1097207. [PMID: 36685963 PMCID: PMC9846574 DOI: 10.3389/fgene.2022.1097207] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 12/15/2022] [Indexed: 01/06/2023] Open
Abstract
Introduction: Of all the cancers that afflict women, breast cancer (BC) has the second-highest mortality rate, and it is also believed to be the primary cause of the high death rate. Breast cancer is the most common cancer that affects women globally. There are two types of breast tumors: benign (less harmful and unlikely to become breast cancer) and malignant (which are very dangerous and might result in aberrant cells that could result in cancer). Methods: To find breast abnormalities like masses and micro-calcifications, competent and educated radiologists often examine mammographic images. This study focuses on computer-aided diagnosis to help radiologists make more precise diagnoses of breast cancer. This study aims to compare and examine the performance of the proposed shallow convolutional neural network architecture having different specifications against pre-trained deep convolutional neural network architectures trained on mammography images. Mammogram images are pre-processed in this study's initial attempt to carry out the automatic identification of BC. Thereafter, three different types of shallow convolutional neural networks with representational differences are then fed with the resulting data. In the second method, transfer learning via fine-tuning is used to feed the same collection of images into pre-trained convolutional neural networks VGG19, ResNet50, MobileNet-v2, Inception-v3, Xception, and Inception-ResNet-v2. Results: In our experiment with two datasets, the accuracy for the CBIS-DDSM and INbreast datasets are 80.4%, 89.2%, and 87.8%, 95.1% respectively. Discussion: It can be concluded from the experimental findings that the deep network-based approach with precise tuning outperforms all other state-of-the-art techniques in experiments on both datasets.
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Affiliation(s)
- Himanish Shekhar Das
- Department of Computer Science and Information Technology, Cotton University, Guwahati, India
| | - Akalpita Das
- Department of Computer Science and Engineering, GIMT Guwahati, Guwahati, India
| | - Anupal Neog
- Department of AI and Machine Learning COE, IQVIA, Bengaluru, Karnataka, India
| | - Saurav Mallik
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, United States
- Department of Pharmacology and Toxicology, University of Arizona, Tucson, AZ, United States
| | - Kangkana Bora
- Department of Computer Science and Information Technology, Cotton University, Guwahati, India
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Department of Pathology and Laboratory Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, United States
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Jang WS, Kim S, Yun PS, Jang HS, Seong YW, Yang HS, Chang JS. Accurate detection for dental implant and peri-implant tissue by transfer learning of faster R-CNN: a diagnostic accuracy study. BMC Oral Health 2022; 22:591. [PMID: 36494645 PMCID: PMC9737962 DOI: 10.1186/s12903-022-02539-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 10/26/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The diagnosis of dental implants and the periapical tissues using periapical radiographs is crucial. Recently, artificial intelligence has shown a rapid advancement in the field of radiographic imaging. PURPOSE This study attempted to detect dental implants and peri-implant tissues by using a deep learning method known as object detection on the implant image of periapical radiographs. METHODS After implant treatment, the periapical images were collected and data were processed by labeling the dental implant and peri-implant tissue together in the images. Next, 300 images of the periapical radiographs were split into 80:20 ratio (i.e. 80% of the data were used for training the model while 20% were used for testing the model). These were evaluated using an object detection model known as Faster R-CNN, which simultaneously performs classification and localization. This model was evaluated on the classification performance using metrics, including precision, recall, and F1 score. Additionally, in order to assess the localization performance, an evaluation through intersection over union (IoU) was utilized, and, Average Precision (AP) was used to assess both the classification and localization performance. RESULTS Considering the classification performance, precision = 0.977, recall = 0.992, and F1 score = 0.984 were derived. The indicator of localization was derived as mean IoU = 0.907. On the other hand, considering the indicators of both classification and localization performance, AP showed an object detection level of AP@0.5 = 0.996 and AP@0.75 = 0.967. CONCLUSION Thus, the implementation of Faster R-CNN model for object detection on 300 periapical radiographic images including dental implants, resulted in high-quality object detection for dental implants and peri-implant tissues.
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Affiliation(s)
- Woo Sung Jang
- grid.15444.300000 0004 0470 5454Department of Artificial Intelligence, College of Engineering, Yonsei University, Seoul, Korea
| | - Sunjai Kim
- grid.15444.300000 0004 0470 5454Department of Prosthodontics, Gangnam Severance Dental Hospital, College of Dentistry, Yonsei University, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Korea
| | - Pill Sang Yun
- grid.15444.300000 0004 0470 5454Department of Prosthodontics, Gangnam Severance Dental Hospital, College of Dentistry, Yonsei University, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Korea
| | - Han Sol Jang
- grid.15444.300000 0004 0470 5454Department of Prosthodontics, Gangnam Severance Dental Hospital, College of Dentistry, Yonsei University, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Korea
| | - You Won Seong
- grid.26999.3d0000 0001 2151 536XGraduate School of Public Policy, The University of Tokyo, Tokyo, Japan
| | - Hee Soo Yang
- grid.15444.300000 0004 0470 5454Department of Mechanical Engineering, College of Engineering, Yonsei University, Seoul, Korea
| | - Jae-Seung Chang
- grid.15444.300000 0004 0470 5454Department of Prosthodontics, Gangnam Severance Dental Hospital, College of Dentistry, Yonsei University, 211 Eonju-ro, Gangnam-gu, 06273 Seoul, Korea
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Miloglu O, Guller MT, Tosun ZT. The Use of Artificial Intelligence in Dentistry Practices. Eurasian J Med 2022; 54:34-42. [PMID: 36655443 PMCID: PMC11163356 DOI: 10.5152/eurasianjmed.2022.22301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 11/30/2022] [Indexed: 01/19/2023] Open
Abstract
Artificial intelligence can be defined as "understanding human thinking and trying to develop computer processes that will produce a similar structure." Thus, it is an attempt by a programmed computer to think. According to a broader definition, artificial intelligence is a computer equipped with human intelligencespecific capacities such as acquiring information, perceiving, seeing, thinking, and making decisions. Quality demands in dental treatments have constantly been increasing in recent years. In parallel with this, using image-based methods and multimedia-supported explanation systems on the computer is becoming widespread to evaluate the available information. The use of artificial intelligence in dentistry will greatly contribute to the reduction of treatment times and the effort spent by the dentist, reduce the need for a specialist dentist, and give a new perspective to how dentistry is practiced. In this review, we aim to review the studies conducted with artificial intelligence in dentistry and to inform our dentists about the existence of this new technology.
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Affiliation(s)
- Ozkan Miloglu
- Department of Oral, Dental and Maxillofacial Radiology, Atatürk University Faculty of Dentistry, Erzurum, Turkey
| | - Mustafa Taha Guller
- Department of Dentistry Services, Oral and Dental Health Program, Binali Yıldırım University Vocational School of Health Services, , Erzincan, Turkey
| | - Zeynep Turanli Tosun
- Department of Oral, Dental and Maxillofacial Radiology, Atatürk University Faculty of Dentistry, Erzurum, Turkey
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Zhang L, Xu F, Li Y, Zhang H, Xi Z, Xiang J, Wang B. A lightweight convolutional neural network model with receptive field block for C-shaped root canal detection in mandibular second molars. Sci Rep 2022; 12:17373. [PMID: 36253430 PMCID: PMC9576767 DOI: 10.1038/s41598-022-20411-4] [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: 03/22/2022] [Accepted: 09/13/2022] [Indexed: 01/10/2023] Open
Abstract
Rapid and accurate detection of a C-shaped root canal on mandibular second molars can assist dentists in diagnosis and treatment. Oral panoramic radiography is one of the most effective methods of determining the root canal of teeth. There are already some traditional methods based on deep learning to learn the characteristics of C-shaped root canal tooth images. However, previous studies have shown that the accuracy of detecting the C-shaped root canal still needs to be improved. And it is not suitable for implementing these network structures with limited hardware resources. In this paper, a new lightweight convolutional neural network is designed, which combined with receptive field block (RFB) for optimizing feature extraction. In order to optimize the hardware resource requirements of the model, a lightweight, multi-branch, convolutional neural network model was developed in this study. To improve the feature extraction ability of the model for C-shaped root canal tooth images, RFB has been merged with this model. RFB has achieved excellent results in target detection and classification. In the multiscale receptive field block, some small convolution kernels are used to replace the large convolution kernels, which allows the model to extract detailed features and reduce the computational complexity. Finally, the accuracy and area under receiver operating characteristics curve (AUC) values of C-shaped root canals on the image data of our mandibular second molars were 0.9838 and 0.996, respectively. The results show that the deep learning model proposed in this paper is more accurate and has lower computational complexity than many other similar studies. In addition, score-weighted class activation maps (Score-CAM) were generated to localize the internal structure that contributed to the predictions.
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Affiliation(s)
- Lijuan Zhang
- grid.464423.3Department of Oral Medicine, Shanxi Provincial People’s Hospital, Taiyuan, China
| | - Feng Xu
- grid.440656.50000 0000 9491 9632College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, 030024 Shanxi China
| | - Ying Li
- grid.440656.50000 0000 9491 9632College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, 030024 Shanxi China
| | - Huimin Zhang
- grid.440656.50000 0000 9491 9632College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, 030024 Shanxi China
| | - Ziyi Xi
- grid.440656.50000 0000 9491 9632College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, 030024 Shanxi China
| | - Jie Xiang
- grid.440656.50000 0000 9491 9632College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, 030024 Shanxi China
| | - Bin Wang
- grid.440656.50000 0000 9491 9632College of Information and Computer, Taiyuan University of Technology, No. 79, Yingze West Street, Taiyuan, 030024 Shanxi China
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Kohlakala A, Coetzer J, Bertels J, Vandermeulen D. Deep learning-based dental implant recognition using synthetic X-ray images. Med Biol Eng Comput 2022; 60:2951-2968. [PMID: 35978215 PMCID: PMC9385426 DOI: 10.1007/s11517-022-02642-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 08/02/2022] [Indexed: 11/24/2022]
Abstract
Abstract A novel algorithm for generating artificial training samples from triangulated three-dimensional (3D) surface models within the context of dental implant recognition is proposed. The proposed algorithm is based on the calculation of two-dimensional (2D) projections (from a number of different angles) of 3D volumetric representations of computer-aided design (CAD) surface models. A fully convolutional network (FCN) is subsequently trained on the artificially generated X-ray images for the purpose of automatically identifying the connection type associated with a specific dental implant in an actual X-ray image. Semi-automated and fully automated systems are proposed for segmenting questioned dental implants from the background in actual X-ray images. Within the context of the semi-automated system, suitable regions of interest (ROIs), which contain the dental implants, are manually specified. However, as part of the fully automated system, suitable ROIs are automatically detected. It is demonstrated that a segmentation/detection accuracy of 94.0% and a classification/recognition accuracy of 71.7% are attainable within the context of the proposed fully automated system. Since the proposed systems utilise an ensemble of techniques that has not been employed for the purpose of dental implant classification/recognition on any previous occasion, the above-mentioned results are very encouraging. Graphical abstract ![]()
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Affiliation(s)
- Aviwe Kohlakala
- Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Johannes Coetzer
- Department of Mathematical Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Jeroen Bertels
- ESAT, Centre for Processing Speech and Images, KU Leuven, Leuven, Belgium
| | - Dirk Vandermeulen
- ESAT, Centre for Processing Speech and Images, KU Leuven, Leuven, Belgium
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Bayrakdar IS, Orhan K, Akarsu S, Çelik Ö, Atasoy S, Pekince A, Yasa Y, Bilgir E, Sağlam H, Aslan AF, Odabaş A. Deep-learning approach for caries detection and segmentation on dental bitewing radiographs. Oral Radiol 2022; 38:468-479. [PMID: 34807344 DOI: 10.1007/s11282-021-00577-9] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/09/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVES The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer. METHODS A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University. VGG-16 and U-Net implemented with PyTorch models were used for the detection and segmentation of caries lesions, respectively. RESULTS The sensitivity, precision, and F-measure rates for caries detection and caries segmentation were 0.84, 0.81; 0.84, 0.86; and 0.84, 0.84, respectively. Comparing to 5 different experienced observers and AI models on external radiographic dataset, AI models showed superiority to assistant specialists. CONCLUSION CNN-based AI algorithms can have the potential to detect and segmentation of dental caries accurately and effectively in bitewing radiographs. AI algorithms based on the deep-learning method have the potential to assist clinicians in routine clinical practice for quickly and reliably detecting the tooth caries. The use of these algorithms in clinical practice can provide to important benefit to physicians as a clinical decision support system in dentistry.
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Affiliation(s)
- Ibrahim Sevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey.
- Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir, Turkey.
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
- Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey
| | - Serdar Akarsu
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Özer Çelik
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
- Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey
| | - Samet Atasoy
- Department of Restorative Dentistry, Faculty of Dentistry, Ordu University, Ordu, Turkey
| | - Adem Pekince
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Karabuk University, Karabuk, Turkey
| | - Yasin Yasa
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ordu University, Ordu, Turkey
| | - Elif Bilgir
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey
| | - Hande Sağlam
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey
| | - Ahmet Faruk Aslan
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Alper Odabaş
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
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Bonfanti-Gris M, Garcia-Cañas A, Alonso-Calvo R, Salido Rodriguez-Manzaneque MP, Pradies Ramiro G. Evaluation of an Artificial Intelligence web-based software to detect and classify dental structures and treatments in panoramic radiographs. J Dent 2022; 126:104301. [PMID: 36150430 DOI: 10.1016/j.jdent.2022.104301] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVES To evaluate the diagnostic reliability of a web-based Artificial Intelligence program on the detection and classification of dental structures and treatments present on panoramic radiographs. METHODS A total of 300 orthopantomographies (OPG) were randomly selected for this study. First, the images were visually evaluated by two calibrated operators with radiodiagnosis experience that, after consensus, established the "ground truth". Operators' findings on the radiographs were collected and classified as follows: metal restorations (MR), resin-based restorations (RR), endodontic treatment (ET), Crowns (C) and Implants (I). The orthopantomographies were then anonymously uploaded and automatically analyzed by the web-based software (Denti.Ai). Results were then stored, and a statistical analysis was performed by comparing them with the ground truth in terms of Sensitivity (S), Specificity (E), Positive Predictive Value (PPV) Negative Predictive Value (NPV) and its later representation in the area under (AUC) the Receiver Operating Characteristic (ROC) Curve. RESULTS Diagnostic metrics obtained for each study variable were as follows: (MR) S=85.48%, E=87.50%, PPV=82.8%, NPV=42.51%, AUC=0.869; (PR) S=41.11%, E=93.30%, PPV=90.24%, NPV=87.50%, AUC=0.672; (ET) S=91.9%, E=100%, PPV=100%, NPV=94.62%, AUC=0.960; (C) S=89.53%, E=95.79%, PPV=89.53%, NPV=95.79%, AUC=0.927; (I) S, E, PPV, NPV=100%, AUC=1.000. CONCLUSIONS Findings suggest that the web-based Artificial intelligence software provides a good performance on the detection of implants, crowns, metal fillings and endodontic treatments, not being so accurate on the classification of dental structures or resin-based restorations. CLINICAL SIGNIFICANCE General diagnostic and treatment decisions using orthopantomographies can be improved by using web-based artificial intelligence tools, avoiding subjectivity and lapses from the clinician.
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Affiliation(s)
- Monica Bonfanti-Gris
- Department of Conservative and Prosthetic Dentistry, Faculty of Dentistry, Complutense University of Madrid. Plaza Ramón y Cajal, s/n. 28040 Madrid, Spain
| | - Angel Garcia-Cañas
- Department of Conservative and Prosthetic Dentistry, Faculty of Dentistry, Complutense University of Madrid. Plaza Ramón y Cajal, s/n. 28040 Madrid, Spain
| | - Raul Alonso-Calvo
- Department of Informatics Systems and Languages, Faculty of Software Engineering, Polytechnic University of Madrid. Campus Montegancedo s/n, Boadilla del Monte. 28660 Madrid, Spain
| | - Maria Paz Salido Rodriguez-Manzaneque
- Department of Conservative and Prosthetic Dentistry, Faculty of Dentistry, Complutense University of Madrid. Plaza Ramón y Cajal, s/n. 28040 Madrid, Spain.
| | - Guillermo Pradies Ramiro
- Department of Conservative and Prosthetic Dentistry, Faculty of Dentistry, Complutense University of Madrid. Plaza Ramón y Cajal, s/n. 28040 Madrid, Spain
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Guo J, Tsai PW, Xue X, Wu D, Van QT, Kaluarachchi CN, Dang HT, Chintha N. TVGG Dental Implant Identification System. Front Pharmacol 2022; 13:948283. [PMID: 36003505 PMCID: PMC9393209 DOI: 10.3389/fphar.2022.948283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 06/09/2022] [Indexed: 11/13/2022] Open
Abstract
Identifying the right accessories for installing the dental implant is a vital element that impacts the sustainability and the reliability of the dental prosthesis when the medical case of a patient is not comprehensive. Dentists need to identify the implant manufacturer from the x-ray image to determine further treatment procedures. Identifying the manufacturer is a high-pressure task under the scaling volume of patients pending in the queue for treatment. To reduce the burden on the doctors, a dental implant identification system is built based on a new proposed thinner VGG model with an on-demand client-server structure. We propose a thinner version of VGG16 called TVGG by reducing the number of neurons in the dense layers to improve the system’s performance and gain advantages from the limited texture and patterns in the dental radiography images. The outcome of the proposed system is compared with the original pre-trained VGG16 to verify the usability of the proposed system.
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Affiliation(s)
- Jianbin Guo
- Fujian Key Laboratory of Oral Diseases and Fujian Provincial Engineering Research Center of Oral Biomaterial and Stomatological Key Lab of Fujian College and University, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
- *Correspondence: Jianbin Guo,
| | - Pei-Wei Tsai
- Department of Computing Technologies, Swinburne University of Technology, Hawthorn, VIC, Australia
| | - Xingsi Xue
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China
| | - Dong Wu
- Institute of Stomatology and Research Center of Dental and Craniofacial Implants, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China
| | - Qui Tran Van
- Department of Computing Technologies, Swinburne University of Technology, Hawthorn, VIC, Australia
| | | | - Hong Thi Dang
- Department of Computing Technologies, Swinburne University of Technology, Hawthorn, VIC, Australia
| | - Nikhitha Chintha
- Department of Computing Technologies, Swinburne University of Technology, Hawthorn, VIC, Australia
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Sukegawa S, Tanaka F, Nakano K, Hara T, Yoshii K, Yamashita K, Ono S, Takabatake K, Kawai H, Nagatsuka H, Furuki Y. Effective deep learning for oral exfoliative cytology classification. Sci Rep 2022; 12:13281. [PMID: 35918498 PMCID: PMC9346110 DOI: 10.1038/s41598-022-17602-4] [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: 02/20/2022] [Accepted: 07/28/2022] [Indexed: 12/24/2022] Open
Abstract
The use of sharpness aware minimization (SAM) as an optimizer that achieves high performance for convolutional neural networks (CNNs) is attracting attention in various fields of deep learning. We used deep learning to perform classification diagnosis in oral exfoliative cytology and to analyze performance, using SAM as an optimization algorithm to improve classification accuracy. The whole image of the oral exfoliation cytology slide was cut into tiles and labeled by an oral pathologist. CNN was VGG16, and stochastic gradient descent (SGD) and SAM were used as optimizers. Each was analyzed with and without a learning rate scheduler in 300 epochs. The performance metrics used were accuracy, precision, recall, specificity, F1 score, AUC, and statistical and effect size. All optimizers performed better with the rate scheduler. In particular, the SAM effect size had high accuracy (11.2) and AUC (11.0). SAM had the best performance of all models with a learning rate scheduler. (AUC = 0.9328) SAM tended to suppress overfitting compared to SGD. In oral exfoliation cytology classification, CNNs using SAM rate scheduler showed the highest classification performance. These results suggest that SAM can play an important role in primary screening of the oral cytological diagnostic environment.
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Affiliation(s)
- Shintaro Sukegawa
- Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa, 760-8557, Japan. .,Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan.
| | - Futa Tanaka
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
| | - Keisuke Nakano
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Takeshi Hara
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan.,Center for Healthcare Information Technology, Tokai National Higher Education and Research System, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
| | - Kazumasa Yoshii
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu, 501-1193, Japan
| | | | - Sawako Ono
- Department of Pathology, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa, 760-8557, Japan
| | - Kiyofumi Takabatake
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Hotaka Kawai
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Hitoshi Nagatsuka
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Yoshihiko Furuki
- Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa, 760-8557, Japan
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Sukegawa S, Yoshii K, Hara T, Tanaka F, Yamashita K, Kagaya T, Nakano K, Takabatake K, Kawai H, Nagatsuka H, Furuki Y. Is attention branch network effective in classifying dental implants from panoramic radiograph images by deep learning? PLoS One 2022; 17:e0269016. [PMID: 35895591 PMCID: PMC9328496 DOI: 10.1371/journal.pone.0269016] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 05/13/2022] [Indexed: 11/19/2022] Open
Abstract
Attention mechanism, which is a means of determining which part of the forced data is emphasized, has attracted attention in various fields of deep learning in recent years. The purpose of this study was to evaluate the performance of the attention branch network (ABN) for implant classification using convolutional neural networks (CNNs). The data consisted of 10191 dental implant images from 13 implant brands that cropped the site, including dental implants as pretreatment, from digital panoramic radiographs of patients who underwent surgery at Kagawa Prefectural Central Hospital between 2005 and 2021. ResNet 18, 50, and 152 were evaluated as CNN models that were compared with and without the ABN. We used accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristics curve as performance metrics. We also performed statistical and effect size evaluations of the 30-time performance metrics of the simple CNNs and the ABN model. ResNet18 with ABN significantly improved the dental implant classification performance for all the performance metrics. Effect sizes were equivalent to “Huge” for all performance metrics. In contrast, the classification performance of ResNet50 and 152 deteriorated by adding the attention mechanism. ResNet18 showed considerably high compatibility with the ABN model in dental implant classification (AUC = 0.9993) despite the small number of parameters. The limitation of this study is that only ResNet was verified as a CNN; further studies are required for other CNN models.
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Affiliation(s)
- Shintaro Sukegawa
- Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, Takamatsu, Kagawa, Japan
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
- * E-mail:
| | - Kazumasa Yoshii
- Department of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, Gifu University, Gifu, Japan
| | - Takeshi Hara
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, Gifu, Gifu, Japan
- Center for Healthcare Information Technology, Tokai National Higher Education and Research System, Gifu, Gifu, Japan
| | - Futa Tanaka
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, Gifu, Gifu, Japan
| | | | - Tutaro Kagaya
- Department of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, Gifu University, Gifu, Japan
| | - Keisuke Nakano
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Kiyofumi Takabatake
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Hotaka Kawai
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Hitoshi Nagatsuka
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan
| | - Yoshihiko Furuki
- Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, Takamatsu, Kagawa, Japan
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Choudhury S, Rana M, Chakraborty A, Majumder S, Roy S, RoyChowdhury A, Datta S. Design of patient specific basal dental implant using Finite Element method and Artificial Neural Network technique. Proc Inst Mech Eng H 2022; 236:1375-1387. [PMID: 35880901 DOI: 10.1177/09544119221114729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The bone conditions of mandibular bone vary from patient to patient, and as a result, a patient-specific dental implant needs to be designed. The basal dental implant is implanted in the cortical region of the bone since the top surface of the bone narrows down because of aging. Taguchi designs of experiments technique are used in which 25 optimum solid models of basal dental implants are modeled with variable geometrical parameters, viz. thread length, diameter, and pitch. In the solid models the implants are placed in the cortical part of the 3D models of cadaveric mandibles, that are prepared from CT data using image processing software. Patient-specific bone conditions are varied according to the strong, weak, and normal basal bone. A compressive force of 200 N is applied on the top surface of these implants and using finite element analysis software, the microstrain on the peri-implant bone ranges from 1000 to 4000 depending on the various bone conditions. According to the finite element data, it can be concluded that weak bone microstrain is comparatively high compared with normal and strong bone conditions. A surrogate artificial neural network model is prepared from the finite element analysis data. Surrogate model assisted genetic algorithm is used to find the optimum patient-specific basal dental implant for a better osseointegration-friendly mechanical environment.
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Affiliation(s)
- Sandeep Choudhury
- Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India
| | - Masud Rana
- Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India
| | - Arindam Chakraborty
- Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India
| | - Santanu Majumder
- Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India
| | - Sandipan Roy
- Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
| | - Amit RoyChowdhury
- Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Howrah, West Bengal, India
| | - Shubhabrata Datta
- Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
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Mohammad-Rahimi H, Motamedian SR, Pirayesh Z, Haiat A, Zahedrozegar S, Mahmoudinia E, Rohban MH, Krois J, Lee JH, Schwendicke F. Deep learning in periodontology and oral implantology: A scoping review. J Periodontal Res 2022; 57:942-951. [PMID: 35856183 DOI: 10.1111/jre.13037] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 06/08/2022] [Accepted: 07/07/2022] [Indexed: 12/20/2022]
Abstract
Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal and implantological purposes. A systematic electronic search was conducted on four databases (Medline via PubMed, Google Scholar, Scopus, and Embase) and a repository (ArXiv) for publications after 2010, without any limitation on language. In the present review, we included studies that reported deep learning models' performance on periodontal or oral implantological tasks. Given the heterogeneities in the included studies, no meta-analysis was performed. The risk of bias was assessed using the QUADAS-2 tool. We included 47 studies: focusing on imaging data (n = 20) and non-imaging data in periodontology (n = 12), or dental implantology (n = 15). The detection of periodontitis and gingivitis or periodontal bone loss, the classification of dental implant systems, or the prediction of treatment outcomes in periodontology and implantology were major use cases. The performance of the models was generally high. However, it varied given the employed methods (which includes various types of convolutional neural networks (CNN) and multi-layered perceptron (MLP)), the variety in specific modeling tasks, as well as the chosen and reported outcomes, outcome measures and outcome level. Only a few studies (n = 7) showed a low risk of bias across all assessed domains. A growing number of studies evaluated DL for periodontal or implantological objectives. Heterogeneity in study design, poor reporting and a high risk of bias severely limit the comparability of studies and the robustness of the overall evidence.
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Affiliation(s)
- Hossein Mohammad-Rahimi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.,Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Saeed Reza Motamedian
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.,Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zeynab Pirayesh
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Anahita Haiat
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Samira Zahedrozegar
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Erfan Mahmoudinia
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | | | - 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
| | - Jae-Hong Lee
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.,Department of Periodontology, Daejeon Dental Hospital, Institute of Wonkwang Dental Research, Wonkwang University College of Dentistry, Daejeon, South Korea
| | - 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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42
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Potential and impact of artificial intelligence algorithms in dento-maxillofacial radiology. Clin Oral Investig 2022; 26:5535-5555. [PMID: 35438326 DOI: 10.1007/s00784-022-04477-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 03/25/2022] [Indexed: 12/20/2022]
Abstract
OBJECTIVES Novel artificial intelligence (AI) learning algorithms in dento-maxillofacial radiology (DMFR) are continuously being developed and improved using advanced convolutional neural networks. This review provides an overview of the potential and impact of AI algorithms in DMFR. MATERIALS AND METHODS A narrative review was conducted on the literature on AI algorithms in DMFR. RESULTS In the field of DMFR, AI algorithms were mainly proposed for (1) automated detection of dental caries, periapical pathologies, root fracture, periodontal/peri-implant bone loss, and maxillofacial cysts/tumors; (2) classification of mandibular third molars, skeletal malocclusion, and dental implant systems; (3) localization of cephalometric landmarks; and (4) improvement of image quality. Data insufficiency, overfitting, and the lack of interpretability are the main issues in the development and use of image-based AI algorithms. Several strategies have been suggested to address these issues, such as data augmentation, transfer learning, semi-supervised training, few-shot learning, and gradient-weighted class activation mapping. CONCLUSIONS Further integration of relevant AI algorithms into one fully automatic end-to-end intelligent system for possible multi-disciplinary applications is very likely to be a field of increased interest in the future. CLINICAL RELEVANCE This review provides dental practitioners and researchers with a comprehensive understanding of the current development, performance, issues, and prospects of image-based AI algorithms in DMFR.
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Sukegawa S, Fujimura A, Taguchi A, Yamamoto N, Kitamura A, Goto R, Nakano K, Takabatake K, Kawai H, Nagatsuka H, Furuki Y. Identification of osteoporosis using ensemble deep learning model with panoramic radiographs and clinical covariates. Sci Rep 2022; 12:6088. [PMID: 35413983 PMCID: PMC9005660 DOI: 10.1038/s41598-022-10150-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 03/25/2022] [Indexed: 11/18/2022] Open
Abstract
Osteoporosis is becoming a global health issue due to increased life expectancy. However, it is difficult to detect in its early stages owing to a lack of discernible symptoms. Hence, screening for osteoporosis with widely used dental panoramic radiographs would be very cost-effective and useful. In this study, we investigate the use of deep learning to classify osteoporosis from dental panoramic radiographs. In addition, the effect of adding clinical covariate data to the radiographic images on the identification performance was assessed. For objective labeling, a dataset containing 778 images was collected from patients who underwent both skeletal-bone-mineral density measurement and dental panoramic radiography at a single general hospital between 2014 and 2020. Osteoporosis was assessed from the dental panoramic radiographs using convolutional neural network (CNN) models, including EfficientNet-b0, -b3, and -b7 and ResNet-18, -50, and -152. An ensemble model was also constructed with clinical covariates added to each CNN. The ensemble model exhibited improved performance on all metrics for all CNNs, especially accuracy and AUC. The results show that deep learning using CNN can accurately classify osteoporosis from dental panoramic radiographs. Furthermore, it was shown that the accuracy can be improved using an ensemble model with patient covariates.
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Affiliation(s)
- Shintaro Sukegawa
- Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa, 760-8557, Japan. .,Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan.
| | - Ai Fujimura
- Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa, 760-8557, Japan
| | - Akira Taguchi
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Matsumoto Dental University, 1780 Hirooka Gobara, Shiojiri, Nagano, 399-0781, Japan
| | - Norio Yamamoto
- Department of Epidemiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | | | | | - Keisuke Nakano
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Kiyofumi Takabatake
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Hotaka Kawai
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Hitoshi Nagatsuka
- Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan
| | - Yoshihiko Furuki
- Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa, 760-8557, Japan
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SCHNYDER JASON D A, KRİSHNAN V, VİNAYACHANDRAN D. Intelligent systems for precision dental diagnosis and treatment planning – A review. CUMHURIYET DENTAL JOURNAL 2022. [DOI: 10.7126/cumudj.991480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Machines have changed the course of mankind. Simple machines were the basis of human civilization. Today with humongous technological development, machines are intelligent enough to carry out very complex nerve-racking tasks. The ability of a machine to learn from algorithms changed eventually into, the machine learning by itself, which constitutes artificial intelligence. Literature has plausible evidence for the use of intelligent systems in medical field. Artificial intelligence has been used in the multiple denominations of dentistry. These machines are used in the precision diagnosis, interpretation of medical images, accumulation of data, classification and compilation of records, determination of treatment and construction of a personalized treatment plan. Artificial intelligence can help in timely diagnosis of complex dental diseases which would ultimately aid in rapid commencement of treatment. Research helps us understand the effectiveness and challenges in the use of this technology. The apt use of intelligent systems could transform the entire medical system for the better.
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Affiliation(s)
| | - Vidya KRİSHNAN
- SRM Kattankulathur Dental College, SRM Institute of Science and Technology
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Karacı A. VGGCOV19-NET: automatic detection of COVID-19 cases from X-ray images using modified VGG19 CNN architecture and YOLO algorithm. Neural Comput Appl 2022; 34:8253-8274. [PMID: 35095212 PMCID: PMC8785935 DOI: 10.1007/s00521-022-06918-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 01/04/2022] [Indexed: 01/09/2023]
Abstract
X-ray images are an easily accessible, fast, and inexpensive method of diagnosing COVID-19, widely used in health centers around the world. In places where there is a shortage of specialist doctors and radiologists, there is need for a system that can direct patients to advanced health centers by pre-diagnosing COVID-19 from X-ray images. Also, smart computer-aided systems that automatically detect COVID-19 positive cases will support daily clinical applications. The study aimed to classify COVID-19 via X-ray images in high precision ratios with pre-trained VGG19 deep CNN architecture and the YOLOv3 detection algorithm. For this purpose, VGG19, VGGCOV19-NET models, and the original Cascade models were created by feeding these models with the YOLOv3 algorithm. Cascade models are the original models fed with the lung zone X-ray images detected with the YOLOv3 algorithm. Model performances were evaluated using fivefold cross-validation according to recall, specificity, precision, f1-score, confusion matrix, and ROC analysis performance metrics. While the accuracy of the Cascade VGGCOV19-NET model was 99.84% for the binary class (COVID vs. no-findings) data set, it was 97.16% for the three-class (COVID vs. no-findings vs. pneumonia) data set. The Cascade VGGCOV19-NET model has a higher classification performance than VGG19, Cascade VGG19, VGGCOV19-NET and previous studies. Feeding the CNN models with the YOLOv3 detection algorithm decreases the training test time while increasing the classification performance. The results indicate that the proposed Cascade VGGCOV19-NET architecture was highly successful in detecting COVID-19. Therefore, this study contributes to the literature in terms of both YOLO-aided deep architecture and classification success.
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Affiliation(s)
- Abdulkadir Karacı
- Faculty of Engineering and Architecture, Computer Engineering, Kastamonu University, 37200 Kastamonu, Turkey
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Diagnosis of middle cerebral artery stenosis using the transcranial Doppler images based on convolutional neural network. World Neurosurg 2022; 161:e118-e125. [PMID: 35077885 DOI: 10.1016/j.wneu.2022.01.068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 01/16/2022] [Accepted: 01/17/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND The purpose of this study was to explore the diagnostic value of convolutional neural networks (CNNs) in middle cerebral artery (MCA) stenosis by analyzing the transcranial Doppler (TCD) images. METHODS Overall 278 patients who underwent cerebral vascular TCD and cerebral angiography were enrolled and classified into stenosis and non-stenosis groups based on cerebral angiography findings. Manual measurements were performed on TCD images. The patients were divided into a training set and a test set, and the CNNs architecture was used to classify TCD images. The diagnostic accuracies of manual measurements, CNNs, and TCD parameters for MCA stenosis were calculated and compared. RESULTS Overall, 203 patients without stenosis and 75 patients with stenosis were evaluated. The sensitivity, specificity, and area under the curve (AUC) for manual measurements of MCA stenosis were 0.80, 0.83, and 0.81, respectively. After 24 iterations of the running model in the training set, the sensitivity, specificity, and AUC of the CNNs in the test set were 0.84, 0.86, and 0.80, respectively. The diagnostic value of CNNs differed minimally from that of manual measurements. Two parameters of TCD, peak systolic velocity and mean flow velocity, were higher in patients with stenosis than in those without stenosis; however, their diagnostic values were significantly lower than those of CNNs (P < 0.05). CONCLUSIONS The diagnostic value of CNNs for MCA stenosis based on TCD images paralleled that of manual measurements. CNNs could be used as an auxiliary diagnostic tool to improve the diagnosis of MCA stenosis.
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Liu M, Wang S, Chen H, Liu Y. A pilot study of a deep learning approach to detect marginal bone loss around implants. BMC Oral Health 2022; 22:11. [PMID: 35034611 PMCID: PMC8762847 DOI: 10.1186/s12903-021-02035-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 12/28/2021] [Indexed: 01/17/2023] Open
Abstract
Background Recently, there has been considerable innovation in artificial intelligence (AI) for healthcare. Convolutional neural networks (CNNs) show excellent object detection and classification performance. This study assessed the accuracy of an artificial intelligence (AI) application for the detection of marginal bone loss on periapical radiographs. Methods A Faster region-based convolutional neural network (R-CNN) was trained. Overall, 1670 periapical radiographic images were divided into training (n = 1370), validation (n = 150), and test (n = 150) datasets. The system was evaluated in terms of sensitivity, specificity, the mistake diagnostic rate, the omission diagnostic rate, and the positive predictive value. Kappa (κ) statistics were compared between the system and dental clinicians. Results Evaluation metrics of AI system is equal to resident dentist. The agreement between the AI system and expert is moderate to substantial (κ = 0.547 and 0.568 for bone loss sites and bone loss implants, respectively) for detecting marginal bone loss around dental implants. Conclusions This AI system based on Faster R-CNN analysis of periapical radiographs is a highly promising auxiliary diagnostic tool for peri-implant bone loss detection.
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Affiliation(s)
- Min Liu
- Department of Prosthodontics, Peking University School and Hospital of Stomatology and National Engineering Laboratory for Digital and Material Technology of Stomatology and Research Center of Engineering and Technology for Digital Dentistry of Ministry of Health and Beijing Key Laboratory of Digital Stomatology and National Clinical Research Center for Oral Diseases, 22 ZhongguancunNandajie, Haidian District, Beijing, 100081, China
| | - Shimin Wang
- Department of Prosthodontics, Peking University School and Hospital of Stomatology and National Engineering Laboratory for Digital and Material Technology of Stomatology and Research Center of Engineering and Technology for Digital Dentistry of Ministry of Health and Beijing Key Laboratory of Digital Stomatology and National Clinical Research Center for Oral Diseases, 22 ZhongguancunNandajie, Haidian District, Beijing, 100081, China
| | - Hu Chen
- Department of Prosthodontics, Peking University School and Hospital of Stomatology and National Engineering Laboratory for Digital and Material Technology of Stomatology and Research Center of Engineering and Technology for Digital Dentistry of Ministry of Health and Beijing Key Laboratory of Digital Stomatology and National Clinical Research Center for Oral Diseases, 22 ZhongguancunNandajie, Haidian District, Beijing, 100081, China.
| | - Yunsong Liu
- Department of Prosthodontics, Peking University School and Hospital of Stomatology and National Engineering Laboratory for Digital and Material Technology of Stomatology and Research Center of Engineering and Technology for Digital Dentistry of Ministry of Health and Beijing Key Laboratory of Digital Stomatology and National Clinical Research Center for Oral Diseases, 22 ZhongguancunNandajie, Haidian District, Beijing, 100081, China.
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Sultan H, Owais M, Choi J, Mahmood T, Haider A, Ullah N, Park KR. Artificial Intelligence-Based Solution in Personalized Computer-Aided Arthroscopy of Shoulder Prostheses. J Pers Med 2022; 12:jpm12010109. [PMID: 35055427 PMCID: PMC8780458 DOI: 10.3390/jpm12010109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 12/30/2021] [Accepted: 01/07/2022] [Indexed: 01/01/2023] Open
Abstract
Background: Early recognition of prostheses before reoperation can reduce perioperative morbidity and mortality. Because of the intricacy of the shoulder biomechanics, accurate classification of implant models before surgery is fundamental for planning the correct medical procedure and setting apparatus for personalized medicine. Expert surgeons usually use X-ray images of prostheses to set the patient-specific apparatus. However, this subjective method is time-consuming and prone to errors. Method: As an alternative, artificial intelligence has played a vital role in orthopedic surgery and clinical decision-making for accurate prosthesis placement. In this study, three different deep learning-based frameworks are proposed to identify different types of shoulder implants in X-ray scans. We mainly propose an efficient ensemble network called the Inception Mobile Fully-Connected Convolutional Network (IMFC-Net), which is comprised of our two designed convolutional neural networks and a classifier. To evaluate the performance of the IMFC-Net and state-of-the-art models, experiments were performed with a public data set of 597 de-identified patients (597 shoulder implants). Moreover, to demonstrate the generalizability of IMFC-Net, experiments were performed with two augmentation techniques and without augmentation, in which our model ranked first, with a considerable difference from the comparison models. A gradient-weighted class activation map technique was also used to find distinct implant characteristics needed for IMFC-Net classification decisions. Results: The results confirmed that the proposed IMFC-Net model yielded an average accuracy of 89.09%, a precision rate of 89.54%, a recall rate of 86.57%, and an F1.score of 87.94%, which were higher than those of the comparison models. Conclusion: The proposed model is efficient and can minimize the revision complexities of implants.
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Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars. Sci Rep 2022; 12:684. [PMID: 35027629 PMCID: PMC8758752 DOI: 10.1038/s41598-021-04603-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 12/21/2021] [Indexed: 01/18/2023] Open
Abstract
Pell and Gregory, and Winter's classifications are frequently implemented to classify the mandibular third molars and are crucial for safe tooth extraction. This study aimed to evaluate the classification accuracy of convolutional neural network (CNN) deep learning models using cropped panoramic radiographs based on these classifications. We compared the diagnostic accuracy of single-task and multi-task learning after labeling 1330 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014-2021). The mandibular third molar classifications were analyzed using a VGG 16 model of a CNN. We statistically evaluated performance metrics [accuracy, precision, recall, F1 score, and area under the curve (AUC)] for each prediction. We found that single-task learning was superior to multi-task learning (all p < 0.05) for all metrics, with large effect sizes and low p-values. Recall and F1 scores for position classification showed medium effect sizes in single and multi-task learning. To our knowledge, this is the first deep learning study to examine single-task and multi-task learning for the classification of mandibular third molars. Our results demonstrated the efficacy of implementing Pell and Gregory, and Winter's classifications for specific respective tasks.
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Dental Material Detection based on Faster Regional Convolutional Neural Networks and Shape Features. Neural Process Lett 2022. [DOI: 10.1007/s11063-021-10721-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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