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Obwegeser D, Timofte R, Mayer C, Bornstein MM, Schätzle MA, Patcas R. Scoring facial attractiveness with deep convolutional neural networks: How training on standardized images reduces the bias of facial expressions. Orthod Craniofac Res 2024. [PMID: 38825845 DOI: 10.1111/ocr.12820] [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] [Accepted: 05/17/2024] [Indexed: 06/04/2024]
Abstract
OBJECTIVE In many medical disciplines, facial attractiveness is part of the diagnosis, yet its scoring might be confounded by facial expressions. The intent was to apply deep convolutional neural networks (CNN) to identify how facial expressions affect facial attractiveness and to explore whether a dedicated training of the CNN is able to reduce the bias of facial expressions. MATERIALS AND METHODS Frontal facial images (n = 840) of 40 female participants (mean age 24.5 years) were taken adapting a neutral facial expression and the six universal facial expressions. Facial attractiveness was computed by means of a face detector, deep convolutional neural networks, standard support vector regression for facial beauty, visual regularized collaborative filtering and a regression technique for handling visual queries without rating history. CNN was first trained on random facial photographs from a dating website and then further trained on the Chicago Face Database (CFD) to increase its suitability to medical conditions. Both algorithms scored every image for attractiveness. RESULTS Facial expressions affect facial attractiveness scores significantly. Scores from CNN additionally trained on CFD had less variability between the expressions (range 54.3-60.9 compared to range: 32.6-49.5) and less variance within the scores (P ≤ .05), but also caused a shift in the ranking of the expressions' facial attractiveness. CONCLUSION Facial expressions confound attractiveness scores. Training on norming images generated scores less susceptible to distortion, but more difficult to interpret. Scoring facial attractiveness based on CNN seems promising, but AI solutions must be developed on CNN trained to recognize facial expressions as distractors.
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Affiliation(s)
- Dorothea Obwegeser
- Clinic of Orthodontics and Pediatric Dentistry, Center of Dental Medicine, University of Zurich, Zurich, Switzerland
| | - Radu Timofte
- Computer Vision Laboratory, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
- CAIDAS and Institute of Computer Science, Faculty of Mathematics and Computer Science, University of Wurzburg, Wurzburg, Germany
| | - Christoph Mayer
- Computer Vision Laboratory, Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland
| | - Michael M Bornstein
- Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland
| | - Marc A Schätzle
- Clinic of Orthodontics and Pediatric Dentistry, Center of Dental Medicine, University of Zurich, Zurich, Switzerland
| | - Raphael Patcas
- Clinic of Orthodontics and Pediatric Dentistry, Center of Dental Medicine, University of Zurich, Zurich, Switzerland
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Daraqel B, Wafaie K, Mohammed H, Cao L, Mheissen S, Liu Y, Zheng L. The performance of artificial intelligence models in generating responses to general orthodontic questions: ChatGPT vs Google Bard. Am J Orthod Dentofacial Orthop 2024; 165:652-662. [PMID: 38493370 DOI: 10.1016/j.ajodo.2024.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 01/01/2024] [Accepted: 01/01/2024] [Indexed: 03/18/2024]
Abstract
INTRODUCTION This study aimed to evaluate and compare the performance of 2 artificial intelligence (AI) models, Chat Generative Pretrained Transformer-3.5 (ChatGPT-3.5; OpenAI, San Francisco, Calif) and Google Bidirectional Encoder Representations from Transformers (Google Bard; Bard Experiment, Google, Mountain View, Calif), in terms of response accuracy, completeness, generation time, and response length when answering general orthodontic questions. METHODS A team of orthodontic specialists developed a set of 100 questions in 10 orthodontic domains. One author submitted the questions to both ChatGPT and Google Bard. The AI-generated responses from both models were randomly assigned into 2 forms and sent to 5 blinded and independent assessors. The quality of AI-generated responses was evaluated using a newly developed tool for accuracy of information and completeness. In addition, response generation time and length were recorded. RESULTS The accuracy and completeness of responses were high in both AI models. The median accuracy score was 9 (interquartile range [IQR]: 8-9) for ChatGPT and 8 (IQR: 8-9) for Google Bard (Median difference: 1; P <0.001). The median completeness score was similar in both models, with 8 (IQR: 8-9) for ChatGPT and 8 (IQR: 7-9) for Google Bard. The odds of accuracy and completeness were higher by 31% and 23% in ChatGPT than in Google Bard. Google Bard's response generation time was significantly shorter than that of ChatGPT by 10.4 second/question. However, both models were similar in terms of response length generation. CONCLUSIONS Both ChatGPT and Google Bard generated responses were rated with a high level of accuracy and completeness to the posed general orthodontic questions. However, acquiring answers was generally faster using the Google Bard model.
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Affiliation(s)
- Baraa Daraqel
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University Chongqing Key Laboratory of Oral Disease and Biomedical Sciences Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China; Oral Health Research and Promotion Unit, Al-Quds University, Jerusalem, Palestine.
| | - Khaled Wafaie
- Department of Orthodontics, Faculty of Dentistry, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | | | - Li Cao
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University Chongqing Key Laboratory of Oral Disease and Biomedical Sciences Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China
| | | | - Yang Liu
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University Chongqing Key Laboratory of Oral Disease and Biomedical Sciences Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China
| | - Leilei Zheng
- Department of Orthodontics, Stomatological Hospital of Chongqing Medical University Chongqing Key Laboratory of Oral Disease and Biomedical Sciences Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China.
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Boubaris M, Cameron A, Manakil J, George R. Artificial intelligence vs. semi-automated segmentation for assessment of dental periapical lesion volume index score: A cone-beam CT study. Comput Biol Med 2024; 175:108527. [PMID: 38714047 DOI: 10.1016/j.compbiomed.2024.108527] [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: 02/02/2024] [Revised: 04/26/2024] [Accepted: 04/26/2024] [Indexed: 05/09/2024]
Abstract
INTRODUCTION Cone beam computed tomography periapical volume index (CBCTPAVI) is a categorisation tool to assess periapical lesion size in three-dimensions and predict treatment outcomes. This index was determined using a time-consuming semi-automatic segmentation technique. This study compared artificial intelligence (AI) with semi-automated segmentation to determine AI's ability to accurately determine CBCTPAVI score. METHODS CBCTPAVI scores for 500 tooth roots were determined using both the semi-automatic segmentation technique in three-dimensional imaging analysis software (Mimics Research™) and AI (Diagnocat™). A confusion matrix was created to compare the CBCTPAVI score by the AI with the semi-automatic segmentation technique. Evaluation metrics, precision, recall, F1-score (2×precision×recallprecision+recall), and overall accuracy were determined. RESULTS In 84.4 % (n = 422) of cases the AI classified CBCTPAVI score the same as the semi-automated technique. AI was unable to classify any lesion as index 1 or 2, due to its limitation in small volume measurement. When lesions classified as index 1 and 2 by the semi-automatic segmentation technique were excluded, the AI demonstrated levels of precision, recall and F1-score, all above 0.85, for indices 0, 3-6; and accuracy over 90 %. CONCLUSIONS Diagnocat™ with its ability to determine CBCTPAVI score in approximately 2 min following upload of the CBCT could be an excellent and efficient tool to facilitate better monitoring and assessment of periapical lesions in everyday clinical practice and/or radiographic reporting. However, to assess three-dimensional healing of smaller lesions (with scores 1 and 2), further advancements in AI technologies are needed.
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Affiliation(s)
- Matthew Boubaris
- School of Medicine and Dentistry, Griffith University, Gold Coast, Australia
| | - Andrew Cameron
- School of Medicine and Dentistry, Griffith University, Gold Coast, Australia
| | - Jane Manakil
- School of Medicine and Dentistry, Griffith University, Gold Coast, Australia
| | - Roy George
- School of Medicine and Dentistry, Griffith University, Gold Coast, Australia.
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Wang J, Wang B, Liu YY, Luo YL, Wu YY, Xiang L, Yang XM, Qu YL, Tian TR, Man Y. Recent Advances in Digital Technology in Implant Dentistry. J Dent Res 2024:220345241253794. [PMID: 38822563 DOI: 10.1177/00220345241253794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2024] Open
Abstract
Digital technology has emerged as a transformative tool in dental implantation, profoundly enhancing accuracy and effectiveness across multiple facets, such as diagnosis, preoperative treatment planning, surgical procedures, and restoration delivery. The multiple integration of radiographic data and intraoral data, sometimes with facial scan data or electronic facebow through virtual planning software, enables comprehensive 3-dimensional visualization of the hard and soft tissue and the position of future restoration, resulting in heightened diagnostic precision. In virtual surgery design, the incorporation of both prosthetic arrangement and individual anatomical details enables the virtual execution of critical procedures (e.g., implant placement, extended applications, etc.) through analysis of cross-sectional images and the reconstruction of 3-dimensional surface models. After verification, the utilization of digital technology including templates, navigation, combined techniques, and implant robots achieved seamless transfer of the virtual treatment plan to the actual surgical sites, ultimately leading to enhanced surgical outcomes with highly improved accuracy. In restoration delivery, digital techniques for impression, shade matching, and prosthesis fabrication have advanced, enabling seamless digital data conversion and efficient communication among clinicians and technicians. Compared with clinical medicine, artificial intelligence (AI) technology in dental implantology primarily focuses on diagnosis and prediction. AI-supported preoperative planning and surgery remain in developmental phases, impeded by the complexity of clinical cases and ethical considerations, thereby constraining widespread adoption.
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Affiliation(s)
- J Wang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - B Wang
- Department of Stomatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Sichuan, Henan
| | - Y Y Liu
- Department of Oral Implantology, The Affiliated Stomatological Hospital of Kunming Medical University, Kunming, Yunnan, Sichuan, China
| | - Y L Luo
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y Y Wu
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - L Xiang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - X M Yang
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y L Qu
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - T R Tian
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
| | - Y Man
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
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Chen S, Yang Y, Wu W, Wei R, Wang Z, Tay FR, Hu J, Ma J. Classification of Caries Based on CBCT: A Deep Learning Network Interpretability Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01143-5. [PMID: 38806951 DOI: 10.1007/s10278-024-01143-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 04/16/2024] [Accepted: 05/16/2024] [Indexed: 05/30/2024]
Abstract
This study aimed to create a caries classification scheme based on cone-beam computed tomography (CBCT) and develop two deep learning models to improve caries classification accuracy. A total of 2713 axial slices were obtained from CBCT images of 204 carious teeth. Both classification models were trained and tested using the same pretrained classification networks on the dataset, including ResNet50_vd, MobileNetV3_large_ssld, and ResNet50_vd_ssld. The first model was used directly to classify the original images (direct classification model). The second model incorporated a presegmentation step for interpretation (interpretable classification model). Performance evaluation metrics including accuracy, precision, recall, and F1 score were calculated. The Local Interpretable Model-agnostic Explanations (LIME) method was employed to elucidate the decision-making process of the two models. In addition, a minimum distance between caries and pulp was introduced for determining the treatment strategies for type II carious teeth. The direct model that utilized the ResNet50_vd_ssld network achieved top accuracy, precision, recall, and F1 score of 0.700, 0.786, 0.606, and 0.616, respectively. Conversely, the interpretable model consistently yielded metrics surpassing 0.917, irrespective of the network employed. The LIME algorithm confirmed the interpretability of the classification models by identifying key image features for caries classification. Evaluation of treatment strategies for type II carious teeth revealed a significant negative correlation (p < 0.01) with the minimum distance. These results demonstrated that the CBCT-based caries classification scheme and the two classification models appeared to be acceptable tools for the diagnosis and categorization of dental caries.
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Affiliation(s)
- Surong Chen
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Yan Yang
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Weiwei Wu
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Ruonan Wei
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Zezhou Wang
- West China School of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Franklin R Tay
- Department of Endodontics, Dental College of Georgia, Augusta University, Augusta, GA, USA
| | - Jingyu Hu
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China.
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China.
| | - Jingzhi Ma
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China.
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China.
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Zayed SO, Abd-Rabou RYM, Abdelhameed GM, Abdelhamid Y, Khairy K, Abulnoor BA, Ibrahim SH, Khaled H. The innovation of AI-based software in oral diseases: clinical-histopathological correlation diagnostic accuracy primary study. BMC Oral Health 2024; 24:598. [PMID: 38778322 PMCID: PMC11112957 DOI: 10.1186/s12903-024-04347-x] [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: 03/14/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Machine learning (ML) through artificial intelligence (AI) could provide clinicians and oral pathologists to advance diagnostic problems in the field of potentially malignant lesions, oral cancer, periodontal diseases, salivary gland disease, oral infections, immune-mediated disease, and others. AI can detect micro-features beyond human eyes and provide solution in critical diagnostic cases. OBJECTIVE The objective of this study was developing a software with all needed feeding data to act as AI-based program to diagnose oral diseases. So our research question was: Can we develop a Computer-Aided Software for accurate diagnosis of oral diseases based on clinical and histopathological data inputs? METHOD The study sample included clinical images, patient symptoms, radiographic images, histopathological images and texts for the oral diseases of interest in the current study (premalignant lesions, oral cancer, salivary gland neoplasms, immune mediated oral mucosal lesions, oral reactive lesions) total oral diseases enrolled in this study was 28 diseases retrieved from the archives of oral maxillofacial pathology department. Total 11,200 texts and 3000 images (2800 images were used for training data to the program and 100 images were used as test data to the program and 100 cases for calculating accuracy, sensitivity& specificity). RESULTS The correct diagnosis rates for group 1 (software users), group 2 (microscopic users) and group 3 (hybrid) were 87%, 90.6, 95% respectively. The reliability for inter-observer value was done by calculating Cronbach's alpha and interclass correlation coefficient. The test revealed for group 1, 2 and 3 the following values respectively 0.934, 0.712 & 0.703. All groups showed acceptable reliability especially for Diagnosis Oral Diseases Software (DODS) that revealed higher reliability value than other groups. However, The accuracy, sensitivity & specificity of this software was lower than those of oral pathologists (master's degree). CONCLUSION The correct diagnosis rate of DODS was comparable to oral pathologists using standard microscopic examination. The DODS program could be utilized as diagnostic guidance tool with high reliability & accuracy.
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Affiliation(s)
- Shaimaa O Zayed
- Department of Oral maxillofacial Pathology, Faculty of Dentistry, Cairo University, Cairo, Egypt
- Department of Oral Pathology, Misr University for Science and Technology, P. O. Box 77, Giza, Egypt
| | - Rawan Y M Abd-Rabou
- Faculty of Oral Medicine & Dental Surgery, Misr University for Science and Technology, P. O. Box 77, Giza, Egypt
| | | | - Youssef Abdelhamid
- Philosophy & Interactive Media Minors, New York University, Abu Dhabi, United Arab Emirates
| | | | - Bassam A Abulnoor
- Fixes Prosthodontics, Faculty of Dentistry, Ain Shams University, Cairo, Egypt
| | | | - Heba Khaled
- Lecturer of Oral Maxillofacial Pathology, Faculty of Dentistry, Cairo University, Cairo, Egypt
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Köktürk B, Pamukçu H, Gözüaçık Ö. Evaluation of different machine learning algorithms for extraction decision in orthodontic treatment. Orthod Craniofac Res 2024. [PMID: 38764408 DOI: 10.1111/ocr.12811] [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] [Accepted: 05/08/2024] [Indexed: 05/21/2024]
Abstract
INTRODUCTION The extraction decision significantly affects the treatment process and outcome. Therefore, it is crucial to make this decision with a more objective and standardized method. The objectives of this study were (1) to identify the best-performing model among seven machine learning (ML) models, which will standardize the extraction decision and serve as a guide for inexperienced clinicians, and (2) to determine the important variables for the extraction decision. METHODS This study included 1000 patients who received orthodontic treatment with or without extraction (500 extraction and 500 non-extraction). The success criteria of the study were the decisions made by the four experienced orthodontists. Seven ML models were trained using 36 variables; including demographic information, cephalometric and model measurements. First, the extraction decision was performed, and then the extraction type was identified. Accuracy and area under the curve (AUC) of the receiver operating characteristics (ROC) curve were used to measure the success of ML models. RESULTS The Stacking Classifier model, which consists of Gradient Boosted Trees, Support Vector Machine, and Random Forest models, showed the highest performance in extraction decision with 91.2% AUC. The most important features determining extraction decision were maxillary and mandibular arch length discrepancy, Wits Appraisal, and ANS-Me length. Likewise, the Stacking Classifier showed the highest performance with 76.3% accuracy in extraction type decisions. The most important variables for the extraction type decision were mandibular arch length discrepancy, Class I molar relationship, cephalometric overbite, Wits Appraisal, and L1-NB distance. CONCLUSION The Stacking Classifier model exhibited the best performance for the extraction decision. While ML models showed a high performance in extraction decision, they could not able to achieve the same level of performance in extraction type decision.
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Affiliation(s)
- Begüm Köktürk
- Department of Orthodontics, Faculty of Dentistry, Başkent University, Ankara, Turkey
| | - Hande Pamukçu
- Department of Orthodontics, Faculty of Dentistry, Başkent University, Ankara, Turkey
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Karakuş R, Öziç MÜ, Tassoker M. AI-Assisted Detection of Interproximal, Occlusal, and Secondary Caries on Bite-Wing Radiographs: A Single-Shot Deep Learning Approach. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01113-x. [PMID: 38743125 DOI: 10.1007/s10278-024-01113-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/28/2024] [Accepted: 04/01/2024] [Indexed: 05/16/2024]
Abstract
Tooth decay is a common oral disease worldwide, but errors in diagnosis can often be made in dental clinics, which can lead to a delay in treatment. This study aims to use artificial intelligence (AI) for the automated detection and localization of secondary, occlusal, and interproximal (D1, D2, D3) caries types on bite-wing radiographs. The eight hundred and sixty bite-wing radiographs were collected from the School of Dentistry database. Pre-processing and data augmentation operations were performed. Interproximal (D1, D2, D3), secondary, and occlusal caries on bite-wing radiographs were annotated by two oral radiologists. The data were split into 80% for training, 10% for validation, and 10% for testing. The AI-based training process was conducted using the YOLOv8 algorithm. A clinical decision support system interface was designed using the Python PyQT5 library, allowing for the use of dental caries detection without the need for complex programming procedures. In the test images, the average precision, average sensitivity, and average F1 score values for secondary, occlusal, and interproximal caries were obtained as 0.977, 0.932, and 0.954, respectively. The AI-based dental caries detection system yielded highly successful results in the test, receiving full approval from dentists for clinical use. YOLOv8 has the potential to increase sensitivity and reliability while reducing the burden on dentists and can prevent diagnostic errors in dental clinics.
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Affiliation(s)
- Rabia Karakuş
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Necmettin Erbakan University, Konya, Turkey
| | - Muhammet Üsame Öziç
- Faculty of Technology, Department of Biomedical Engineering, Pamukkale University, Denizli, Turkey.
| | - Melek Tassoker
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Necmettin Erbakan University, Konya, Turkey
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Ni FD, Xu ZN, Liu MQ, Zhang MJ, Li S, Bai HL, Ding P, Fu KY. Towards clinically applicable automated mandibular canal segmentation on CBCT. J Dent 2024; 144:104931. [PMID: 38458378 DOI: 10.1016/j.jdent.2024.104931] [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: 09/20/2023] [Revised: 03/04/2024] [Accepted: 03/05/2024] [Indexed: 03/10/2024] Open
Abstract
OBJECTIVES To develop a deep learning-based system for precise, robust, and fully automated segmentation of the mandibular canal on cone beam computed tomography (CBCT) images. METHODS The system was developed on 536 CBCT scans (training set: 376, validation set: 80, testing set: 80) from one center and validated on an external dataset of 89 CBCT scans from 3 centers. Each scan was annotated using a multi-stage annotation method and refined by oral and maxillofacial radiologists. We proposed a three-step strategy for the mandibular canal segmentation: extraction of the region of interest based on 2D U-Net, global segmentation of the mandibular canal, and segmentation refinement based on 3D U-Net. RESULTS The system consistently achieved accurate mandibular canal segmentation in the internal set (Dice similarity coefficient [DSC], 0.952; intersection over union [IoU], 0.912; average symmetric surface distance [ASSD], 0.046 mm; 95% Hausdorff distance [HD95], 0.325 mm) and the external set (DSC, 0.960; IoU, 0.924; ASSD, 0.040 mm; HD95, 0.288 mm). CONCLUSIONS These results demonstrated the potential clinical application of this AI system in facilitating clinical workflows related to mandibular canal localization. CLINICAL SIGNIFICANCE Accurate delineation of the mandibular canal on CBCT images is critical for implant placement, mandibular third molar extraction, and orthognathic surgery. This AI system enables accurate segmentation across different models, which could contribute to more efficient and precise dental automation systems.
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Affiliation(s)
- Fang-Duan Ni
- Department of Oral & Maxillofacial Radiology, Peking University School & Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases, Beijing 100081, China; National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing 100081, China; Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China
| | | | - Mu-Qing Liu
- Department of Oral & Maxillofacial Radiology, Peking University School & Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases, Beijing 100081, China; National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing 100081, China; Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China.
| | - Min-Juan Zhang
- Second Dental Center, Peking University Hospital of Stomatology, Beijing 100101, China
| | - Shu Li
- Department of Stomatology, Beijing Hospital, Beijing 100005, China
| | | | | | - Kai-Yuan Fu
- Department of Oral & Maxillofacial Radiology, Peking University School & Hospital of Stomatology, Beijing 100081, China; National Center for Stomatology & National Clinical Research Center for Oral Diseases, Beijing 100081, China; National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing 100081, China; Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China.
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Brozović J, Mikulić B, Tomas M, Juzbašić M, Blašković M. Assessing the performance of Bing Chat artificial intelligence: Dental exams, clinical guidelines, and patients' frequent questions. J Dent 2024; 144:104927. [PMID: 38458379 DOI: 10.1016/j.jdent.2024.104927] [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: 08/28/2023] [Revised: 03/03/2024] [Accepted: 03/05/2024] [Indexed: 03/10/2024] Open
Abstract
OBJECTIVES Bing Chat is a large language model artificial intelligence (AI) with online search and text generating capabilities. This study assessed its performance within the scope of dentistry in: (a) tackling exam questions for dental students, (ii) providing guidelines for dental practitioners, and (iii) answering patients' frequently asked questions. We discuss the potential of clinical tutoring, common patient communication and impact on academia. METHODS With the aim of assessing AI's performance in dental exams, Bing Chat was presented with 532 multiple-choice questions and awarded scores based on its answers. In evaluating guidelines for clinicians, a further set of 15 questions, each with 2 follow-up questions on clinical protocols, was presented to the AI. The answers were assessed by 4 reviewers using electronic visual analog scale. In evaluating answers to patients' frequently asked questions, another list of 15 common questions was included in the session, with respective outputs assessed. RESULTS Bing Chat correctly answered 383 out of 532 multiple-choice questions in dental exam part, achieving a score of 71.99 %. As for outlining clinical protocols for practitioners, the overall assessment score was 81.05 %. In answering patients' frequently asked questions, Bing Chat achieved an overall mean score of 83.8 %. The assessments demonstrated low inter-rater reliability. CONCLUSIONS The overall performance of Bing Chat was above the regularly adopted passing scores, particularly in answering patient's frequently asked questions. The generated content may have biased sources. These results suggest the importance of raising clinicians' awareness of AI's benefits and risks, as well as timely adaptations of dental education curricula, and safeguarding its use in dentistry and healthcare in general. CLINICAL SIGNIFICANCE Bing Chat AI performed above the passing threshold in three categories, and thus demonstrated potential for educational assistance, clinical tutoring, and answering patients' questions. We recommend popularizing its benefits and risks among students and clinicians, while maintaining awareness of possible false information.
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Affiliation(s)
- Juraj Brozović
- Assistant Professor, Ph.D., DMD, Specialist in Oral Surgery, Faculty of Dental Medicine and Health, University of Osijek, Croatia.
| | - Barbara Mikulić
- Assistant, DMD, Faculty of Dental Medicine and Health, University of Osijek, Croatia
| | - Matej Tomas
- Assistant, Ph.D., DMD, Faculty of Dental Medicine and Health, University of Osijek, Croatia
| | - Martina Juzbašić
- Assistant, DMD, Faculty of Dental Medicine and Health, University of Osijek, Croatia
| | - Marko Blašković
- Assistant, DMD, Specialist in Oral Surgery, Department of Oral Surgery, Faculty of Dental Medicine, University of Rijeka, Croatia
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11
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Naeimi SM, Darvish S, Salman BN, Luchian I. Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review. Bioengineering (Basel) 2024; 11:431. [PMID: 38790300 PMCID: PMC11118054 DOI: 10.3390/bioengineering11050431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/21/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial intelligence (AI) has been recently introduced into clinical dentistry, and it has assisted professionals in analyzing medical data with unprecedented speed and an accuracy level comparable to humans. With the help of AI, meaningful information can be extracted from dental databases, especially dental radiographs, to devise machine learning (a subset of AI) models. This study focuses on models that can diagnose and assist with clinical conditions such as oral cancers, early childhood caries, deciduous teeth numbering, periodontal bone loss, cysts, peri-implantitis, osteoporosis, locating minor apical foramen, orthodontic landmark identification, temporomandibular joint disorders, and more. The aim of the authors was to outline by means of a review the state-of-the-art applications of AI technologies in several dental subfields and to discuss the efficacy of machine learning algorithms, especially convolutional neural networks (CNNs), among different types of patients, such as pediatric cases, that were neglected by previous reviews. They performed an electronic search in PubMed, Google Scholar, Scopus, and Medline to locate relevant articles. They concluded that even though clinicians encounter challenges in implementing AI technologies, such as data management, limited processing capabilities, and biased outcomes, they have observed positive results, such as decreased diagnosis costs and time, as well as early cancer detection. Thus, further research and development should be considered to address the existing complications.
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Affiliation(s)
| | - Shayan Darvish
- School of Dentistry, University of Michigan, Ann Arbor, MI 48104, USA;
| | - Bahareh Nazemi Salman
- Department of Pediatric Dentistry, School of Dentistry, Zanjan University of Medical Sciences, Zanjan 4513956184, Iran
| | - Ionut Luchian
- Department of Periodontology, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
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12
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Koseoglu M, Ramachandran RA, Ozdemir H, Ariani MD, Bayindir F, Sukotjo C. Automated facial landmark measurement using machine learning: A feasibility study. J Prosthet Dent 2024:S0022-3913(24)00282-8. [PMID: 38670909 DOI: 10.1016/j.prosdent.2024.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/09/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024]
Abstract
STATEMENT OF PROBLEM Information regarding facial landmark measurement using machine learning (ML) techniques in prosthodontics is lacking. PURPOSE The objective of this study was to evaluate and compare the reliability, validity, and accuracy of facial anthropological measurements using both manual and ML landmark detection techniques. MATERIAL AND METHODS Two-dimensional (2D) frontal full-face photographs of 50 men and 50 women were made. The interpupillary width (IPW), interlateral canthus width (LCW), intermedial canthus width (MCW), interalar width (IAW), and intercommissural width (ICW) were measured on 2D digital images using manual and ML methods. The automated measurements were recorded using a programming language (Python), and a convolutional neural network (CNN) model was trained to detect human facial landmarks. The obtained data from the manual and ML methods were analyzed using intraclass correlation coefficients (ICCs), the paired sample t test, Bland-Altman plots, and the Pearson correlation analysis (α=.05). RESULTS Intrarater and interrater reliability values were greater than 0.90, indicating excellent reliability. The mean difference between the manual and ML measurements of IPW, MCW, IAW, and ICW was 0.02 mm, while it was 0.01 mm for LCW. No statistically significant differences were found between the measurements obtained by the manual and ML methods (P>.05). Highly significant positive correlations (P<.001) were obtained between the results of the manual and ML methods: (r=0.996[IPW], r=0.977[LCW], r=0.944[MCW], r=0.965[IAW], and r=0.997[ICW]). CONCLUSIONS In the field of prosthodontics, the use of ML methods provides a reliable alternative to manual digital techniques for carrying out facial anthropometric measurements.
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Affiliation(s)
- Merve Koseoglu
- Associate Professor, Department of Prosthodontics, Faculty of Dentistry, University of Sakarya, Sakarya, Turkey and Ph.D student, Department of Prosthodontics, Faculty of Dentistry, University of Ataturk, Erzurum, Turkey
| | - Remya Ampadi Ramachandran
- Fellow (Postdoc), 1DATA Consortium, Computational Comparative Medicine, Department of Mathematics, K- State Olathe, Olathe, Kansas
| | - Hatice Ozdemir
- Associate Professor, Department of Prosthodontics, Faculty of Dentistry, University of Ataturk, Erzurum, Turkey
| | - Maretaningtias Dwi Ariani
- Lecturer, Department of Prosthodontic, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
| | - Funda Bayindir
- Professor, Department of Prosthodontics, Faculty of Dentistry, University of Ataturk, Erzurum, Turkey
| | - Cortino Sukotjo
- Professor, Department of Restorative Dentistry, College of Dentistry, University of Illinois, Chicago, Ill; and Adjunct Professor, Department of Prosthodontic, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia.
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Ahmed WM, Azhari AA, Alfaraj A, Alhamadani A, Zhang M, Lu CT. The Quality of AI-Generated Dental Caries Multiple Choice Questions: A Comparative Analysis of ChatGPT and Google Bard Language Models. Heliyon 2024; 10:e28198. [PMID: 38596020 PMCID: PMC11002540 DOI: 10.1016/j.heliyon.2024.e28198] [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: 10/12/2023] [Revised: 03/05/2024] [Accepted: 03/13/2024] [Indexed: 04/11/2024] Open
Abstract
Statement of problem AI technology presents a variety of benefits and challenges for educators. Purpose To investigate whether ChatGPT and Google Bard (now is named Gemini) are valuable resources for generating multiple-choice questions for educators of dental caries. Material and methods A book on dental caries was used. Sixteen paragraphs were extracted by an expert consultant based on applicability and potential for developing multiple-choice questions. ChatGPT and Bard language models were used to produce multiple-choice questions based on this input, and 64 questions were generated. Three dental specialists assessed the relevance, accuracy, and complexity of the generated questions. The questions were qualitatively evaluated using cognitive learning objectives and item writing flaws. Paired sample t-tests and two-way analysis of variance (ANOVA) were used to compare the generated multiple-choice questions and answers between ChatGPT and Bard. Results There were no significant differences between the questions generated by ChatGPT and Bard. Moreover, the analysis of variance found no significant differences in question quality. Bard-generated questions tended to have higher cognitive levels than those of ChatGPT. Format error was predominant in ChatGPT-generated questions. Finally, Bard exhibited more absolute terms than ChatGPT. Conclusions ChatGPT and Bard could generate questions related to dental caries, mainly at the cognitive level of knowledge and comprehension. Clinical significance Language models are crucial for generating subject-specific questions used in quizzes, tests, and education. By using these models, educators can save time and focus on lesson preparation and student engagement instead of solely focusing on assessment creation. Additionally, language models are adept at generating numerous questions, making them particularly valuable for large-scale exams. However, educators must carefully review and adapt the questions to ensure they align with their learning goals.
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Affiliation(s)
- Walaa Magdy Ahmed
- Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Amr Ahmed Azhari
- Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Amal Alfaraj
- Department of Prosthodontics, School of Dentistry, King Faisal Universality, Al Ahsa, Saudi Arabia
| | | | - Min Zhang
- Department of Computer Science, Virginia Tech, Northern Virginia Center, USA
| | - Chang-Tien Lu
- Department of Computer Science, Virginia Tech, Northern Virginia Center, USA
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Ali IE, Sumita Y, Wakabayashi N. Advancing maxillofacial prosthodontics by using pre-trained convolutional neural networks: Image-based classification of the maxilla. J Prosthodont 2024. [PMID: 38566564 DOI: 10.1111/jopr.13853] [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: 11/21/2023] [Accepted: 03/15/2024] [Indexed: 04/04/2024] Open
Abstract
PURPOSE The study aimed to compare the performance of four pre-trained convolutional neural networks in recognizing seven distinct prosthodontic scenarios involving the maxilla, as a preliminary step in developing an artificial intelligence (AI)-powered prosthesis design system. MATERIALS AND METHODS Seven distinct classes, including cleft palate, dentulous maxillectomy, edentulous maxillectomy, reconstructed maxillectomy, completely dentulous, partially edentulous, and completely edentulous, were considered for recognition. Utilizing transfer learning and fine-tuned hyperparameters, four AI models (VGG16, Inception-ResNet-V2, DenseNet-201, and Xception) were employed. The dataset, consisting of 3541 preprocessed intraoral occlusal images, was divided into training, validation, and test sets. Model performance metrics encompassed accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), and confusion matrix. RESULTS VGG16, Inception-ResNet-V2, DenseNet-201, and Xception demonstrated comparable performance, with maximum test accuracies of 0.92, 0.90, 0.94, and 0.95, respectively. Xception and DenseNet-201 slightly outperformed the other models, particularly compared with InceptionResNet-V2. Precision, recall, and F1 scores exceeded 90% for most classes in Xception and DenseNet-201 and the average AUC values for all models ranged between 0.98 and 1.00. CONCLUSIONS While DenseNet-201 and Xception demonstrated superior performance, all models consistently achieved diagnostic accuracy exceeding 90%, highlighting their potential in dental image analysis. This AI application could help work assignments based on difficulty levels and enable the development of an automated diagnosis system at patient admission. It also facilitates prosthesis designing by integrating necessary prosthesis morphology, oral function, and treatment difficulty. Furthermore, it tackles dataset size challenges in model optimization, providing valuable insights for future research.
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Affiliation(s)
- Islam E Ali
- Department of Advanced Prosthodontics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
- Department of Prosthodontics, Faculty of Dentistry, Mansoura University, Mansoura, Egypt
| | - Yuka Sumita
- Division of General Dentistry 4, The Nippon Dental University Hospital, Tokyo, Japan
- Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Noriyuki Wakabayashi
- Department of Advanced Prosthodontics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
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Lin GSS, Tan WW, Hashim H. Students' perceptions towards the ethical considerations of using artificial intelligence algorithms in clinical decision-making. Br Dent J 2024:10.1038/s41415-024-7184-3. [PMID: 38491204 DOI: 10.1038/s41415-024-7184-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/01/2023] [Indexed: 03/18/2024]
Abstract
Aim The present study aimed to explore the perceptions of dental students regarding the ethical considerations associated with the use of artificial intelligence (AI) algorithms in clinical decision-making.Methods All the undergraduate clinical-year dental students were invited to take part in the study. A validated online questionnaire which consisted of 21 closed-ended questions (five-point Likert scales) was distributed to the students to evaluate their perceptions on the topic. Mean perception scores of the students from different years were analysed using a one-way ANOVA test, while independent t-tests were used to compare the scores between sexes.Results In total, 165 students participated in the present study. The mean age of the respondents was 23.3 (± 1.38) years and the majority were female, Chinese students. Respondents showed positive perceptions throughout all three domains. Uniform and comparable perceptions were seen across various academic years and sexes, with female respondents expressing stronger agreement regarding patient consent and privacy prioritisation.Conclusion Undergraduate clinical dental students generally showed positive perceptions regarding the ethical considerations associated with the integration of AI algorithms in clinical decision-making. It is essential to address these ethical considerations to ensure that AI benefits patient outcomes while upholding fundamental ethical principles and patient-centred care.
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Affiliation(s)
- Galvin Sim Siang Lin
- Department of Restorative Dentistry, Kulliyyah of Dentistry, International Islamic University Malaysia, 25200, Pahang, Malaysia.
| | - Wen Wu Tan
- Department of Dental Public Health, Faculty of Dentistry, AIMST University, 08100, Kedah, Malaysia
| | - Hasnah Hashim
- Department of Dental Public Health, Faculty of Dentistry, AIMST University, 08100, Kedah, Malaysia
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Oszlánszky J, Gulácsi L, Péntek M, Hermann P, Zrubka Z. Psychometric Properties of General Oral Health Assessment Index Across Ages: COSMIN Systematic Review. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2024:S1098-3015(24)00120-7. [PMID: 38492926 DOI: 10.1016/j.jval.2024.02.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/15/2024] [Accepted: 02/29/2024] [Indexed: 03/18/2024]
Abstract
OBJECTIVES To systematically review the psychometric properties of the Geriatric Oral Health Assessment Index (GOHAI) across age groups using the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) methodology. METHODS Data: English peer-reviewed articles reporting studies of the development, translation, or validation of GOHAI. SOURCES PubMed, Web of Science, and EMBASE from Jan 1990 until December 31, 2023. Methodological evaluation: based on COSMIN methodology. The results are presented overall and for 4 age groups (≥60 years, all ages, <60 years, ≤45 years). Structural validity was summarized qualitatively. Internal consistency and reliability were synthesized via random-effects meta-analysis of T-transformed Cronbach α values, and Fisher's Z transformed correlation coefficients. Construct validity and responsiveness were assessed using effect sizes. RESULTS Four hundred ninety-seven records were identified, 72 underwent full-text assessment, resulting in 60 included reports. Structural validity was inconsistent across all age groups and overall. Internal consistency was sufficient with overall α = 0.81, and high evidence quality. Test-retest reliability was consistently sufficient across age groups with overall r = 0.84. For construct validity 361 hypotheses were assessed (37.4% for convergent-, 62.6% for known-groups validity). The percentage of confirmed hypotheses in ≥60-years, all ages, <60-years and ≤45-years were 75.5%, 66.7%, 78.9%, and 88.9%, respectively. Responsiveness was not assessed in the <60-years and ≤45-years age groups, leading to indeterminate overall rating with very low evidence quality. CONCLUSIONS This review affirms that GOHAI has sufficient psychometric properties as an oral health-related quality of life instrument in various age groups, but its responsiveness is scarcely researched and its utility for individual-level follow-up is limited. The measurement properties of oral health-related quality of life tools must be scrutinized in the changing demands of personalized and value-based dental care. (PROSPERO registration: CRD42022384132).
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Affiliation(s)
- Judit Oszlánszky
- Department of Prosthodontics, Faculty of Dentistry, Semmelweis University, Budapest, Hungary.
| | - László Gulácsi
- Health Economics Research Center, University Research and Innovation Center, University of Óbuda, Budapest, Hungary
| | - Márta Péntek
- Health Economics Research Center, University Research and Innovation Center, University of Óbuda, Budapest, Hungary
| | - Péter Hermann
- Department of Prosthodontics, Faculty of Dentistry, Semmelweis University, Budapest, Hungary
| | - Zsombor Zrubka
- Health Economics Research Center, University Research and Innovation Center, University of Óbuda, Budapest, Hungary
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Yu H, Ye X, Hong W, Shi R, Ding Y, Liu C. A cascading learning method with SegFormer for radiographic measurement of periodontal bone loss. BMC Oral Health 2024; 24:325. [PMID: 38468273 DOI: 10.1186/s12903-024-04079-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/27/2024] [Indexed: 03/13/2024] Open
Abstract
OBJECTIVE Marginal alveolar bone loss is one of the key features of periodontitis and can be observed via panoramic radiographs. This study aimed to establish a cascading learning method with deep learning (DL) for precise radiographic bone loss (RBL) measurements at specific tooth positions. MATERIALS AND METHODS Through the design of two tasks for tooth position recognition and tooth semantic segmentation using the SegFormer model, specific tooth's crown, intrabony portion, and suprabony portion of the roots were obtained. The RBL was subsequently measured by length through these three areas using the principal component analysis (PCA) principal axis. RESULTS The average intersection over union (IoU) for the tooth position recognition task was 0.8906, with an F1-score of 0.9338. The average IoU for the tooth semantic segmentation task was 0.8465, with an F1-score of 0.9138. When the two tasks were combined, the average IoU was 0.7889, with an F1-score of 0.8674. The correlation coefficient between the RBL prediction results based on the PCA principal axis and the clinicians' measurements exceeded 0.85. Compared to those of the other two methods, the average precision of the predicted RBL was 0.7722, the average sensitivity was 0.7416, and the average F1-score was 0.7444. CONCLUSIONS The method for predicting RBL using DL and PCA produced promising results, offering rapid and reliable auxiliary information for future periodontal disease diagnosis. CLINICAL RELEVANCE Precise RBL measurements are important for periodontal diagnosis. The proposed RBL-SF can measure RBL at specific tooth positions and assign the bone loss stage. The ability of the RBL-SF to measure RBL at specific tooth positions can guide clinicians to a certain extent in the accurate diagnosis of periodontitis.
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Affiliation(s)
- Hanwen Yu
- School of Resources and Environment, University of Electronic Science and Technology, Chengdu, Sichuan, 610097, China
| | - Xin Ye
- School of Resources and Environment, University of Electronic Science and Technology, Chengdu, Sichuan, 610097, China
| | - Wanjing Hong
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Rui Shi
- School of Resources and Environment, University of Electronic Science and Technology, Chengdu, Sichuan, 610097, China
| | - Yi Ding
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Chengcheng Liu
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China.
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Alam MK, Alftaikhah SAA, Issrani R, Ronsivalle V, Lo Giudice A, Cicciù M, Minervini G. Applications of artificial intelligence in the utilisation of imaging modalities in dentistry: A systematic review and meta-analysis of in-vitro studies. Heliyon 2024; 10:e24221. [PMID: 38317889 PMCID: PMC10838702 DOI: 10.1016/j.heliyon.2024.e24221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 01/02/2024] [Accepted: 01/04/2024] [Indexed: 02/07/2024] Open
Abstract
Background In the past, dentistry heavily relied on manual image analysis and diagnostic procedures, which could be time-consuming and prone to human error. The advent of artificial intelligence (AI) has brought transformative potential to the field, promising enhanced accuracy and efficiency in various dental imaging tasks. This systematic review and meta-analysis aimed to comprehensively evaluate the applications of AI in dental imaging modalities, focusing on in-vitro studies. Methods A systematic literature search was conducted, in accordance with the PRISMA guidelines. The following databases were systematically searched: PubMed/MEDLINE, Embase, Web of Science, Scopus, IEEE Xplore, Cochrane Library, CINAHL (Cumulative Index to Nursing and Allied Health Literature), and Google Scholar. The meta-analysis employed fixed-effects models to assess AI accuracy, calculating odds ratios (OR) for true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) with 95 % confidence intervals (CI). Heterogeneity and overall effect tests were applied to ensure the reliability of the findings. Results 9 studies were selected that encompassed various objectives, such as tooth segmentation and classification, caries detection, maxillofacial bone segmentation, and 3D surface model creation. AI techniques included convolutional neural networks (CNNs), deep learning algorithms, and AI-driven tools. Imaging parameters assessed in these studies were specific to the respective dental tasks. The analysis of combined ORs indicated higher odds of accurate dental image assessments, highlighting the potential for AI to improve TPR, TNR, PPV, and NPV. The studies collectively revealed a statistically significant overall effect in favor of AI in dental imaging applications. Conclusion In summary, this systematic review and meta-analysis underscore the transformative impact of AI on dental imaging. AI has the potential to revolutionize the field by enhancing accuracy, efficiency, and time savings in various dental tasks. While further research in clinical settings is needed to validate these findings and address study limitations, the future implications of integrating AI into dental practice hold great promise for advancing patient care and the field of dentistry.
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Affiliation(s)
- Mohammad Khursheed Alam
- Preventive Dentistry Department, College of Dentistry, Jouf University, Sakaka, 72345, Saudi Arabia
- Department of Dental Research Cell, Saveetha Institute of Medical and Technical Sciences, Saveetha Dental College and Hospitals, Chennai, 600077, India
- Department of Public Health, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, 1207, Bangladesh
| | | | - Rakhi Issrani
- Preventive Dentistry Department, College of Dentistry, Jouf University, Sakaka, 72345, Saudi Arabia
| | - Vincenzo Ronsivalle
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, Italy
| | - Antonino Lo Giudice
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, Italy
| | - Marco Cicciù
- Department of Biomedical and Surgical and Biomedical Sciences, Catania University, 95123, Catania, Italy
| | - Giuseppe Minervini
- Multidisciplinary Department of Medical-Surgical and Odontostomatological Specialties, University of Campania “Luigi Vanvitelli”, 80121, Naples, Italy
- Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Science (SIMATS), Saveetha University, Chennai, Tamil Nadu, India
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Umer F, Adnan S, Lal A. Research and application of artificial intelligence in dentistry from lower-middle income countries - a scoping review. BMC Oral Health 2024; 24:220. [PMID: 38347508 PMCID: PMC10860267 DOI: 10.1186/s12903-024-03970-y] [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: 09/21/2023] [Accepted: 02/02/2024] [Indexed: 02/15/2024] Open
Abstract
Artificial intelligence (AI) has been integrated into dentistry for improvement of current dental practice. While many studies have explored the utilization of AI in various fields, the potential of AI in dentistry, particularly in low-middle income countries (LMICs) remains understudied. This scoping review aimed to study the existing literature on the applications of artificial intelligence in dentistry in low-middle income countries. A comprehensive search strategy was applied utilizing three major databases: PubMed, Scopus, and EBSCO Dentistry & Oral Sciences Source. The search strategy included keywords related to AI, Dentistry, and LMICs. The initial search yielded a total of 1587, out of which 25 articles were included in this review. Our findings demonstrated that limited studies have been carried out in LMICs in terms of AI and dentistry. Most of the studies were related to Orthodontics. In addition gaps in literature were noted such as cost utility and patient experience were not mentioned in the included studies.
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Affiliation(s)
- Fahad Umer
- Department of Surgery, Section of Dentistry, The Aga Khan University, Karachi, Pakistan
| | - Samira Adnan
- Department of Operative Dentistry, Sindh Institute of Oral Health Sciences, Jinnah Sindh Medical University, Karachi, Pakistan
| | - Abhishek Lal
- Department of Medicine, Section of Gastroenterology, The Aga Khan University, Karachi, Pakistan.
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Chen X, Ma N, Xu T, Xu C. Deep learning-based tooth segmentation methods in medical imaging: A review. Proc Inst Mech Eng H 2024; 238:115-131. [PMID: 38314788 DOI: 10.1177/09544119231217603] [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] [Indexed: 02/07/2024]
Abstract
Deep learning approaches for tooth segmentation employ convolutional neural networks (CNNs) or Transformers to derive tooth feature maps from extensive training datasets. Tooth segmentation serves as a critical prerequisite for clinical dental analysis and surgical procedures, enabling dentists to comprehensively assess oral conditions and subsequently diagnose pathologies. Over the past decade, deep learning has experienced significant advancements, with researchers introducing efficient models such as U-Net, Mask R-CNN, and Segmentation Transformer (SETR). Building upon these frameworks, scholars have proposed numerous enhancement and optimization modules to attain superior tooth segmentation performance. This paper discusses the deep learning methods of tooth segmentation on dental panoramic radiographs (DPRs), cone-beam computed tomography (CBCT) images, intro oral scan (IOS) models, and others. Finally, we outline performance-enhancing techniques and suggest potential avenues for ongoing research. Numerous challenges remain, including data annotation and model generalization limitations. This paper offers insights for future tooth segmentation studies, potentially facilitating broader clinical adoption.
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Affiliation(s)
- Xiaokang Chen
- Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China
| | - Nan Ma
- Faculty of Information and Technology, Beijing University of Technology, Beijing, China
- Engineering Research Center of Intelligence Perception and Autonomous Control, Ministry of Education, Beijing University of Technology, Beijing, China
| | - Tongkai Xu
- Department of General Dentistry II, Peking University School and Hospital of Stomatology, Beijing, China
| | - Cheng Xu
- Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China
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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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22
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Ying S, Huang F, Liu W, He F. Deep learning in the overall process of implant prosthodontics: A state-of-the-art review. Clin Implant Dent Relat Res 2024. [PMID: 38286659 DOI: 10.1111/cid.13307] [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: 12/11/2023] [Revised: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 01/31/2024]
Abstract
Artificial intelligence represented by deep learning has attracted attention in the field of dental implant restoration. It is widely used in surgical image analysis, implant plan design, prosthesis shape design, and prognosis judgment. This article mainly describes the research progress of deep learning in the whole process of dental implant prosthodontics. It analyzes the limitations of current research, and looks forward to the future development direction.
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Affiliation(s)
- Shunv Ying
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Feng Huang
- School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou, China
| | - Wei Liu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
| | - Fuming He
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China
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Cai J, Min Z, Deng Y, Jing D, Zhao Z. Assessing the impact of occlusal plane rotation on facial aesthetics in orthodontic treatment: a machine learning approach. BMC Oral Health 2024; 24:30. [PMID: 38184528 PMCID: PMC10771708 DOI: 10.1186/s12903-023-03817-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 12/21/2023] [Indexed: 01/08/2024] Open
Abstract
BACKGROUND Adequate occlusal plane (OP) rotation through orthodontic therapy enables satisfying profile improvements for patients who are disturbed by their maxillomandibular imbalance but reluctant to surgery. The study aims to quantify profile improvements that OP rotation could produce in orthodontic treatment and whether the efficacy differs among skeletal types via machine learning. MATERIALS AND METHODS Cephalometric radiographs of 903 patients were marked and analyzed by trained orthodontists with assistance of Uceph, a commercial software which use artificial intelligence to perform the cephalometrics analysis. Back-propagation artificial neural network (BP-ANN) models were then trained based on collected samples to fit the relationship among maxillomandibular structural indicators, SN-OP and P-A Face Height ratio (FHR), Facial Angle (FA). After corroborating the precision and reliability of the models by T-test and Bland-Altman analysis, simulation strategy and matrix computation were combined to predict the consequent changes of FHR, FA to OP rotation. Linear regression and statistical approaches were then applied for coefficient calculation and differences comparison. RESULTS The regression scores calculating the similarity between predicted and true values reached 0.916 and 0.908 in FHR, FA models respectively, and almost all pairs were in 95% CI of Bland-Altman analysis, confirming the effectiveness of our models. Matrix simulation was used to ascertain the efficacy of OP control in aesthetic improvements. Intriguingly, though FHR change rate appeared to be constant across groups, in FA models, hypodivergent group displayed more sensitive changes to SN-OP than normodivergent, hypodivergent group, and Class III group significantly showed larger changes than Class I and II. CONCLUSIONS Rotation of OP could yield differently to facial aesthetic improvements as more efficient in hypodivergent groups vertically and Class III groups sagittally.
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Affiliation(s)
- Jingyi Cai
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan, 610041, China
| | - Ziyang Min
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan, 610041, China
| | - Yudi Deng
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan, 610041, China
| | - Dian Jing
- Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University, No.639, Zhizaoju Road, Huangpu District, Shanghai, 200011, China.
| | - Zhihe Zhao
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan, 610041, China.
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24
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Fan FY, Lin WC, Huang HY, Shen YK, Chang YC, Li HY, Ruslin M, Lee SY. Applying machine learning to assess the morphology of sculpted teeth. J Dent Sci 2024; 19:542-549. [PMID: 38303893 PMCID: PMC10829735 DOI: 10.1016/j.jds.2023.09.023] [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: 08/24/2023] [Revised: 09/21/2023] [Indexed: 02/03/2024] Open
Abstract
Background/purpose Producing tooth crowns through dental technology is a basic function of dentistry. The morphology of tooth crowns is the most important parameter for evaluating its acceptability. The procedures were divided into four steps: tooth collection, scanning skills, use of mathematical methods and software, and machine learning calculation. Materials and methods Dental plaster rods were prepared. The effective data collected were to classify 121 teeth (15th tooth position), 342 teeth (16th tooth position), 69 teeth (21st tooth position), and 89 teeth (43rd tooth position), for a total of 621 teeth. The procedures are divided into four steps: tooth collection, scanning skills, use of mathematical methods and software, and machine learning calculation. Results The area under the curve (AUC) value was 0, 0.5, and 0.72 in this study. The precision rate and recall rate of micro-averaging/macro-averaging were 0.75/0.73 and 0.75/0.72. If we took a newly carved tooth picture into the program, the current effectiveness of machine learning was about 70%-75% to evaluate the quality of tooth morphology. Through the calculation and analysis of the two different concepts of micro-average/macro-average and AUC, similar values could be obtained. Conclusion This study established a set of procedures that can judge the quality of hand-carved plaster sticks and teeth, and the accuracy rate is about 70%-75%. It is expected that this process can be used to assist dental technicians in judging the pros and cons of hand-carved plaster sticks and teeth, so as to help dental technicians to learn the tooth morphology more effectively.
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Affiliation(s)
- Fang-Yu Fan
- School of Dental Technology, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Wei-Chun Lin
- School of Dental Technology, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Dentistry, Wan-Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Center for Tooth Bank and Dental Stem Cell Technology, Taipei Medical University, Taipei, Taiwan
| | - Huei-Yu Huang
- Department of Dentistry, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan
- School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yung-Kang Shen
- School of Dental Technology, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
- Department of Oral Biology, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia
| | - Yung-Chun Chang
- Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Heng-Yu Li
- School of Dental Technology, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Muhammad Ruslin
- Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Hasanuddin University, Makassar, Indonesia
| | - Sheng-Yang Lee
- Department of Dentistry, Wan-Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Center for Tooth Bank and Dental Stem Cell Technology, Taipei Medical University, Taipei, Taiwan
- School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
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25
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Liu CM, Lin WC, Lee SY. Evaluation of the efficiency, trueness, and clinical application of novel artificial intelligence design for dental crown prostheses. Dent Mater 2024; 40:19-27. [PMID: 37858418 DOI: 10.1016/j.dental.2023.10.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/05/2023] [Accepted: 10/05/2023] [Indexed: 10/21/2023]
Abstract
OBJECTIVE The unique structure of human teeth limits dental repair to custom-made solutions. The production process requires a lot of time and manpower. At present, artificial intelligence (AI) has begun to be used in the medical field and improve efficiency. This study attempted to design a variety of dental restorations using AI and evaluate their clinical applicability. METHODS Using inlay and crown restoration types commonly used in dental standard models, we compared differences in artificial wax-up carving (wax-up), artificial digital designs (digital) and AI designs (AI). The AI system was designed using computer calculations, and the other two methods were designed by humans. Restorations were made by 3D printing resin material. Image evaluations were compared with cone beam computed tomography (CBCT) by calculating the root mean squared error. RESULTS Surface truth results showed that AI (68.4 µm) and digital-designed crowns (51.0 µm) had better reproducibility. Using AI for the crown reduced the time spent by 400% (compared to digital) and 900% (compared to wax-up). Optical microscopic and CBCT images showed that AI and digital designs had close margin gaps (p < 0.05). The margin gap of the crown showed that the wax-up group was 4.1 and 4.3 times greater than those of the AI and digital crowns, respectively. Therefore, the utilization of artificial intelligence can assist in the production of dental restorations, thereby enhancing both production efficiency and accuracy. SIGNIFICANCE It is expected that the development of AI can contribute to the reproducibility, efficiency, and goodness of fit of dental restorations.
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Affiliation(s)
- Che-Ming Liu
- Department of Dentistry, Wan-Fang Hospital, Taipei Medical University, Taipei 116, Taiwan; School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Wei-Chun Lin
- Department of Dentistry, Wan-Fang Hospital, Taipei Medical University, Taipei 116, Taiwan; Center for Tooth Bank and Dental Stem Cell Technology, Taipei Medical University, Taipei 110, Taiwan; School of Dental Technology, College of Oral Medicine, Taipei Medical University, Taipei 110, Taiwan.
| | - Sheng-Yang Lee
- Department of Dentistry, Wan-Fang Hospital, Taipei Medical University, Taipei 116, Taiwan; School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei 110, Taiwan; Center for Tooth Bank and Dental Stem Cell Technology, Taipei Medical University, Taipei 110, Taiwan.
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26
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Fu WT, Zhu QK, Li N, Wang YQ, Deng SL, Chen HP, Shen J, Meng LY, Bian Z. Clinically Oriented CBCT Periapical Lesion Evaluation via 3D CNN Algorithm. J Dent Res 2024; 103:5-12. [PMID: 37968798 DOI: 10.1177/00220345231201793] [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] [Indexed: 11/17/2023] Open
Abstract
Apical periodontitis (AP) is one of the most prevalent disorders in dentistry. However, it can be underdiagnosed in asymptomatic patients. In addition, the perioperative evaluation of 3-dimensional (3D) lesion volume is of great clinical relevance, but the required slice-by-slice manual delineation method is time- and labor-intensive. Here, for quickly and accurately detecting and segmenting periapical lesions (PALs) associated with AP on cone beam computed tomography (CBCT) images, we proposed and geographically validated a novel 3D deep convolutional neural network algorithm, named PAL-Net. On the internal 5-fold cross-validation set, our PAL-Net achieved an area under the receiver operating characteristic curve (AUC) of 0.98. The algorithm also improved the diagnostic performance of dentists with varying levels of experience, as evidenced by their enhanced average AUC values (junior dentists: 0.89-0.94; senior dentists: 0.91-0.93), and significantly reduced the diagnostic time (junior dentists: 69.3 min faster; senior dentists: 32.4 min faster). Moreover, our PAL-Net achieved an average Dice similarity coefficient over 0.87 (0.85-0.88), which is superior or comparable to that of other existing state-of-the-art PAL segmentation algorithms. Furthermore, we validated the generalizability of the PAL-Net system using multiple external data sets from Central, East, and North China, showing that our PAL-Net has strong robustness. Our PAL-Net can help improve the diagnostic performance and speed of dentists working from CBCT images, provide clinically relevant volume information to dentists, and can potentially be applied in dental clinics, especially without expert-level dentists or radiologists.
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Affiliation(s)
- W T Fu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
- Department of Cariology and Endodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Q K Zhu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - N Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
- Department of Cariology and Endodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Y Q Wang
- Department of Gynecology, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, China
| | - S L Deng
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Hangzhou, China
| | - H P Chen
- Xiangyang Stomatological Hospital; Affiliated Stomatological Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - J Shen
- Department of International VIP Dental Clinic, Tianjin Stomatological Hospital, School of Medicine, Nankai University, Tianjin, China
| | - L Y Meng
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
- Department of Cariology and Endodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Z Bian
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
- Department of Cariology and Endodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China
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27
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Farajollahi M, Safarian MS, Hatami M, Esmaeil Nejad A, Peters OA. Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss-A scoping review. AUST ENDOD J 2023; 49:720-734. [PMID: 37439465 DOI: 10.1111/aej.12775] [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: 02/19/2023] [Revised: 07/03/2023] [Accepted: 07/04/2023] [Indexed: 07/14/2023]
Abstract
Radiographic evaluation of bone changes is one of the main tools in the diagnosis of many oral and maxillofacial diseases. However, this approach to assessment has limitations in accuracy, inconsistency and comparatively low diagnostic efficiency. Recently, artificial intelligence (AI)-based algorithms like deep learning networks have been introduced as a solution to overcome these challenges. Based on recent studies, AI can improve the detection accuracy of an expert clinician for periapical pathology, periodontal diseases and their prognostication, as well as peri-implant bone loss. Also, AI has been successfully used to detect and diagnose oral and maxillofacial lesions with a high predictive value. This study aims to review the current evidence on artificial intelligence applications in the detection and analysis of bone loss in the oral and maxillofacial regions.
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Affiliation(s)
- Mehran Farajollahi
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Sadegh Safarian
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Masoud Hatami
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Azadeh Esmaeil Nejad
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ove A Peters
- School of Dentistry, The University of Queensland, Herston, Queensland, Australia
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Zeng P, Song R, Lin Y, Li H, Chen S, Shi M, Cai G, Gong Z, Huang K, Chen Z. Abnormal maxillary sinus diagnosing on CBCT images via object detection and 'straight-forward' classification deep learning strategy. J Oral Rehabil 2023; 50:1465-1480. [PMID: 37665121 DOI: 10.1111/joor.13585] [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: 02/23/2023] [Revised: 04/06/2023] [Accepted: 08/18/2023] [Indexed: 09/05/2023]
Abstract
BACKGROUND Pathological maxillary sinus would affect implant treatment and even result in failure of maxillary sinus lift and implant surgery. However, the maxillary sinus abnormalities are challenging to be diagnosed through CBCT images, especially for young dentists or dentists in grassroots medical institutions without systematical education of general medicine. OBJECTIVES To develop a deep-learning-based screening model incorporating object detection and 'straight-forward' classification strategy to screen out maxillary sinus abnormalities on CBCT images. METHODS The large area of background noise outside maxillary sinus would affect the generalisation and prediction accuracy of the model, and the diversity and imbalanced distribution of imaging manifestations may bring challenges to intellectualization. Thus we adopted an object detection to limit model's observation zone and 'straight-forward' classification strategy with various tuning methods to adapt to dental clinical need and extract typical features of diverse manifestations so that turn the task into a 'normal-or-not' classification. RESULTS We successfully constructed a deep-learning model consist of well-trained detector and diagnostor module. This model achieved ideal AUROC and AUPRC of 0.953 and 0.887, reaching more than 90% accuracy at optimal cut-off. McNemar and Kappa test verified no statistical difference and high consistency between the prediction and ground truth. Dentist-model comparison test showed the model's statistically higher diagnostic performance than dental students. Visualisation method confirmed the model's effectiveness in region recognition and feature extraction. CONCLUSION The deep-learning model incorporating object detection and straightforward classification strategy could achieve satisfying predictive performance for screening maxillary sinus abnormalities on CBCT images.
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Affiliation(s)
- Peisheng Zeng
- Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, China
| | - Rihui Song
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yixiong Lin
- Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, China
| | - Haopeng Li
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Shijie Chen
- Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, China
| | - Mengru Shi
- Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, China
| | - Gengbin Cai
- Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, China
| | - Zhuohong Gong
- Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, China
| | - Kai Huang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Zetao Chen
- Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University and Guangdong Research Center for Dental and Cranial Rehabilitation and Material Engineering, Guangzhou, China
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Portella PD, de Oliveira LF, Ferreira MFDC, Dias BC, de Souza JF, Assunção LRDS. Improving accuracy of early dental carious lesions detection using deep learning-based automated method. Clin Oral Investig 2023; 27:7663-7670. [PMID: 37906303 DOI: 10.1007/s00784-023-05355-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 10/18/2023] [Indexed: 11/02/2023]
Abstract
OBJECTIVE To investigate the effectiveness of a convolutional neural network (CNN) in detecting healthy teeth and early carious lesions on occlusal surfaces and to assess the applicability of this deep learning algorithm as an auxiliary aid. MATERIALS AND METHODS A total of 2,481 posterior teeth (2,459 permanent and 22 deciduous teeth) with varying stages of carious lesions were classified according to the International Caries Detection and Assessment System (ICDAS). After clinical evaluation, ICDAS 0 and 2 occlusal surfaces were photographed with a professional digital camera. VGG-19 was chosen as the CNN and the findings were compared with those of a reference examiner to evaluate its detection efficiency. To verify the effectiveness of the CNN as an auxiliary detection aid, three examiners (an undergraduate student (US), a newly graduated dental surgeon (ND), and a specialist in pediatric dentistry (SP) assessed the acquired images (Phase I). In Phase II, the examiners reassessed the same images using the CNN-generated algorithms. RESULTS The training dataset consisted of 8,749 images, whereas the test dataset included 140 images. VGG-19 achieved an accuracy of 0.879, positive agreement of 0.827, precision of 0.949, negative agreement 0.800, and an F1-score of 0.887. In Phase I, the accuracy rates for examiners US, ND, and SP were 0.543, 0.771, and 0.807, respectively. In Phase II, the accuracy rates improved to 0.679, 0.886, and 0.857 for the respective examiners. The number of correct answers was significantly higher in Phase II than in Phase I for all examiners (McNemar test;P<0.05). CONCLUSIONS VGG-19 demonstrated satisfactory performance in the detection of early carious lesions, as well as an auxiliary detection aid. CLINICAL RELEVANCE Automated detection using deep learning algorithms is an important aid in detecting early caries lesions and improves the accuracy of the disease detection, enabling quicker and more reliable clinical decision-making.
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Affiliation(s)
- Paula Dresch Portella
- Stomatology Department, Universidade Federal do Paraná, Avenida Prefeito Lothário Meissner, 632, Jardim Botânico, Curitiba, PR, 80210-170, Brazil.
| | | | | | - Bruna Cristine Dias
- Stomatology Department, Universidade Federal do Paraná, Avenida Prefeito Lothário Meissner, 632, Jardim Botânico, Curitiba, PR, 80210-170, Brazil
| | - Juliana Feltrin de Souza
- Stomatology Department, Universidade Federal do Paraná, Avenida Prefeito Lothário Meissner, 632, Jardim Botânico, Curitiba, PR, 80210-170, Brazil
| | - Luciana Reichert da Silva Assunção
- Stomatology Department, Universidade Federal do Paraná, Avenida Prefeito Lothário Meissner, 632, Jardim Botânico, Curitiba, PR, 80210-170, Brazil
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30
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Carvalho J, Lotz M, Rubi L, Unger S, Pfister T, Buhmann J, Stadlinger B. Preinterventional Third-Molar Assessment Using Robust Machine Learning. J Dent Res 2023; 102:1452-1459. [PMID: 37944556 PMCID: PMC10683342 DOI: 10.1177/00220345231200786] [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] [Indexed: 11/12/2023] Open
Abstract
Machine learning (ML) models, especially deep neural networks, are increasingly being used for the analysis of medical images and as a supporting tool for clinical decision-making. In this study, we propose an artificial intelligence system to facilitate dental decision-making for the removal of mandibular third molars (M3M) based on 2-dimensional orthopantograms and the risk assessment of such a procedure. A total of 4,516 panoramic radiographic images collected at the Center of Dental Medicine at the University of Zurich, Switzerland, were used for training the ML model. After image preparation and preprocessing, a spatially dependent U-Net was employed to detect and retrieve the region of the M3M and inferior alveolar nerve (IAN). Image patches identified to contain a M3M were automatically processed by a deep neural network for the classification of M3M superimposition over the IAN (task 1) and M3M root development (task 2). A control evaluation set of 120 images, collected from a different data source than the training data and labeled by 5 dental practitioners, was leveraged to reliably evaluate model performance. By 10-fold cross-validation, we achieved accuracy values of 0.94 and 0.93 for the M3M-IAN superimposition task and the M3M root development task, respectively, and accuracies of 0.9 and 0.87 when evaluated on the control data set, using a ResNet-101 trained in a semisupervised fashion. Matthew's correlation coefficient values of 0.82 and 0.75 for task 1 and task 2, evaluated on the control data set, indicate robust generalization of our model. Depending on the different label combinations of task 1 and task 2, we propose a diagnostic table that suggests whether additional imaging via 3-dimensional cone beam tomography is advisable. Ultimately, computer-aided decision-making tools benefit clinical practice by enabling efficient and risk-reduced decision-making and by supporting less experienced practitioners before the surgical removal of the M3M.
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Affiliation(s)
- J.S. Carvalho
- ETH Zurich, Department of Computer Science, Zurich, Switzerland
- ETH AI Center, Zurich, Switzerland
| | - M. Lotz
- University of Zurich, Center for Dental Medicine, Zurich, Switzerland
| | - L. Rubi
- ETH Zurich, Department of Computer Science, Zurich, Switzerland
| | - S. Unger
- University of Zurich, Center for Dental Medicine, Zurich, Switzerland
| | - T. Pfister
- University of Zurich, Center for Dental Medicine, Zurich, Switzerland
| | - J.M. Buhmann
- ETH Zurich, Department of Computer Science, Zurich, Switzerland
- ETH AI Center, Zurich, Switzerland
| | - B. Stadlinger
- University of Zurich, Center for Dental Medicine, Zurich, Switzerland
- ETH AI Center, Zurich, Switzerland
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Cai J, Deng Y, Min Z, Zhang Y, Zhao Z, Jing D. Revealing the representative facial traits of different sagittal skeletal types: decipher what artificial intelligence can see by Grad-CAM. J Dent 2023; 138:104701. [PMID: 37717687 DOI: 10.1016/j.jdent.2023.104701] [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: 03/04/2023] [Revised: 09/10/2023] [Accepted: 09/12/2023] [Indexed: 09/19/2023] Open
Abstract
OBJECTIVES Aesthetic improvement is a significant concern in dental therapy. While orthodontic treatment primarily targets hard tissue, the impact on soft tissue and the extent of these changes remains empirical. This study aims to unveil the intricate relationship between facial soft tissue and skeletal types using artificial intelligence (AI) analysis. METHODS First, we collected a dataset of 1044 3-side-photographs and categorized them based on cephalometric measurements. After pre-processing and data augmentation, samples were fed to two independent models (Sfa, Res model) for training and testing. After validating that the Sfa model could accurately recognize the skeletal types based merely on photographs, Grad-CAM algorithm was utilized for model decipherment. Verification of the vital traits were carried out by facial adjustment simulation. RESULTS The Sfa model demonstrated superior accuracy (0.9293) in identifying skeletal types based solely on soft tissue, compared to the Res model (0.8395) and even trained orthodontists (0.764), testifying our hypothesis that AI could be more capable of processing imperceptible cues compared to mankind. Intriguingly, Grad-CAM revealed that cheek volume, forehead, chin and nasolabial traits could be representative features of each type, exceeding the traditional knowledge which merely concerns mandible and chin. CONCLUSION By constructing a deep learning model as a classifier and then decipher it with Grad-CAM, we revealed the subtle and unnoticed cues associating skeletal and soft tissue, as well as provided a novel approach that could aid practitioners in devising tailored treatment plans for enhanced esthetic outcomes. CLINICAL SIGNIFICANCE The proposed AI methods offer valuable assistance to practitioners in identifying uncoordinated facial traits that may detract from a patient's attractiveness. By incorporating these insights into customized treatment plans, dental therapy can maximize esthetic benefits for individual patient.
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Affiliation(s)
- Jingyi Cai
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan 610041 China
| | - Yudi Deng
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan 610041 China
| | - Ziyang Min
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan 610041 China
| | - Yiyi Zhang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan 610041 China
| | - Zhihe Zhao
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No.14, 3rd Section, South Renmin Road, Chengdu, Sichuan 610041 China.
| | - Dian Jing
- Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Jiao Tong University, No.639, Zhizaoju Road, Huangpu District, Shanghai 200011, China.
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Astudillo-Rozas W, Valdivia-Gandur I, Vasquez AV, Aceituno-Antezana O, Vasquez-Salinas M, Guerra CL, Manzanares-Céspedes MC. Declarative knowledge in oral health: The case of the term 'centric occlusion'. EUROPEAN JOURNAL OF DENTAL EDUCATION : OFFICIAL JOURNAL OF THE ASSOCIATION FOR DENTAL EDUCATION IN EUROPE 2023; 27:908-917. [PMID: 36484223 DOI: 10.1111/eje.12881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/27/2022] [Accepted: 09/30/2022] [Indexed: 06/17/2023]
Abstract
INTRODUCTION The lack of academic agreement in the practical or clinical use of declarative knowledge can generate unnecessary confusion and miscommunication. The concept Centric Occlusion (CO) is part of the body of declarative knowledge in dentistry, but its definition remains unclear. OBJECTIVE To ascertain the CO concept in articles published in dental journals as a study case for the dentistry "corpus" of declarative knowledge. METHODOLOGY The alternative definitions of CO used by the GPT (Glossary of Prosthodontic Terms) from 1956-1977, 'CO as a synonym for maximum intercuspal contact (MIC)', or by the GPT from 1987-2017, 'CO may or may not coincide with MIC', were searched in the articles. The association between the CO definition used and variables such as article aims, journal scope and authors specialty was assessed. RESULTS Eight hundred and twelve articles were analysed. The widespread use of CO as synonym of MIC was the main finding and was significantly associated to the Orthodontics field. The CO definition according to the GPT 1987-2017 was less frequently observed but appeared in all dentistry fields, showing a significant association with the Oral Rehabilitation field. The difficulty of incorporating the current definition of CO (by GPT) into the main clinical discussions was evidenced all the long of the review process. CONCLUSION The lack of consensus in the concept use was confirmed by the present study case, showing the influence of specific fields in Oral Health declarative knowledge. This methodology can provide a tool to the academy to assess controversial terms or concepts in Oral Health education, thus facilitating the critical and reflexive learning by students.
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Affiliation(s)
- Wilson Astudillo-Rozas
- Biomedical Department, Faculty of Health Science, Universidad de Antofagasta, Antofagasta, Chile
- Master of Biomedical Science, Universidad de Antofagasta, Antofagasta, Chile
| | - Iván Valdivia-Gandur
- Biomedical Department, Faculty of Health Science, Universidad de Antofagasta, Antofagasta, Chile
- Dentistry Department, Faculty of Medicine and Odontology, Universidad de Antofagasta, Antofagasta, Chile
| | - Aruny Vasquez Vasquez
- Dentistry Department, Faculty of Medicine and Odontology, Universidad de Antofagasta, Antofagasta, Chile
| | - Oscar Aceituno-Antezana
- Master of Biomedical Science, Universidad de Antofagasta, Antofagasta, Chile
- Dentistry Department, Faculty of Medicine and Odontology, Universidad de Antofagasta, Antofagasta, Chile
| | | | - Claudio Ly Guerra
- Dentistry Department, Faculty of Medicine and Odontology, Universidad de Antofagasta, Antofagasta, Chile
| | - María Cristina Manzanares-Céspedes
- Human Anatomy and Embryology Unit, Experimental Pathology and Therapeutics Department, Faculty of Medicine and Health Sciences, University of Barcelona, Hospitalet, Barcelona, Spain
- UNIPRO - Oral Pathology and Rehabilitation Research Unit, University Institute of Health Sciences (IUCS), CESPU, Gandra, Portugal
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Jindanil T, Marinho-Vieira LE, de-Azevedo-Vaz SL, Jacobs R. A unique artificial intelligence-based tool for automated CBCT segmentation of mandibular incisive canal. Dentomaxillofac Radiol 2023; 52:20230321. [PMID: 37870152 DOI: 10.1259/dmfr.20230321] [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] [Indexed: 10/24/2023] Open
Abstract
OBJECTIVES To develop and validate a novel artificial intelligence (AI) tool for automated segmentation of mandibular incisive canal on cone beam computed tomography (CBCT) scans. METHODS After ethical approval, a data set of 200 CBCT scans were selected and categorized into training (160), validation (20), and test (20) sets. CBCT scans were imported into Virtual Patient Creator and ground truth for training and validation were manually segmented by three oral radiologists in multiplanar reconstructions. Intra- and interobserver analysis for human segmentation variability was performed on 20% of the data set. Segmentations were imported into Mimics for standardization. Resulting files were imported to 3-Matic for analysis using surface- and voxel-based methods. Evaluation metrics involved time efficiency, analysis metrics including Dice Similarity Coefficient (DSC), Intersection over Union (IoU), Root mean square error (RMSE), precision, recall, accuracy, and consistency. These values were calculated considering AI-based segmentation and refined-AI segmentation compared to manual segmentation. RESULTS Average time for AI-based segmentation, refined-AI segmentation and manual segmentation was 00:10, 08:09, and 47:18 (284-fold time reduction). AI-based segmentation showed mean values of DSC 0.873, IoU 0.775, RMSE 0.256 mm, precision 0.837 and recall 0.890 while refined-AI segmentation provided DSC 0.876, IoU 0.781, RMSE 0.267 mm, precision 0. 852 and recall 0.902 with the accuracy of 0.998 for both methods. The consistency was one for AI-based segmentation and 0.910 for manual segmentation. CONCLUSIONS An innovative AI-tool for automated segmentation of mandibular incisive canal on CBCT scans was proofed to be accurate, time efficient, and highly consistent, serving pre-surgical planning.
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Affiliation(s)
- Thanatchaporn Jindanil
- Department of Imaging and Pathology, Faculty of Medicine, OMFS-IMPATH Research Group, KU Leuven, Leuven, Belgium
| | - Luiz Eduardo Marinho-Vieira
- Department of Imaging and Pathology, Faculty of Medicine, OMFS-IMPATH Research Group, KU Leuven, Leuven, Belgium
- Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil
| | | | - Reinhilde Jacobs
- Department of Imaging and Pathology, Faculty of Medicine, OMFS-IMPATH Research Group, KU Leuven, Leuven, Belgium
- Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden
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Alzaid N, Ghulam O, Albani M, Alharbi R, Othman M, Taher H, Albaradie S, Ahmed S. Revolutionizing Dental Care: A Comprehensive Review of Artificial Intelligence Applications Among Various Dental Specialties. Cureus 2023; 15:e47033. [PMID: 37965397 PMCID: PMC10642940 DOI: 10.7759/cureus.47033] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2023] [Indexed: 11/16/2023] Open
Abstract
Since the beginning of recorded history, the human brain has been one of the most intriguing structures for scientists and engineers. Over the centuries, newer technologies have been developed based on principles that seek to mimic their functioning, but the creation of a machine that can think and behave like a human remains an unattainable fantasy. This idea is now known as "artificial intelligence". Dentistry has begun to experience the effects of artificial intelligence (AI). These include image enhancement for radiology, which improves the visibility of dental structures and facilitates disease diagnosis. AI has also been utilized for the identification of periapical lesions and root anatomy in endodontics, as well as for the diagnosis of periodontitis. This review is intended to provide a comprehensive overview of the use of AI in modern dentistry's numerous specialties. The relevant publications published between March 1987 and July 2023 were identified through an exhaustive search. Studies published in English were selected and included data regarding AI applications among various dental specialties. Dental practice involves more than just disease diagnosis, including correlation with clinical findings and administering treatment to patients. AI cannot replace dentists. However, a comprehensive understanding of AI concepts and techniques will be advantageous in the future. AI models for dental applications are currently being developed.
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Affiliation(s)
- Najd Alzaid
- Dentistry, University of Hail College of Dentistry, Hail, SAU
| | - Omar Ghulam
- General Dentistry, Prince Mohammed bin Abdulaziz Hospital, Madinah, SAU
| | - Modhi Albani
- Dentistry, University of Hail College of Dentistry, Hail, SAU
| | - Rafa Alharbi
- Dentistry, Taibah University College of Dentistry, Madinah, SAU
| | - Mayan Othman
- Dentistry, Taibah University College of Dentistry, Madinah, SAU
| | - Hasan Taher
- Endodontics, Prince Mohammed bin Abdulaziz Hospital, Madinah, SAU
| | - Saleem Albaradie
- General Dentistry, Prince Mohammed bin Abdulaziz Hospital, Madinah, SAU
| | - Suhael Ahmed
- Maxillofacial Surgery and Diagnostic Sciences, College of Medicine and Dentistry, Riyadh Elm University, Riyadh, SAU
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Lin L, Tang B, Cao L, Yan J, Zhao T, Hua F, He H. The knowledge, experience, and attitude on artificial intelligence-assisted cephalometric analysis: Survey of orthodontists and orthodontic students. Am J Orthod Dentofacial Orthop 2023; 164:e97-e105. [PMID: 37565946 DOI: 10.1016/j.ajodo.2023.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 07/01/2023] [Accepted: 07/01/2023] [Indexed: 08/12/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) developed rapidly in orthodontics, and AI-based cephalometric applications have been adopted. This study aimed to assess AI-assisted cephalometric technologies related knowledge, experience, and attitude among orthodontists and orthodontic students; describe their subject view of the applications and related technologies in orthodontics; and identify associated factors. METHODS An online cross-sectional survey based on a professional tool (www.wjx.cn) was performed from October 11-17, 2022. Participants were recruited with a purposive and snowball sampling approach. Data was collected and analyzed with descriptive statistics, chi-square tests, and multivariable generalized estimating equations. RESULTS Four hundred eighty valid questionnaires were collected and analyzed; 68.8% of the respondents agreed that AI-based cephalometric applications would replace manual and semiautomatic approaches. Practitioners using AI-assisted applications (87.5%) spent less time in cephalometric analysis than the other groups using other approaches, and 349 (72.7%) respondents considered AI-based applications could assist in obtaining more accurate analysis results. Lectures and training programs (56.0%) were the main sources of respondents' knowledge about AI. Knowledge level was associated with experience in AI-related clinical or scientific projects (P <0.001). Most respondents (88.8%) were interested in future AI applications in orthodontics. CONCLUSIONS Respondents are optimistic about the future of AI in orthodontics. AI-assisted cephalometric applications were believed to make clinical diagnostic analysis more convenient and straightforward for practitioners and even replace manual and semiautomatic approaches. The education and promotion of AI should be strengthened to elevate orthodontists' understanding.
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Affiliation(s)
- Lizhuo Lin
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Orthodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Bojun Tang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Orthodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Lingyun Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Orthodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Jiarong Yan
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Orthodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Tingting Zhao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Orthodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China; Center for Dentofacial Development and Sleep Medicine, School and Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Fang Hua
- Center for Dentofacial Development and Sleep Medicine, School and Hospital of Stomatology, Wuhan University, Wuhan, China; Center for Orthodontics and Pediatric Dentistry at Optics Valley Branch, School and Hospital of Stomatology, Wuhan University, Wuhan, China; Center for Evidence-Based Stomatology, School and Hospital of Stomatology, Wuhan University, Wuhan, China; Division of Dentistry, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom.
| | - Hong He
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, China; Department of Orthodontics, School and Hospital of Stomatology, Wuhan University, Wuhan, China; Center for Dentofacial Development and Sleep Medicine, School and Hospital of Stomatology, Wuhan University, Wuhan, China.
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Linn TY, Salamanca E, Aung LM, Huang TK, Wu YF, Chang WJ. Accuracy of implant site preparation in robotic navigated dental implant surgery. Clin Implant Dent Relat Res 2023; 25:881-891. [PMID: 37199055 DOI: 10.1111/cid.13224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 05/05/2023] [Accepted: 05/09/2023] [Indexed: 05/19/2023]
Abstract
BACKGROUND Modern technological advancements have led to increase in the development of surgical robots in dentistry, resulting in excellent clinical treatment outcomes. PURPOSE This study aimed to determine the accuracy of automatic robotic implant site preparation for different implant sizes by correlating planned and posttreatment positions, and to compare the performance of robotic and human freehand drilling. METHOD Seventy-six drilling sites on partially edentulous models were used, with three different implant sizes (Ø = 3.5 × 10 mm, 4.0 × 10 mm, 5.0 × 10 mm). The robotic procedure was performed using software for calibration and step-by-step drilling processes. After robotic drilling, deviations in the implant position from the planned position were determined. The angulation, depth, and coronal and apical diameters on the sagittal plane of sockets created by human and robotic drilling were measured. RESULTS The deviation of the robotic system was 3.78° ± 1.97° (angulation), 0.58 ± 0.36 mm (entry point), and 0.99 ± 0.56 mm (apical point). Comparison of implant groups showed the largest deviation from the planned position for 5 mm implants. On the sagittal plane, there were no significant differences between robotic and human surgery except for the 5-mm implant angulation, indicating similar quality between human and robotic drilling. Based on standard implant measurements, robotic drilling exhibited comparable performance to freehand human drilling. CONCLUSIONS A robotic surgical system can provide the greatest accuracy and reliability regarding the preoperative plan for small implant diameters. In addition, the accuracy of robotic drilling for anterior implant surgery can also be comparable to that of human drilling.
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Affiliation(s)
- Thu Ya Linn
- School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Eisner Salamanca
- School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Lwin Moe Aung
- School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Ta-Ko Huang
- School of Dentistry, Kaohsiung Medical University, Kaohsiung, Taiwan
- EPED Incorporation, Kaohsiung, Taiwan
| | - Yi-Fan Wu
- School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
| | - Wei-Jen Chang
- School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan
- Dental Department, Shuang-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
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Bonny T, Al Nassan W, Obaideen K, Al Mallahi MN, Mohammad Y, El-damanhoury HM. Contemporary Role and Applications of Artificial Intelligence in Dentistry. F1000Res 2023; 12:1179. [PMID: 37942018 PMCID: PMC10630586 DOI: 10.12688/f1000research.140204.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/24/2023] [Indexed: 11/10/2023] Open
Abstract
Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.
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Affiliation(s)
- Talal Bonny
- Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Wafaa Al Nassan
- Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Khaled Obaideen
- Sustainable Energy and Power Systems Research Centre, RISE, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Maryam Nooman Al Mallahi
- Department of Mechanical and Aerospace Engineering, United Arab Emirates University, Al Ain City, Abu Dhabi, 27272, United Arab Emirates
| | - Yara Mohammad
- College of Engineering and Information Technology, Ajman University, Ajman University, Ajman, Ajman, United Arab Emirates
| | - Hatem M. El-damanhoury
- Department of Preventive and Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, 27272, United Arab Emirates
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Abad-Coronel C, Pazán DP, Hidalgo L, Larriva Loyola J. Comparative Analysis between 3D-Printed Models Designed with Generic and Dental-Specific Software. Dent J (Basel) 2023; 11:216. [PMID: 37754336 PMCID: PMC10529710 DOI: 10.3390/dj11090216] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/28/2023] [Accepted: 09/11/2023] [Indexed: 09/28/2023] Open
Abstract
With the great demand in the market for new dental software, the need has been seen to carry out a precision study for applications in digital dentistry, for which there is no comparative study, and there is a general ignorance regarding their applications. The purpose of this study was to investigate the accuracy differences between digital impressions obtained using generic G-CAD (general CAD) and D-CAD (CAD dental) software. Today, there is a difference between the design software used in dentistry and these in common use. Thus, it is necessary to make a comparison of precision software for specific and generic dental use. We hypothesized that there is no significant difference between the software for specific and general dental use. METHODS A typodont was digitized with an intraoral scanner and the models obtained were exported in STL format to four different softwares (Autodesk MeshMixer 3.5, Exocad Dental, Blender for dental, and InLAB). The STL files obtained by each software were materialized using a 3D printer. The printed models were scanned and exported in STL files, with which six pairs of groups were formed. The groups were compared using analysis software (3D Geomagic Control X) by superimposing them in the initial alignment order and using the best fit method. RESULTS There were no significant differences between the four analyzed software types; however, group 4, composed of the combination of D-CAD (Blender-InLAB), obtained the highest average (-0.0324 SD = 0.0456), with a higher accuracy compared to the group with the lowest average (group 5, composed of the combination of the Meshmixer and Blender models), a generic software and a specific software (0.1024 SD = 0.0819). CONCLUSION Although no evidence of significant difference was found regarding the accuracy of 3D models produced by G-CAD and D-CAD, combinations of groups where specific dental design software was present showed higher accuracy (precision and trueness). The comparison of the 3D graphics obtained with the superimposition of the digital meshes of the printed models performed with the help of the analysis software using the best fit method, replicating the same five reference points for the six groups formed, evidenced a greater tolerance in the groups using D-CAD.
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Affiliation(s)
- Cristian Abad-Coronel
- CAD/CAM Materials and Digital Dentistry Research Group, Faculty of Dentistry, Universidad de Cuenca, Cuenca 010107, Ecuador
| | - Doménica Patricia Pazán
- Faculty of Dentistry, Universidad de Cuenca, Cuenca 010101, Ecuador; (D.P.P.); (L.H.); (J.L.L.)
| | - Lorena Hidalgo
- Faculty of Dentistry, Universidad de Cuenca, Cuenca 010101, Ecuador; (D.P.P.); (L.H.); (J.L.L.)
| | - Jaime Larriva Loyola
- Faculty of Dentistry, Universidad de Cuenca, Cuenca 010101, Ecuador; (D.P.P.); (L.H.); (J.L.L.)
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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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Herbst SR, Pitchika V, Krois J, Krasowski A, Schwendicke F. Machine Learning to Predict Apical Lesions: A Cross-Sectional and Model Development Study. J Clin Med 2023; 12:5464. [PMID: 37685531 PMCID: PMC10488275 DOI: 10.3390/jcm12175464] [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: 07/31/2023] [Revised: 08/16/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
(1) Background: We aimed to identify factors associated with the presence of apical lesions (AL) in panoramic radiographs and to evaluate the predictive value of the identified factors. (2) Methodology: Panoramic radiographs from 1071 patients (age: 11-93 a, mean: 50.6 a ± 19.7 a) with 27,532 teeth were included. Each radiograph was independently assessed by five experienced dentists for AL. A range of shallow machine learning algorithms (logistic regression, k-nearest neighbor, decision tree, random forest, support vector machine, adaptive and gradient boosting) were employed to identify factors at both the patient and tooth level associated with AL and to predict AL. (3) Results: AL were detected in 522 patients (48.7%) and 1133 teeth (4.1%), whereas males showed a significantly higher prevalence than females (52.5%/44.8%; p < 0.05). Logistic regression found that an existing root canal treatment was the most important risk factor (adjusted Odds Ratio 16.89; 95% CI: 13.98-20.41), followed by the tooth type 'molar' (2.54; 2.1-3.08) and the restoration with a crown (2.1; 1.67-2.63). Associations between factors and AL were stronger and accuracy higher when using fewer complex models like decision tree (F1 score: 0.9 (0.89-0.9)). (4) Conclusions: The presence of AL was higher in root-canal treated teeth, those with crowns and molars. More complex machine learning models did not outperform less-complex ones.
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Affiliation(s)
| | | | | | | | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Aßmannshauser Street 4-6, 14197 Berlin, Germany; (S.R.H.); (V.P.); (J.K.); (A.K.)
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Bennasar C, García I, Gonzalez-Cid Y, Pérez F, Jiménez J. Second Opinion for Non-Surgical Root Canal Treatment Prognosis Using Machine Learning Models. Diagnostics (Basel) 2023; 13:2742. [PMID: 37685280 PMCID: PMC10487079 DOI: 10.3390/diagnostics13172742] [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: 07/02/2023] [Revised: 08/08/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
Although the association between risk factors and non-surgical root canal treatment (NSRCT) failure has been extensively studied, methods to predict the outcomes of NSRCT are in an early stage, and dentists currently make the treatment prognosis based mainly on their clinical experience. Since this involves different sources of error, we investigated the use of machine learning (ML) models as a second opinion to support the clinical decision on whether to perform NSRCT. We undertook a retrospective study of 119 confirmed and not previously treated Apical Periodontitis cases that received the same treatment by the same specialist. For each patient, we recorded the variables from a newly proposed data collection template and defined a binary outcome: Success if the lesion clears and failure otherwise. We conducted tests for detecting the association between the variables and the outcome and selected a set of variables as the initial inputs into four ML algorithms: Logistic Regression (LR), Random Forest (RF), Naive-Bayes (NB), and K Nearest Neighbors (KNN). According to our results, RF and KNN significantly improve (p-values < 0.05) the sensitivity and accuracy of the dentist's treatment prognosis. Taking our results as a proof of concept, we conclude that future randomized clinical trials are worth designing to test the clinical utility of ML models as a second opinion for NSRCT prognosis.
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Affiliation(s)
- Catalina Bennasar
- ADEMA, School of Dentistry, University of the Balearic Islands, 07122 Palma de Mallorca, Spain;
| | - Irene García
- Department of Mathematical Sciences and Informatics, University of the Balearic Islands, 07120 Palma de Mallorca, Spain; (I.G.); (Y.G.-C.)
| | - Yolanda Gonzalez-Cid
- Department of Mathematical Sciences and Informatics, University of the Balearic Islands, 07120 Palma de Mallorca, Spain; (I.G.); (Y.G.-C.)
| | - Francesc Pérez
- Dental Public Health Service, IB-Salut, Balearic Islands, 07003 Palma de Mallorca, Spain;
- TotIA Artificial Intelligence for Dentistry, 07006 Palma de Mallorca, Spain
| | - Juan Jiménez
- ADEMA, School of Dentistry, University of the Balearic Islands, 07122 Palma de Mallorca, Spain;
- TotIA Artificial Intelligence for Dentistry, 07006 Palma de Mallorca, Spain
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Wong KF, Lam XY, Jiang Y, Yeung AWK, Lin Y. Artificial intelligence in orthodontics and orthognathic surgery: a bibliometric analysis of the 100 most-cited articles. Head Face Med 2023; 19:38. [PMID: 37612673 PMCID: PMC10463886 DOI: 10.1186/s13005-023-00383-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 08/10/2023] [Indexed: 08/25/2023] Open
Abstract
BACKGROUND The application of artificial intelligence (AI) in orthodontics and orthognathic surgery has gained significant attention in recent years. However, there is a lack of bibliometric reports that analyze the academic literature in this field to identify publishing and citation trends. By conducting an analysis of the top 100 most-cited articles on AI in orthodontics and orthognathic surgery, we aim to unveil popular research topics, key authors, institutions, countries, and journals in this area. METHODS A comprehensive search was conducted in the Web of Science (WOS) electronic database to identify the top 100 most-cited articles on AI in orthodontics and orthognathic surgery. Publication and citation data were obtained and further analyzed and visualized using R Biblioshiny. The key domains of the 100 articles were also identified. RESULTS The top 100 most-cited articles were published between 2005 and 2022, contributed by 458 authors, with an average citation count of 22.09. South Korea emerged as the leading contributor with the highest number of publications (28) and citations (595), followed by China (16, 373), and the United States (7, 248). Notably, six South Korean authors ranked among the top 10 contributors, and three South Korean institutions were listed as the most productive. International collaborations were predominantly observed between the United States, China, and South Korea. The main domains of the articles focused on automated imaging assessment (42%), aiding diagnosis and treatment planning (34%), and the assessment of growth and development (10%). Besides, a positive correlation was observed between the testing sample size and citation counts (P = 0.010), as well as between the time of publication and citation counts (P < 0.001). CONCLUSIONS The utilization of AI in orthodontics and orthognathic surgery has shown remarkable progress, particularly in the domains of imaging analysis, diagnosis and treatment planning, and growth and development assessment. This bibliometric analysis provides valuable insights into the top-cited articles and the trends of AI research in this field.
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Affiliation(s)
- Ka Fai Wong
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, the University of Hong Kong, Prince Philip Dental Hospital, No.34 Hospital Road, Hong Kong SAR, China
| | - Xiang Yao Lam
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, the University of Hong Kong, Prince Philip Dental Hospital, No.34 Hospital Road, Hong Kong SAR, China
| | - Yuhao Jiang
- Department of Restorative Dentistry, Faculty of Dentistry, the National University of Malaysia, Kuala Lumpur, Malaysia
| | - Andy Wai Kan Yeung
- Division of Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, the University of Hong Kong, Hong Kong SAR, China
| | - Yifan Lin
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, the University of Hong Kong, Prince Philip Dental Hospital, No.34 Hospital Road, Hong Kong SAR, China.
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Ozsari S, Güzel MS, Yılmaz D, Kamburoğlu K. A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases. Diagnostics (Basel) 2023; 13:2700. [PMID: 37627959 PMCID: PMC10453523 DOI: 10.3390/diagnostics13162700] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/13/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
Today, with rapid advances in technology, computer-based studies and Artificial Intelligence (AI) approaches are finding their place in every field, especially in the medical sector, where they attract great attention. The Temporomandibular Joint (TMJ) stands as the most intricate joint within the human body, and diseases related to this joint are quite common. In this paper, we reviewed studies that utilize AI-based algorithms and computer-aided programs for investigating TMJ and TMJ-related diseases. We conducted a literature search on Google Scholar, Web of Science, and PubMed without any time constraints and exclusively selected English articles. Moreover, we examined the references to papers directly related to the topic matter. As a consequence of the survey, a total of 66 articles within the defined scope were assessed. These selected papers were distributed across various areas, with 11 focusing on segmentation, 3 on Juvenile Idiopathic Arthritis (JIA), 10 on TMJ Osteoarthritis (OA), 21 on Temporomandibular Joint Disorders (TMD), 6 on decision support systems, 10 reviews, and 5 on sound studies. The observed trend indicates a growing interest in artificial intelligence algorithms, suggesting that the number of studies in this field will likely continue to expand in the future.
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Affiliation(s)
- Sifa Ozsari
- Department of Computer Engineering, Ankara University, 06830 Ankara, Turkey;
| | - Mehmet Serdar Güzel
- Department of Computer Engineering, Ankara University, 06830 Ankara, Turkey;
| | - Dilek Yılmaz
- Faculty of Dentistry, Baskent University, 06490 Ankara, Turkey;
| | - Kıvanç Kamburoğlu
- Department of Dentomaxillofacial Radiology, Ankara University, 06560 Ankara, Turkey;
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Chen Q, Wang Y, Shuai J. Current status and future prospects of stomatology research. J Zhejiang Univ Sci B 2023; 24:853-867. [PMID: 37752088 PMCID: PMC10522564 DOI: 10.1631/jzus.b2200702] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 03/27/2023] [Indexed: 08/08/2023]
Abstract
Research in stomatology (dental medicine) continues to expand globally and is oriented towards solving clinical issues, focusing on clarifying the clinical relevance and potential mechanisms of oral-systemic connections via clinical epidemiology, oral microecological characterization, and the establishment of animal models. Interdisciplinary integration of materials science and tissue engineering with stomatology is expected to lead to the creation of innovative materials and technologies to better resolve the most prevalent and challenging clinical issues such as peri-implantitis, soft and hard tissue defects, and dentin hypersensitivity. With the rapid development of artificial intelligence (AI), 5th generation mobile communication technology (5G), and big data applications, "intelligent stomatology" is emerging to build models for better clinical diagnosis and management, accelerate the reform of education, and support the growth and advancement of scientific research. Here, we summarized the current research status, and listed the future prospects and limitations of these three aspects, aiming to provide a basis for more accurate etiological exploration, novel treatment methods, and abundant big data analysis in stomatology to promote the translation of research achievements into practical applications for both clinicians and the public.
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Affiliation(s)
- Qianming Chen
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou 310006, China.
| | - Yahui Wang
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou 310006, China
| | - Jing Shuai
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou 310006, China
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45
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Icoz D, Terzioglu H, Ozel MA, Karakurt R. Evaluation of an artificial intelligence system for the diagnosis of apical periodontitis on digital panoramic images. Niger J Clin Pract 2023; 26:1085-1090. [PMID: 37635600 DOI: 10.4103/njcp.njcp_624_22] [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] [Indexed: 08/29/2023]
Abstract
Aims The aim of the present study was to evaluate the effectiveness of an artificial intelligence (AI) system in the detection of roots with apical periodontitis (AP) on digital panoramic radiographs. Materials and Methods Three hundred and six panoramic radiographs containing 400 roots with AP (an equal number for both jaws) were used to test the diagnostic performance of an AI system. Panoramic radiographs of the patients were selected with the terms 'apical lesion' and 'apical periodontitis' from the archive and then with the agreement of two oral and maxillofacial radiologists. The radiologists also carried out the grouping and determination of the lesion borders. A deep learning (DL) model was built and the diagnostic performance of the model was evaluated by using recall, precision, and F measure. Results The recall, precision, and F-measure scores were 0.98, 0.56, and 0.71, respectively. While the number of roots with AP detected correctly in the mandible was 169 of 200 roots, it was only 56 of 200 roots in the maxilla. Only four roots without AP were incorrectly identified as those with AP. Conclusions The DL method developed for the automatic detection of AP on digital panoramic radiographs showed high recall, precision, and F measure values for the mandible, but low values for the maxilla, especially for the widened periodontal ligament (PL)/uncertain AP.
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Affiliation(s)
- D Icoz
- Oral and Maxillofacial Radiology, Faculty of Dentistry, Selcuk University, Turkey
| | - H Terzioglu
- Electrical Electronics Engineering, Faculty of Technology, Selcuk University, Turkey
| | - M A Ozel
- Private Practice, Department of Research and Development, Aydin Spare Parts Industry, Turkey
| | - R Karakurt
- Beyhekim Oral and Dental Health Center, Department of Oral and Maxillofacial Radiology, Konya, Turkey
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Wang R, Hass V, Wang Y. Machine Learning Analysis of Microtensile Bond Strength of Dental Adhesives. J Dent Res 2023; 102:1022-1030. [PMID: 37464796 PMCID: PMC10477772 DOI: 10.1177/00220345231175868] [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] [Indexed: 07/20/2023] Open
Abstract
Dental adhesives provide retention to composite fillings in dental restorations. Microtensile bond strength (µTBS) test is the most used laboratory test to evaluate bonding performance of dental adhesives. The traditional approach for developing dental adhesives involves repetitive laboratory measurements, which consumes enormous time and resources. Machine learning (ML) is a promising tool for accelerating this process. This study aimed to develop ML models to predict the µTBS of dental adhesives using their chemical features and to identify important contributing factors for µTBS. Specifically, the chemical composition and µTBS information of 81 dental adhesives were collected from the manufacturers and the literature. The average µTBS value of each adhesive was labeled as either 0 (if <36 MPa) or 1 (if ≥36 MPa) to denote the low and high µTBS classes. The initial 9-feature data set comprised pH, HEMA, BisGMA, UDMA, MDP, PENTA, filler, fluoride, and organic solvent (OS) as input features. Nine ML algorithms, including logistic regression, k-nearest neighbor, support vector machine, decision trees and tree-based ensembles, and multilayer perceptron, were implemented for model development. Feature importance analysis identified MDP, pH, OS, and HEMA as the top 4 contributing features, which were used to construct a 4-feature data set. Grid search with stratified 10-fold cross-validation (CV) was employed for hyperparameter tunning and model performance evaluation using 2 metrics, the area under the receiver operating characteristic curve (AUC) and accuracy. The 4-feature data set generated slightly better performance than the 9-feature data set, with the highest AUC score of 0.90 and accuracy of 0.81 based on stratified CV. In conclusion, ML is an effective tool for predicting dental adhesives with low and high µTBS values and for identifying important chemical features contributing to the µTBS. The ML-based data-driven approach has great potential to accelerate the discovery of new dental adhesives and other dental materials.
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Affiliation(s)
- R. Wang
- Department of Oral and Craniofacial Sciences, University of Missouri-Kansas City, MO, USA
| | - V. Hass
- Department of Oral and Craniofacial Sciences, University of Missouri-Kansas City, MO, USA
| | - Y. Wang
- Department of Oral and Craniofacial Sciences, University of Missouri-Kansas City, MO, USA
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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: 0] [Impact Index Per Article: 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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Ayad N, Schwendicke F, Krois J, van den Bosch S, Bergé S, Bohner L, Hanisch M, Vinayahalingam S. Patients' perspectives on the use of artificial intelligence in dentistry: a regional survey. Head Face Med 2023; 19:23. [PMID: 37349791 PMCID: PMC10288769 DOI: 10.1186/s13005-023-00368-z] [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: 01/05/2023] [Accepted: 06/13/2023] [Indexed: 06/24/2023] Open
Abstract
The use of artificial intelligence (AI) in dentistry is rapidly evolving and could play a major role in a variety of dental fields. This study assessed patients' perceptions and expectations regarding AI use in dentistry. An 18-item questionnaire survey focused on demographics, expectancy, accountability, trust, interaction, advantages and disadvantages was responded to by 330 patients; 265 completed questionnaires were included in this study. Frequencies and differences between age groups were analysed using a two-sided chi-squared or Fisher's exact tests with Monte Carlo approximation. Patients' perceived top three disadvantages of AI use in dentistry were (1) the impact on workforce needs (37.7%), (2) new challenges on doctor-patient relationships (36.2%) and (3) increased dental care costs (31.7%). Major expected advantages were improved diagnostic confidence (60.8%), time reduction (48.3%) and more personalised and evidencebased disease management (43.0%). Most patients expected AI to be part of the dental workflow in 1-5 (42.3%) or 5-10 (46.8%) years. Older patients (> 35 years) expected higher AI performance standards than younger patients (18-35 years) (p < 0.05). Overall, patients showed a positive attitude towards AI in dentistry. Understanding patients' perceptions may allow professionals to shape AI-driven dentistry in the future.
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Affiliation(s)
- Nasim Ayad
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics and Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
| | - Joachim Krois
- Department of Oral Diagnostics and Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
| | - Stefanie van den Bosch
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands
| | - Stefaan Bergé
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands
| | - Lauren Bohner
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
| | - Marcel Hanisch
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
| | - Shankeeth Vinayahalingam
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands
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Gomez-Rios I, Egea-Lopez E, Ortiz Ruiz AJ. ORIENTATE: automated machine learning classifiers for oral health prediction and research. BMC Oral Health 2023; 23:408. [PMID: 37340367 DOI: 10.1186/s12903-023-03112-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 06/06/2023] [Indexed: 06/22/2023] Open
Abstract
BACKGROUND The application of data-driven methods is expected to play an increasingly important role in healthcare. However, a lack of personnel with the necessary skills to develop these models and interpret its output is preventing a wider adoption of these methods. To address this gap, we introduce and describe ORIENTATE, a software for automated application of machine learning classification algorithms by clinical practitioners lacking specific technical skills. ORIENTATE allows the selection of features and the target variable, then automatically generates a number of classification models and cross-validates them, finding the best model and evaluating it. It also implements a custom feature selection algorithm for systematic searches of the best combination of predictors for a given target variable. Finally, it outputs a comprehensive report with graphs that facilitates the explanation of the classification model results, using global interpretation methods, and an interface for the prediction of new input samples. Feature relevance and interaction plots provided by ORIENTATE allow to use it for statistical inference, which can replace and/or complement classical statistical studies. RESULTS Its application to a dataset with healthy and special health care needs (SHCN) children, treated under deep sedation, was discussed as case study. On the example dataset, despite its small size, the feature selection algorithm found a set of features able to predict the need for a second sedation with a f1 score of 0.83 and a ROC (AUC) of 0.92. Eight predictive factors for both populations were found and ordered by the relevance assigned to them by the model. A discussion of how to derive inferences from the relevance and interaction plots and a comparison with a classical study is also provided. CONCLUSIONS ORIENTATE automatically finds suitable features and generates accurate classifiers which can be used in preventive tasks. In addition, researchers without specific skills on data methods can use it for the application of machine learning classification and as a complement to classical studies for inferential analysis of features. In the case study, a high prediction accuracy for a second sedation in SHCN children was achieved. The analysis of the relevance of the features showed that the number of teeth with pulpar treatments at the first sedation is a predictive factor for a second sedation.
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Affiliation(s)
- Inmaculada Gomez-Rios
- Department of Dermatology, Stomatology, Radiology and Physical Medicine, Universidad de Murcia, Murcia, Spain
| | - Esteban Egea-Lopez
- Dept. Information Technologies and Communications, Universidad Politecnica de Cartagena (UPCT), Cartagena, Spain.
| | - Antonio José Ortiz Ruiz
- Department of Dermatology, Stomatology, Radiology and Physical Medicine, Universidad de Murcia, Murcia, Spain
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Yuce F, Öziç MÜ, Tassoker M. Detection of pulpal calcifications on bite-wing radiographs using deep learning. Clin Oral Investig 2023; 27:2679-2689. [PMID: 36564651 DOI: 10.1007/s00784-022-04839-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 12/21/2022] [Indexed: 12/25/2022]
Abstract
OBJECTIVES Pulpal calcifications are discrete hard calcified masses of varying sizes in the dental pulp cavity. This study is aimed at measuring the performance of the YOLOv4 deep learning algorithm to automatically determine whether there is calcification in the pulp chambers in bite-wing radiographs. MATERIALS AND METHODS In this study, 2000 bite-wing radiographs were collected from the faculty database. The oral radiologists labeled the pulp chambers on the radiographs as "Present" and "Absent" according to whether there was calcification. The data were randomly divided into 80% training, 10% validation, and 10% testing. The weight file for pulpal calcification was obtained by training the YOLOv4 algorithm with the transfer learning method. Using the weights obtained, pulp chambers and calcifications were automatically detected on the test radiographs that the algorithm had never seen. Two oral radiologists evaluated the test results, and performance criteria were calculated. RESULTS The results obtained on the test data were evaluated in two stages: detection of pulp chambers and detection of pulpal calcification. The detection performance of pulp chambers was as follows: recall 86.98%, precision 98.94%, F1-score 91.60%, and accuracy 86.18%. Pulpal calcification "Absent" and "Present" detection performance was as follows: recall 86.39%, precision 85.23%, specificity 97.94%, F1-score 85.49%, and accuracy 96.54%. CONCLUSION The YOLOv4 algorithm trained with bite-wing radiographs detected pulp chambers and calcification with high success rates. CLINICAL RELEVANCE Automatic detection of pulpal calcifications with deep learning will be used in clinical practice as a decision support system with high accuracy rates in diagnosing dentists.
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Affiliation(s)
- Fatma Yuce
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Okan University, Istanbul, Turkey
| | - Muhammet Üsame Öziç
- Faculty of Technology Department of Biomedical Engineering, Pamukkale University, Denizli, Turkey
| | - Melek Tassoker
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Konya, Turkey.
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