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Wu Z, Zhang C, Ye X, Dai Y, Zhao J, Zhao W, Zheng Y. Comparison of the Efficacy of Artificial Intelligence-Powered Software in Crown Design: An In Vitro Study. Int Dent J 2024:S0020-6539(24)00196-5. [PMID: 39069456 DOI: 10.1016/j.identj.2024.06.023] [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: 05/15/2024] [Revised: 06/20/2024] [Accepted: 06/24/2024] [Indexed: 07/30/2024] Open
Abstract
INTRODUCTION AND AIMS Artificial intelligence (AI) has been adopted in the field of dental restoration. This study aimed to evaluate the time efficiency and morphological accuracy of crowns designed by two AI-powered software programs in comparison with conventional computer-aided design software. METHODS A total of 33 clinically adapted posterior crowns were involved in the standard group. AI Automate (AA) and AI Dentbird Crown (AD) used two AI-powered design software programs, while the computer-aided experienced and computer-aided novice employed the Exocad DentalCAD software. Time efficiency between the AI-powered groups and computer-aided groups was evaluated by assessing the elapsed time. Morphological accuracy was assessed by means of three-dimensional geometric calculations, with the root-mean-square error compared against the standard group. Statistical analysis was conducted via the Kruskal-Wallis test (α = 0.05). RESULTS The time efficiency of the AI-powered groups was significantly higher than that of the computer-aided groups (P < .01). Moreover, the working time for both AA and AD groups was only one-quarter of that for the computer-aided novice group. Four groups significantly differed in morphological accuracy for occlusal and distal surfaces (P < .05). The AD group performed lower accuracy than the other three groups on the occlusal surfaces (P < .001) and the computer-aided experienced group was superior to the AA group in terms of accuracy on the distal surfaces (P = .029). However, morphological accuracy showed no significant difference among the four groups for mesial surfaces and margin lines (P > .05). CONCLUSION AI-powered software enhanced the efficiency of crown design but failed to excel at morphological accuracy compared with experienced technicians using computer-aided software. AI-powered software requires further research and extensive deep learning to improve the morphological accuracy and stability of the crown design.
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Affiliation(s)
- Ziqiong Wu
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, China
| | - Chengqi Zhang
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xinjian Ye
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Centre for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Centre of Zhejiang University, Hangzhou, China
| | - Yuwei Dai
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Centre for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Centre of Zhejiang University, Hangzhou, China; Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jing Zhao
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, China
| | - Wuyuan Zhao
- Hangzhou Erran Technology Co., Ltd., Hangzhou, China
| | - Yuanna Zheng
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, China; Ningbo Dental Hospital/Ningbo Oral Health Research Institute, Ningbo, China.
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La Rosa S, Quinzi V, Palazzo G, Ronsivalle V, Lo Giudice A. The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence-Based Literature. Healthcare (Basel) 2024; 12:1311. [PMID: 38998846 PMCID: PMC11240988 DOI: 10.3390/healthcare12131311] [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: 05/24/2024] [Revised: 06/19/2024] [Accepted: 06/29/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has emerged as a revolutionary technology with several applications across different dental fields, including pedodontics. This systematic review has the objective to catalog and explore the various uses of artificial intelligence in pediatric dentistry. METHODS A thorough exploration of scientific databases was carried out to identify studies addressing the usage of AI in pediatric dentistry until December 2023 in the Embase, Scopus, PubMed, and Web of Science databases by two researchers, S.L.R. and A.L.G. RESULTS From a pool of 1301 articles, only 64 met the predefined criteria and were considered for inclusion in this review. From the data retrieved, it was possible to provide a narrative discussion of the potential implications of AI in the specialized area of pediatric dentistry. The use of AI algorithms and machine learning techniques has shown promising results in several applications of daily dental pediatric practice, including the following: (1) assisting the diagnostic and recognizing processes of early signs of dental pathologies, (2) enhancing orthodontic diagnosis by automating cephalometric tracing and estimating growth and development, (3) assisting and educating children to develop appropriate behavior for dental hygiene. CONCLUSION AI holds significant potential in transforming clinical practice, improving patient outcomes, and elevating the standards of care in pediatric patients. Future directions may involve developing cloud-based platforms for data integration and sharing, leveraging large datasets for improved predictive results, and expanding AI applications for the pediatric population.
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Affiliation(s)
- Salvatore La Rosa
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
| | - Vincenzo Quinzi
- Department of Life, Health & Environmental Sciences, Postgraduate School of Orthodontics, University of L’Aquila, 67100 L’Aquila, Italy
| | - Giuseppe Palazzo
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
| | - Vincenzo Ronsivalle
- Section of Oral Surgery, Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, Policlinico Universitario “Gaspare Rodolico—San Marco”, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy;
| | - Antonino Lo Giudice
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
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Minhas S, Wu TH, Kim DG, Chen S, Wu YC, Ko CC. Artificial Intelligence for 3D Reconstruction from 2D Panoramic X-rays to Assess Maxillary Impacted Canines. Diagnostics (Basel) 2024; 14:196. [PMID: 38248072 PMCID: PMC10813869 DOI: 10.3390/diagnostics14020196] [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: 11/16/2023] [Revised: 01/03/2024] [Accepted: 01/10/2024] [Indexed: 01/23/2024] Open
Abstract
The objective of this study was to explore the feasibility of current 3D reconstruction in assessing the position of maxillary impacted canines from 2D panoramic X-rays. A dataset was created using pre-treatment CBCT data from a total of 123 patients, comprising 74 patients with impacted canines and 49 patients without impacted canines. From all 74 subjects, we generated a dataset containing paired 2D panoramic X-rays and pseudo-3D images. This pseudo-3D image contained information about the location of the impacted canine in the buccal/lingual, mesial/distal, and apical/coronal positions. These data were utilized to train a deep-learning reconstruction algorithm, a generative AI. The location of the crown of the maxillary impacted canine was determined based on the output of the algorithm. The reconstruction was evaluated using the structure similarity index measure (SSIM) as a metric to indicate the quality of the reconstruction. The prediction of the impacted canine's location was assessed in both the mesiodistal and buccolingual directions. The reconstruction algorithm predicts the position of the impacted canine in the buccal, middle, or lingual position with 41% accuracy, while the mesial and distal positions are predicted with 55% accuracy. The mean SSIM for the output is 0.71, with a range of 0.63 to 0.84. Our study represents the first application of AI reconstruction output for multidisciplinary care involving orthodontists, periodontists, and maxillofacial surgeons in diagnosing and treating maxillary impacted canines. Further development of deep-learning algorithms is necessary to enhance the robustness of dental reconstruction applications.
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Affiliation(s)
- Sumeet Minhas
- Division of Orthodontics, The Ohio State University College of Dentistry, Columbus, OH 43210, USA
| | - Tai-Hsien Wu
- Division of Orthodontics, The Ohio State University College of Dentistry, Columbus, OH 43210, USA
| | - Do-Gyoon Kim
- Division of Orthodontics, The Ohio State University College of Dentistry, Columbus, OH 43210, USA
| | - Si Chen
- Department of Orthodontics, Peking University School and Hospital of Stomatology, Beijing 100082, China
| | - Yi-Chu Wu
- Division of Periodontology, The Ohio State University College of Dentistry, Columbus, OH 43210, USA
| | - Ching-Chang Ko
- Division of Orthodontics, The Ohio State University College of Dentistry, Columbus, OH 43210, USA
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Hartoonian S, Hosseini M, Yousefi I, Mahdian M, Ghazizadeh Ahsaie M. Applications of artificial intelligence in dentomaxillofacial imaging-a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol 2024:S2212-4403(23)01566-3. [PMID: 38637235 DOI: 10.1016/j.oooo.2023.12.790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/02/2023] [Accepted: 12/22/2023] [Indexed: 04/20/2024]
Abstract
BACKGROUND Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities. STUDY DESIGN A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another. RESULTS The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection. CONCLUSION Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.
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Affiliation(s)
- Serlie Hartoonian
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Matine Hosseini
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Iman Yousefi
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mina Mahdian
- Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Mitra Ghazizadeh Ahsaie
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Kapoor S, Shyagali TR, Kuraria A, Gupta A, Tiwari A, Goyal P. An artificial neural network approach for rational decision-making in borderline orthodontic cases: A preliminary analytical observational in silico study. J Orthod 2023; 50:439-448. [PMID: 37148164 DOI: 10.1177/14653125231172527] [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: 05/07/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) technology has transformed the way healthcare functions in the present scenario. In orthodontics, expert systems and machine learning have aided clinicians in making complex, multifactorial decisions. One such scenario is an extraction decision in a borderline case. OBJECTIVE The present in silico study was planned with the intention of building an AI model for extraction decisions in borderline orthodontic cases. DESIGN An observational analytical study. SETTING Department of Orthodontics, Hitkarini Dental College and Hospital, Madhya Pradesh Medical University, Jabalpur, India. METHODS An artificial neural network (ANN) model for extraction or non-extraction decisions in borderline orthodontic cases was constructed based on a supervised learning algorithm using the Python (version 3.9) Sci-Kit Learn library and feed-forward backpropagation method. Based on 40 borderline orthodontic cases, 20 experienced clinicians were asked to recommend extraction or non-extraction treatment. The decision of the orthodontist and the diagnostic records, including the selected extraoral and intra-oral features, model analysis and cephalometric analysis parameters, constituted the training dataset of AI. The built-in model was then tested using a testing dataset of 20 borderline cases. After running the model on the testing dataset, the accuracy, F1 score, precision and recall were calculated. RESULTS The present AI model showed an accuracy of 97.97% for extraction and non-extraction decision-making. The receiver operating curve (ROC) and cumulative accuracy profile showed a near-perfect model with precision, recall and F1 values of 0.80, 0.84 and 0.82 for non-extraction decisions and 0.90, 0.87 and 0.88 for extraction decisions. LIMITATION As the present study was preliminary in nature, the dataset included was too small and population-specific. CONCLUSION The present AI model gave accurate results in decision-making capabilities related to extraction and non-extraction treatment modalities in borderline orthodontic cases of the present population.
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Affiliation(s)
- Shanya Kapoor
- Department of Orthodontics and Dentofacial, Hitkarini Dental College and Hospital, Jabalpur, Madhya Pradesh, India
| | - Tarulatha R Shyagali
- Department of Orthodontics and Dentofacial, MR Ambedkar Dental College and Hospital, Bangalore, Karnataka, India
| | - Amit Kuraria
- Department of Computer Sciences, Rabindranath Tagore University, Bhopal, Madhya Pradesh, India
| | - Abhishek Gupta
- Department of Orthodontics and Dentofacial, Hitkarini Dental College and Hospital, Jabalpur, Madhya Pradesh, India
| | - Anil Tiwari
- Department of Orthodontics and Dentofacial, Hitkarini Dental College and Hospital, Jabalpur, Madhya Pradesh, India
| | - Payal Goyal
- Department of Orthodontics and Dentofacial, Hitkarini Dental College and Hospital, Jabalpur, Madhya Pradesh, India
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Ghods K, Azizi A, Jafari A, Ghods K. Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature. JOURNAL OF DENTISTRY (SHIRAZ, IRAN) 2023; 24:356-371. [PMID: 38149231 PMCID: PMC10749440 DOI: 10.30476/dentjods.2023.96835.1969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 01/04/2023] [Accepted: 03/05/2023] [Indexed: 12/28/2023]
Abstract
Statement of the Problem In recent years, the use of artificial intelligence (AI) has become increasingly popular in dentistry because it facilitates the process of diagnosis and clinical decision-making. However, AI holds multiple prominent drawbacks, which restrict its wide application today. It is necessary for dentists to be aware of AI's pros and cons before its implementation. Purpose Therefore, the present study was conducted to comprehensively review various applications of AI in all dental branches along with its advantages and disadvantages. Materials and Method For this review article, a complete query was carried out on PubMed and Google Scholar databases and the studies published during 2010-2022 were collected using the keywords "Artificial Intelligence", "Dentistry," "Machine learning," "Deep learning," and "Diagnostic System." Ultimately, 116 relevant articles focused on artificial intelligence in dentistry were selected and evaluated. Results In new research AI applications in detecting dental abnormalities and oral malignancies based on radiographic view and histopathological features, designing dental implants and crowns, determining tooth preparation finishing line, analyzing growth patterns, estimating biological age, predicting the viability of dental pulp stem cells, analyzing the gene expression of periapical lesions, forensic dentistry, and predicting the success rate of treatments, have been mentioned. Despite AI's benefits in clinical dentistry, three controversial challenges including ease of use, financial return on investment, and evidence of performance exist and need to be managed. Conclusion As evidenced by the obtained results, the most crucial progression of AI is in oral malignancies' diagnostic systems. However, AI's newest advancements in various branches of dentistry require further scientific work before being applied to clinical practice. Moreover, the immense use of AI in clinical dentistry is only achievable when its challenges are appropriately managed.
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Affiliation(s)
- Kimia Ghods
- Student of Dentistry, Membership of Dental Material Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Arash Azizi
- Dept. Oral Medicine, Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Aryan Jafari
- Student of Dentistry, Membership of Dental Material Research Center, Tehran
| | - Kian Ghods
- Dept. of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Canada
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Iruvuri AG, Miryala G, Khan Y, Ramalingam NT, Sevugaperumal B, Soman M, Padmanabhan A. Revolutionizing Dental Imaging: A Comprehensive Study on the Integration of Artificial Intelligence in Dental and Maxillofacial Radiology. Cureus 2023; 15:e50292. [PMID: 38205468 PMCID: PMC10776831 DOI: 10.7759/cureus.50292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/08/2023] [Indexed: 01/12/2024] Open
Abstract
Recent advancements in deep learning and artificial intelligence (AI) have profoundly impacted various fields, including diagnostic imaging. Integrating AI technologies such as deep learning and convolutional neural networks has the potential to drastically improve diagnostic methods in the field of dentistry and maxillofacial radiography. A systematic study that adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards was carried out to examine the efficacy and uses of AI in dentistry and maxillofacial radiography. Incorporating cohort studies, case-control studies, and randomized clinical trials, the study used an interdisciplinary methodology. A thorough search spanning peer-reviewed research papers from 2009 to 2023 was done in databases including MEDLINE/PubMed and EMBASE. The inclusion criteria were original clinical research in English that employed AI models to recognize anatomical components in oral and maxillofacial pictures, identify anomalies, and diagnose disorders. The study looked at numerous research that used cutting-edge technology to show how accurate and dependable dental imaging is. Among the tasks covered by these investigations were age estimation, periapical lesion detection, segmentation of maxillary structures, assessment of dentofacial abnormalities, and segmentation of the mandibular canal. The study revealed important developments in the precise definition of anatomical structures and the identification of diseases. The use of AI technology in dental imaging marks a revolutionary development that will usher in a time of unmatched accuracy and effectiveness. These technologies have not only improved diagnostic accuracy and enabled early disease detection but have also streamlined intricate procedures, significantly enhancing patient outcomes. The symbiotic collaboration between human expertise and machine intelligence promises a future of more sophisticated and empathetic oral healthcare.
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Affiliation(s)
- Alekhya G Iruvuri
- General Dentistry, Malla Reddy Dental College for Women, Hyderabad, IND
| | - Gouthami Miryala
- General Dentistry, SVS Institute of Dental Sciences, Mahabubnagar, IND
| | - Yusuf Khan
- Orthodontics and Dentofacial Orthopaedics, Diamond Medical Specialists, Taif, SAU
| | | | - Bharath Sevugaperumal
- General Dentistry, Rajah Muthiah Dental College and Hospital, Annamalai University, Chidambaram, IND
| | - Mrunmayee Soman
- Dentistry, Dr. D. Y. Patil Dental College and Hospital, Pune, IND
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Leavitt L, Volovic J, Steinhauer L, Mason T, Eckert G, Dean JA, Dundar MM, Turkkahraman H. Can we predict orthodontic extraction patterns by using machine learning? Orthod Craniofac Res 2023; 26:552-559. [PMID: 36843547 DOI: 10.1111/ocr.12641] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 01/09/2023] [Accepted: 02/13/2023] [Indexed: 02/28/2023]
Abstract
OBJECTIVE To investigate the utility of machine learning (ML) in accurately predicting orthodontic extraction patterns in a heterogeneous population. MATERIALS AND METHODS The material of this retrospective study consisted of records of 366 patients treated with orthodontic extractions. The dataset was randomly split into training (70%) and test sets (30%) and was stratified according to race/ethnicity and gender. Fifty-five cephalometric and demographic input data were used to train and test multiple ML algorithms. The extraction patterns were labelled according to the previous treatment plan. Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) algorithms were used to predict the patient's extraction patterns. RESULTS The highest class accuracy percentages were obtained for the upper and lower 1st premolars (U/L4s) (RF: 81.63%, LR: 63.27%, SVM: 63.27%) and upper 1st premolars only (U4s) extraction patterns (RF: 61.11%, LR: 72.22%, SVM: 72.22%). However, all methods revealed low class accuracy rates (<50%) for the upper 1st and lower 2nd premolars (U4/L5s), upper 2nd and lower 1st premolars (U5/L4s), and upper and lower 2nd premolars (U/L5s) extraction patterns. For the overall accuracy, RF yielded the highest percentage with 54.55%, followed by SVM with 52.73% and LR with 49.09%. CONCLUSION All tested supervised ML techniques yielded good accuracy in predicting U/L4s and U4s extraction patterns. However, they predicted poorly for the U4/L5s, U5/L4s, and U/L5s extraction patterns. Molar relationship, mandibular crowding, and overjet were found to be the most predictive indicators for determining extraction patterns.
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Affiliation(s)
- Landon Leavitt
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, Indiana, USA
| | - James Volovic
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, Indiana, USA
| | - Lily Steinhauer
- Indiana University School of Dentistry, Indianapolis, Indiana, USA
| | - Taylor Mason
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, Indiana, USA
| | - George Eckert
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Jeffrey A Dean
- Department of Pediatric Dentistry, Indiana University School of Dentistry, Indianapolis, Indiana, USA
| | - M Murat Dundar
- School of Science, Department of Computer & Information Science, Indiana University Purdue University at Indianapolis, Indianapolis, Indiana, USA
| | - Hakan Turkkahraman
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, Indiana, USA
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Morita D, Kawarazaki A, Koimizu J, Tsujiko S, Soufi M, Otake Y, Sato Y, Numajiri T. Automatic orbital segmentation using deep learning-based 2D U-net and accuracy evaluation: A retrospective study. J Craniomaxillofac Surg 2023; 51:609-613. [PMID: 37813770 DOI: 10.1016/j.jcms.2023.09.003] [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: 03/05/2023] [Revised: 05/25/2023] [Accepted: 09/05/2023] [Indexed: 10/11/2023] Open
Abstract
The purpose of this study was to verify whether the accuracy of automatic segmentation (AS) of computed tomography (CT) images of fractured orbits using deep learning (DL) is sufficient for clinical application. In the surgery of orbital fractures, many methods have been reported to create a 3D anatomical model for use as a reference. However, because the orbit bone is thin and complex, creating a segmentation model for 3D printing is complicated and time-consuming. Here, the training of DL was performed using U-Net as the DL model, and the AS output was validated with Dice coefficients and average symmetry surface distance (ASSD). In addition, the AS output was 3D printed and evaluated for accuracy by four surgeons, each with over 15 years of clinical experience. One hundred twenty-five CT images were prepared, and manual orbital segmentation was performed in all cases. Ten orbital fracture cases were randomly selected as validation data, and the remaining 115 were set as training data. AS was successful in all cases, with good accuracy: Dice, 0.860 ± 0.033 (mean ± SD); ASSD, 0.713 ± 0.212 mm. In evaluating AS accuracy, the expert surgeons generally considered that it could be used for surgical support without further modification. The orbital AS algorithm developed using DL in this study is extremely accurate and can create 3D models rapidly at low cost, potentially enabling safer and more accurate surgeries.
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Affiliation(s)
- Daiki Morita
- Department of Plastic and Reconstructive Surgery, Kyoto Prefectural University of Medicine, Kyoto, Japan.
| | - Ayako Kawarazaki
- Department of Plastic and Reconstructive Surgery, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Jungen Koimizu
- Department of Plastic and Reconstructive Surgery, Omihachiman Community Medical Center, Shiga, Japan
| | - Shoko Tsujiko
- Department of Plastic and Reconstructive Surgery, Saiseikai Shigaken Hospital, Shiga, Japan
| | - Mazen Soufi
- Division of Information Science, Nara Institute of Science and Technology, Nara, Japan
| | - Yoshito Otake
- Division of Information Science, Nara Institute of Science and Technology, Nara, Japan
| | - Yoshinobu Sato
- Division of Information Science, Nara Institute of Science and Technology, Nara, Japan
| | - Toshiaki Numajiri
- Department of Plastic and Reconstructive Surgery, Kyoto Prefectural University of Medicine, Kyoto, Japan
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Ramachandran RA, Barão VAR, Ozevin D, Sukotjo C, Srinivasa PP, Mathew M. Early Predicting Tribocorrosion Rate of Dental Implant Titanium Materials Using Random Forest Machine Learning Models. TRIBOLOGY INTERNATIONAL 2023; 187:108735. [PMID: 37720691 PMCID: PMC10503681 DOI: 10.1016/j.triboint.2023.108735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Early detection and prediction of bio-tribocorrosion can avert unexpected damage that may lead to secondary revision surgery and associated risks of implantable devices. Therefore, this study sought to develop a state-of-the-art prediction technique leveraging machine learning(ML) models to classify and predict the possibility of mechanical degradation in dental implant materials. Key features considered in the study involving pure titanium and titanium-zirconium (zirconium = 5, 10, and 15 in wt%) alloys include corrosion potential, acoustic emission(AE) absolute energy, hardness, and weight-loss estimates. ML prototype models deployed confirms its suitability in tribocorrosion prediction with an accuracy above 90%. Proposed system can evolve as a continuous structural-health monitoring as well as a reliable predictive modeling technique for dental implant monitoring.
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Affiliation(s)
| | - Valentim A R Barão
- Department of Prosthodontics and Periodontology, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Didem Ozevin
- Department of Civil, Materials, and Environmental Engineering, University of Illinois at Chicago, IL, USA
| | - Cortino Sukotjo
- Department of Restorative Dentistry, College of Dentistry, University of Illinois at Chicago, IL, USA
| | - Pai P Srinivasa
- Department of Mechanical Engineering, NMAM IT, Nitte, Karnataka, India
| | - Mathew Mathew
- Department of Biomedical Engineering, University of Illinois at Chicago, IL, USA
- Department of Restorative Dentistry, College of Dentistry, University of Illinois at Chicago, IL, USA
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Mason T, Kelly KM, Eckert G, Dean JA, Dundar MM, Turkkahraman H. A machine learning model for orthodontic extraction/non-extraction decision in a racially and ethnically diverse patient population. Int Orthod 2023; 21:100759. [PMID: 37196482 DOI: 10.1016/j.ortho.2023.100759] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 04/27/2023] [Accepted: 04/30/2023] [Indexed: 05/19/2023]
Abstract
INTRODUCTION The purpose of the present study was to create a machine learning (ML) algorithm with the ability to predict the extraction/non-extraction decision in a racially and ethnically diverse sample. METHODS Data was gathered from the records of 393 patients (200 non-extraction and 193 extraction) from a racially and ethnically diverse population. Four ML models (logistic regression [LR], random forest [RF], support vector machine [SVM], and neural network [NN]) were trained on a training set (70% of samples) and then tested on the remaining samples (30%). The accuracy and precision of the ML model predictions were calculated using the area under the curve (AUC) of the receiver operating characteristics (ROC) curve. The proportion of correct extraction/non-extraction decisions was also calculated. RESULTS The LR, SVM, and NN models performed best, with an AUC of the ROC of 91.0%, 92.5%, and 92.3%, respectively. The overall proportion of correct decisions was 82%, 76%, 83%, and 81% for the LR, RF, SVM, and NN models, respectively. The features found to be most helpful to the ML algorithms in making their decisions were maxillary crowding/spacing, L1-NB (mm), U1-NA (mm), PFH:AFH, and SN-MP(̊), although many other features contributed significantly. CONCLUSIONS ML models can predict the extraction decision in a racially and ethnically diverse patient population with a high degree of accuracy and precision. Crowding, sagittal, and vertical characteristics all featured prominently in the hierarchy of components most influential to the ML decision-making process.
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Affiliation(s)
- Taylor Mason
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN, US
| | - Kynnedy M Kelly
- Indiana University School of Dentistry, Indianapolis, IN, US
| | - George Eckert
- Department of Biostatistics and Health Data Science, Indianapolis, Indiana University School of Medicine, IN, US
| | - Jeffrey A Dean
- Department of Pediatric Dentistry, Indiana University School of Dentistry, Indianapolis, IN, US
| | - M Murat Dundar
- Department of Computer & Information Science, Indiana University Purdue University at Indianapolis, School of Science, Indianapolis, IN, US
| | - Hakan Turkkahraman
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indianapolis, IN, US.
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Wood T, Anigbo JO, Eckert G, Stewart KT, Dundar MM, Turkkahraman H. Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study. Diagnostics (Basel) 2023; 13:diagnostics13091553. [PMID: 37174945 PMCID: PMC10178146 DOI: 10.3390/diagnostics13091553] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 04/18/2023] [Accepted: 04/25/2023] [Indexed: 05/15/2023] Open
Abstract
The aim was to predict the post-pubertal mandibular length and Y axis of growth in males by using various machine learning (ML) techniques. Cephalometric data obtained from 163 males with Class I Angle malocclusion, were used to train various ML algorithms. Analysis of variances (ANOVA) was used to compare the differences between predicted and actual measurements among methods and between time points. All the algorithms revealed an accuracy range from 95.80% to 97.64% while predicting post-pubertal mandibular length. When predicting the Y axis of growth, accuracies ranged from 96.60% to 98.34%. There was no significant interaction between methods and time points used for predicting the mandibular length (p = 0.235) and Y axis of growth (p = 0.549). All tested ML algorithms accurately predicted the post-pubertal mandibular length and Y axis of growth. The best predictors for the mandibular length were mandibular and maxillary lengths, and lower face height, while they were Y axis of growth, lower face height, and mandibular plane angle for the post-pubertal Y axis of growth. No significant difference was found among the accuracies of the techniques, except the least squares method had a significantly larger error than all others in predicting the Y axis of growth.
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Affiliation(s)
- Tyler Wood
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA
| | - Justina O Anigbo
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA
| | - George Eckert
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Kelton T Stewart
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA
| | - Mehmet Murat Dundar
- Department of Computer & Information Science, Indiana University Purdue University at Indianapolis School of Science, Indianapolis, IN 46202, USA
| | - Hakan Turkkahraman
- Department of Orthodontics and Oral Facial Genetics, Indiana University School of Dentistry, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, USA
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Murphy SJ, Lee S, Scharm JC, Kim S, Amin AA, Wu TH, Lu WE, Ni A, Ko CC, Fields HW, Deguchi T. Comparison of maxillary anterior tooth movement between Invisalign and fixed appliances. Am J Orthod Dentofacial Orthop 2023:S0889-5406(23)00032-X. [PMID: 36801092 DOI: 10.1016/j.ajodo.2022.10.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 10/01/2022] [Accepted: 10/01/2022] [Indexed: 02/17/2023]
Abstract
INTRODUCTION This research project aimed to compare the number of maxillary incisors and canine movement between Invisalign and fixed orthodontic appliances using artificial intelligence and identify any limitations of Invisalign. METHODS Sixty patients (Invisalign, n = 30; braces, n = 30) were randomly selected from the Ohio State University Graduate Orthodontic Clinic archive. Peer Assessment Rating (PAR) analysis was used to indicate the severity of the patients in both groups. To analyze the incisors and canine movement, specific landmarks were identified on incisors and canines using an artificial intelligence framework, two-stage mesh deep learning. Total average tooth movement in the maxilla and individual (incisors and canine) tooth movement in 6 directions (buccolingual, mesiodistal, vertical, tipping, torque, rotation) were then analyzed at a significance level of α = 0.05. RESULTS Based on the posttreatment Peer Assessment Rating scores, the quality of finished patients in both groups was similar. In maxillary incisors and canines, there was a significant difference in movement between Invisalign and conventional appliances for all 6 movement directions (P <0.05). The greatest differences were with rotation and tipping of the maxillary canine, along with incisor and canine torque. The smallest statistical differences observed for incisors and canines were crown translational tooth movement in the mesiodistal and buccolingual directions. CONCLUSIONS When comparing fixed orthodontic appliances to Invisalign, patients treated with fixed appliances were found to have significantly more maxillary tooth movement in all directions, especially with rotation and tipping of the maxillary canine.
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Affiliation(s)
- Shaun J Murphy
- Division of Orthodontics, College of Dentistry, Ohio State University, Columbus, Ohio
| | - Sanghee Lee
- Division of Orthodontics, College of Dentistry, Ohio State University, Columbus, Ohio
| | - Joshua C Scharm
- Division of Orthodontics, College of Dentistry, Ohio State University, Columbus, Ohio
| | - Stella Kim
- Division of Orthodontics, College of Dentistry, Ohio State University, Columbus, Ohio
| | - Aya A Amin
- Division of Orthodontics, College of Dentistry, Ohio State University, Columbus, Ohio
| | - Tai-Hsien Wu
- Division of Orthodontics, College of Dentistry, Ohio State University, Columbus, Ohio
| | - Wei-En Lu
- Division of Biostatistics, The Ohio State University College of Public health, Columbus, Ohio
| | - Ai Ni
- Division of Biostatistics, The Ohio State University College of Public health, Columbus, Ohio
| | - Ching-Chang Ko
- Department of Rehabilitative and Reconstructive Dentistry Division of Orthodontics and Prosthodontics, University of Louisville, School of Dentistry, Louisville, Ky
| | - Henry W Fields
- Department of Rehabilitative and Reconstructive Dentistry Division of Orthodontics and Prosthodontics, University of Louisville, School of Dentistry, Louisville, Ky
| | - Toru Deguchi
- Department of Rehabilitative and Reconstructive Dentistry Division of Orthodontics and Prosthodontics, University of Louisville, School of Dentistry, Louisville, Ky.
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Arsiwala-Scheppach LT, Chaurasia A, Müller A, Krois J, Schwendicke F. Machine Learning in Dentistry: A Scoping Review. J Clin Med 2023; 12:937. [PMID: 36769585 PMCID: PMC9918184 DOI: 10.3390/jcm12030937] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/06/2023] [Accepted: 01/23/2023] [Indexed: 01/27/2023] Open
Abstract
Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies.
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Affiliation(s)
- Lubaina T. Arsiwala-Scheppach
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 14197 Berlin, Germany
- ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, CH-1211 Geneva 20, Switzerland
| | - Akhilanand Chaurasia
- ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, CH-1211 Geneva 20, Switzerland
- Department of Oral Medicine and Radiology, King George’s Medical University, Lucknow 226003, India
| | - Anne Müller
- Pharmacovigilance Institute (Pharmakovigilanz- und Beratungszentrum, PVZ) for Embryotoxicology, Institute of Clinical Pharmacology and Toxicology, Charité—Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 14197 Berlin, Germany
- ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, CH-1211 Geneva 20, Switzerland
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 14197 Berlin, Germany
- ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, CH-1211 Geneva 20, Switzerland
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Zhang X, Qin N, Zhou Z, Chen S. Machine learning in 3D auto-filling alveolar cleft of CT images to assess the influence of alveolar bone grafting on the development of maxilla. BMC Oral Health 2023; 23:16. [PMID: 36631872 PMCID: PMC9835292 DOI: 10.1186/s12903-023-02706-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 01/02/2023] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Machine learning based auto-segmentation of 3D images has been developed rapidly in recent years. However, the application of this new method in the research of patients with unilateral cleft lip and palate (UCLP) is very limited. In this study, a machine learning algorithm utilizing 3D U-net was used to automatically segment the maxilla, fill the cleft and evaluate the alveolar bone graft in UCLP patients. Cleft related factors and the surgery impact on the development of maxilla were analyzed. METHODS Preoperative and postoperative computed tomography images of 32 patients (64 images) were obtained. The deep-learning-based protocol was used to segment the maxilla and defect, followed by manual refinement. Paired t-tests and Mann-Whitney tests were performed to reveal the changes of the maxilla after surgery. Two-factor, two-level analysis for repeated measurement was used to examine the different trends of growth on the cleft and non-cleft sides of the maxilla. Pearson and Spearman correlations were used to explore the relationship between the defect and the changes of the maxillary cleft side. RESULTS One-year after the alveolar bone grafting surgery, different growth amount was found on the cleft and non-cleft sides of maxilla. The maxillary length (from 34.64 ± 2.48 to 35.67 ± 2.45 mm) and the alveolar length (from 36.58 ± 3.21 to 37.63 ± 2.94 mm) increased significantly only on the cleft side while the maxillary anterior width (from 11.61 ± 1.61 to 12.01 ± 1.41 mm) and posterior width (from 29.63 ± 2.25 to 30.74 ± 2.63 mm) increased significantly only on the non-cleft side after surgery. Morphology of the cleft was found to be related to the pre-surgical maxillary dimension on the cleft side, while its correlation with the change of the maxilla after surgery was low or not statistically significant. CONCLUSION The auto-segmentation of the maxilla and the cleft could be performed very efficiently and accurately with the machine learning method. Asymmetric growth was found on the cleft and non-cleft sides of the maxilla after alveolar bone graft in UCLP patients. The morphology of the cleft mainly contributed to the pre-operation variance of the maxilla but had little impact on the maxilla growth after surgery.
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Affiliation(s)
- Xin Zhang
- grid.11135.370000 0001 2256 9319Department of Orthodontics, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, People’s Republic of China
| | - Niu Qin
- grid.11135.370000 0001 2256 9319Department of Orthodontics, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, People’s Republic of China
| | - Zhibo Zhou
- grid.11135.370000 0001 2256 9319Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, People’s Republic of China
| | - Si Chen
- grid.11135.370000 0001 2256 9319Department of Orthodontics, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing, People’s Republic of China
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Saade M, Arai K, Motro M, Saade A, Will LA. Maxillary dimensions and arch shape with palatally displaced canines. Eur J Orthod 2023; 45:338-345. [PMID: 36617278 DOI: 10.1093/ejo/cjac073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
BACKGROUND/OBJECTIVES The aim of this study was to explore the effect of palatally displaced canines on maxillary dimensions and arch shape. METHODS Occlusal and skeletal landmarks were plotted on cone beam computed tomography (CBCT) images of 156 orthodontic patients: 78 with palatally displaced canines (PDC) (27 left, 28 right, and 23 bilateral) and 78 controls using Mimics™ (Materialise, Belgium). Arch forms were created, and arch width, depth, and skeletal dimensions were measured. T-test (P < 0.05) was used to compare the groups. Geometric Morphometrics (GM) analysis was used to compare the groups and to explore the pattern of covariation between two sets of landmarks. RESULTS The unilateral PDC group showed an increased first premolar, second premolar, molar widths, and an increased molar depth. No significant difference was found in arch form. The skeletal measurements were generally larger in the PDC group, but the transverse measurements were the most significant. Discriminant Function Analysis (DFA) with cross-validation allowed an accurate classification of 85.9% of the control group and 66.7% of the PDC group. The Mahalanobis distance displayed significant differences among three combinations of groups. Strong covariance was found between the second premolar and all other landmarks. LIMITATIONS The small sample sizes of the stratified groups precluded recognition of statistical significance. CONCLUSIONS Patients with unilateral PDC displayed significantly wider first, second premolar, and first molar widths, and increased first molar depths. There were no significant differences in arch form; however, GM showed significant differences between the groups and a prominent covariance function for the 2nd premolars.
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Affiliation(s)
- Marianne Saade
- Department of Orthodontics, Boston University, 100 East Newton St., Room 754, MA 02118, USA
| | - Kazuhito Arai
- Department of Orthodontics, Nippon Dental University, 1-9-20 Fujimi, Chiyoda-ku, Tokyo 102-8159, Japan
| | - Melih Motro
- Department of Orthodontics, Boston University, 100 East Newton St., Room 754, MA 02118, USA
| | - Antoine Saade
- Department of Orthodontics, Lebanese University, P.O. Box 6573, Beirut, Lebanon
| | - Leslie A Will
- Department of Orthodontics, Boston University, 100 East Newton St., Room 757, MA 02118, USA
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Revilla-León M, Gómez-Polo M, Vyas S, Barmak AB, Özcan M, Att W, Krishnamurthy VR. Artificial intelligence applications in restorative dentistry: A systematic review. J Prosthet Dent 2022; 128:867-875. [PMID: 33840515 DOI: 10.1016/j.prosdent.2021.02.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 02/03/2021] [Accepted: 02/04/2021] [Indexed: 11/17/2022]
Abstract
STATEMENT OF PROBLEM Artificial intelligence (AI) applications are increasing in restorative procedures. However, the current development and performance of AI in restorative dentistry applications has not yet been systematically documented and analyzed. PURPOSE The purpose of this systematic review was to identify and evaluate the ability of AI models in restorative dentistry to diagnose dental caries and vertical tooth fracture, detect tooth preparation margins, and predict restoration failure. MATERIAL AND METHODS An electronic systematic review was performed in 5 databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. A manual search was also conducted. Studies with AI models were selected based on 4 criteria: diagnosis of dental caries, diagnosis of vertical tooth fracture, detection of the tooth preparation finishing line, and prediction of restoration failure. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus. RESULTS A total of 34 articles were included in the review: 29 studies included AI techniques for the diagnosis of dental caries or the elaboration of caries and postsensitivity prediction models, 2 for the diagnosis of vertical tooth fracture, 1 for the tooth preparation finishing line location, and 2 for the prediction of the restoration failure. Among the studies reviewed, the AI models tested obtained a caries diagnosis accuracy ranging from 76% to 88.3%, sensitivity ranging from 73% to 90%, and specificity ranging from 61.5% to 93%. The caries prediction accuracy among the studies ranged from 83.6% to 97.1%. The studies reported an accuracy for the vertical tooth fracture diagnosis ranging from 88.3% to 95.7%. The article using AI models to locate the finishing line reported an accuracy ranging from 90.6% to 97.4%. CONCLUSIONS AI models have the potential to provide a powerful tool for assisting in the diagnosis of caries and vertical tooth fracture, detecting the tooth preparation margin, and predicting restoration failure. However, the dental applications of AI models are still in development. Further studies are required to assess the clinical performance of AI models in restorative dentistry.
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Affiliation(s)
- Marta Revilla-León
- Assistant Professor and Assistant Program Director AEGD Residency, Department of Comprehensive Dentistry, College of Dentistry, Texas A&M University, Dallas, Texas; Affiliate Faculty Graduate Prosthodontics, Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Wash; Researcher at Revilla Research Center, Madrid, Spain
| | - Miguel Gómez-Polo
- Associate Professor, Department of Conservative Dentistry and Prosthodontics, School of Dentistry, Complutense University of Madrid, Madrid, Spain.
| | - Shantanu Vyas
- Graduate Research Assistant, J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, Dallas, Texas
| | - Abdul Basir Barmak
- Assistant Professor Clinical Research and Biostatistics, Eastman Institute of Oral Health, University of Rochester Medical Center, Rochester, NY
| | - Mutlu Özcan
- Professor and Head, Division of Dental Biomaterials, Clinic for Reconstructive Dentistry, Center for Dental and Oral Medicine, University of Zürich, Zürich, Switzerland
| | - Wael Att
- Professor and Chair, Department of Prosthodontics, Tufts University School of Dental Medicine, Boston, Mass
| | - Vinayak R Krishnamurthy
- Assistant Professor, J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, College Station, Texas
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Fatima A, Shafi I, Afzal H, Díez IDLT, Lourdes DRSM, Breñosa J, Espinosa JCM, Ashraf I. Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives. Healthcare (Basel) 2022; 10:2188. [PMID: 36360529 PMCID: PMC9690084 DOI: 10.3390/healthcare10112188] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 10/11/2022] [Accepted: 10/26/2022] [Indexed: 08/31/2023] Open
Abstract
Artificial intelligence has been widely used in the field of dentistry in recent years. The present study highlights current advances and limitations in integrating artificial intelligence, machine learning, and deep learning in subfields of dentistry including periodontology, endodontics, orthodontics, restorative dentistry, and oral pathology. This article aims to provide a systematic review of current clinical applications of artificial intelligence within different fields of dentistry. The preferred reporting items for systematic reviews (PRISMA) statement was used as a formal guideline for data collection. Data was obtained from research studies for 2009-2022. The analysis included a total of 55 papers from Google Scholar, IEEE, PubMed, and Scopus databases. Results show that artificial intelligence has the potential to improve dental care, disease diagnosis and prognosis, treatment planning, and risk assessment. Finally, this study highlights the limitations of the analyzed studies and provides future directions to improve dental care.
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Affiliation(s)
- Anum Fatima
- National Centre for Robotics, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Imran Shafi
- College of Electrical and Mechanical Engineering, 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
| | - Del Rio-Solá M. Lourdes
- Department of Vascular Surgery, University Hospital of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Jose Breñosa
- Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
- Universidade Internacional do Cuanza, Estrada Nacional 250, Bairro Kaluapanda Cuito- Bié, Angola
| | - Julio César Martínez Espinosa
- Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Fundación Universitaria Internacional de Colombia, Calle 39A #19-18 Bogotá D.C, Colombia
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
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Gillot M, Baquero B, Le C, Deleat-Besson R, Bianchi J, Ruellas A, Gurgel M, Yatabe M, Al Turkestani N, Najarian K, Soroushmehr R, Pieper S, Kikinis R, Paniagua B, Gryak J, Ioshida M, Massaro C, Gomes L, Oh H, Evangelista K, Chaves Junior CM, Garib D, Costa F, Benavides E, Soki F, Fillion-Robin JC, Joshi H, Cevidanes L, Prieto JC. Automatic multi-anatomical skull structure segmentation of cone-beam computed tomography scans using 3D UNETR. PLoS One 2022; 17:e0275033. [PMID: 36223330 PMCID: PMC9555672 DOI: 10.1371/journal.pone.0275033] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/09/2022] [Indexed: 11/06/2022] Open
Abstract
The segmentation of medical and dental images is a fundamental step in automated clinical decision support systems. It supports the entire clinical workflow from diagnosis, therapy planning, intervention, and follow-up. In this paper, we propose a novel tool to accurately process a full-face segmentation in about 5 minutes that would otherwise require an average of 7h of manual work by experienced clinicians. This work focuses on the integration of the state-of-the-art UNEt TRansformers (UNETR) of the Medical Open Network for Artificial Intelligence (MONAI) framework. We trained and tested our models using 618 de-identified Cone-Beam Computed Tomography (CBCT) volumetric images of the head acquired with several parameters from different centers for a generalized clinical application. Our results on a 5-fold cross-validation showed high accuracy and robustness with a Dice score up to 0.962±0.02. Our code is available on our public GitHub repository.
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Affiliation(s)
- Maxime Gillot
- University of Michigan, Ann Arbor, Michigan, United States of America
- CPE Lyon, Lyon, France
- * E-mail:
| | - Baptiste Baquero
- University of Michigan, Ann Arbor, Michigan, United States of America
- CPE Lyon, Lyon, France
| | - Celia Le
- University of Michigan, Ann Arbor, Michigan, United States of America
- CPE Lyon, Lyon, France
| | - Romain Deleat-Besson
- University of Michigan, Ann Arbor, Michigan, United States of America
- CPE Lyon, Lyon, France
| | - Jonas Bianchi
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Antonio Ruellas
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Marcela Gurgel
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Marilia Yatabe
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Najla Al Turkestani
- University of Michigan, Ann Arbor, Michigan, United States of America
- King Abdulaziz University, Jeddah, Saudi Arabia
| | - Kayvan Najarian
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Reza Soroushmehr
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Steve Pieper
- ISOMICS, Cambridge, Massachusetts, United States of America
| | - Ron Kikinis
- Harvard Medical School, Boston, Massachusetts, United States of America
| | | | - Jonathan Gryak
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Marcos Ioshida
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Camila Massaro
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Liliane Gomes
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Heesoo Oh
- University of Pacific, Stockton, California, United States of America
| | | | | | | | - Fábio Costa
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Erika Benavides
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Fabiana Soki
- University of Michigan, Ann Arbor, Michigan, United States of America
| | | | - Hina Joshi
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Lucia Cevidanes
- University of Michigan, Ann Arbor, Michigan, United States of America
| | - Juan Carlos Prieto
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
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Cho HN, Gwon E, Kim KA, Baek SH, Kim N, Kim SJ. Accuracy of convolutional neural networks-based automatic segmentation of pharyngeal airway sections according to craniofacial skeletal pattern. Am J Orthod Dentofacial Orthop 2022; 162:e53-e62. [PMID: 35654686 DOI: 10.1016/j.ajodo.2022.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 01/01/2022] [Accepted: 01/01/2022] [Indexed: 11/28/2022]
Abstract
INTRODUCTION This study aimed to evaluate a 3-dimensional (3D) U-Net-based convolutional neural networks model for the fully automatic segmentation of regional pharyngeal volume of interests (VOIs) in cone-beam computed tomography scans to compare the accuracy of the model performance across different skeletal patterns presenting with various pharyngeal dimensions. METHODS Two-hundred sixteen cone-beam computed tomography scans of adult patients were randomly divided into training (n = 100), validation (n = 16), and test (n = 100) datasets. We trained the 3D U-Net model for fully automatic segmentation of pharyngeal VOIs and their measurements: nasopharyngeal, velopharyngeal, glossopharyngeal, and hypopharyngeal sections as well as total pharyngeal airway space (PAS). The test datasets were subdivided according to the sagittal and vertical skeletal patterns. The segmentation performance was assessed by dice similarity coefficient, volumetric similarity, precision, and recall values, compared with the ground truth created by 1 expert's manual processing using semiautomatic software. RESULTS The proposed model achieved highly accurate performance, showing a mean dice similarity coefficient of 0.928 ± 0.023, the volumetric similarity of 0.928 ± 0.023, precision of 0.925 ± 0.030, and recall of 0.921 ± 0.029 for total PAS segmentation. The performance showed region-specific differences, revealing lower accuracy in the glossopharyngeal and hypopharyngeal sections than in the upper sections (P <0.001). However, the accuracy of model performance at each pharyngeal VOI showed no significant difference according to sagittal or vertical skeletal patterns. CONCLUSIONS The 3D-convolutional neural network performance for region-specific PAS analysis is promising to substitute for laborious and time-consuming manual analysis in every skeletal and pharyngeal pattern.
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Affiliation(s)
- Ha-Nul Cho
- Department of Dentistry, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Eunseo Gwon
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Kyung-A Kim
- Department of Dentistry, Graduate School, Kyung Hee University, Seoul, South Korea
| | - Seung-Hak Baek
- Department of Orthodontics, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea; Department of Radiology, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
| | - Su-Jung Kim
- Department of Dentistry, Graduate School, Kyung Hee University, Seoul, South Korea.
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Skeletal and Dental Morphological Characteristics of the Maxillary in Patients with Impacted Canines Using Cone Beam Computed Tomography: A Retrospective Clinical Study. J Pers Med 2022; 12:jpm12010096. [PMID: 35055411 PMCID: PMC8778207 DOI: 10.3390/jpm12010096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 12/29/2021] [Accepted: 01/10/2022] [Indexed: 12/10/2022] Open
Abstract
The aim of this study is to analyze the skeletal and dentoalveolar morphological characteristics of the maxillary in subjects with a unilateral palatally impacted canine using Cone Beam Computed Tomography (CBCT). A retrospective clinical study was conducted of 100 adult patients divided into two groups: one consisting of patients with a unilaterally palatally impacted maxillary canine (GI), with the subgroups in the right and left hemiarches (GI-R and GI-L), and the second, without impacted canine, as the control group (CG). The CBCT measured skeletal variables (maxillary basal width and alveolar crest height) and dentoalveolar variables (inclination of the upper incisor, tooth lengths of incisors and canines, arch length, tooth size and bone dental discrepancy). In skeletal variables, statistically significant differences were found in alveolar crest height (ACH) in all groups and subgroups (p < 0.01). In the dentoalveolar variables, there were differences in the angle of the upper incisor (II) and lateral incisor length (LLIL) between the GI and GC and the angle of the upper incisor (II′), arch length (AL′) and arch length-tooth size discrepancy (ATD′) among the GI subgroups (p < 0.01). There are skeletal and dentoalveolar differences in patients with unilateral palatally impacted maxillary canines, with lower angular and linear measurements compared with patients without impaction.
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Badr FF, Jadu FM. Performance of artificial intelligence using oral and maxillofacial CBCT images: A systematic review and meta-analysis. Niger J Clin Pract 2022; 25:1918-1927. [DOI: 10.4103/njcp.njcp_394_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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23
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Kim MG, Moon JH, Hwang HW, Cho SJ, Donatelli RE, Lee SJ. Evaluation of an automated superimposition method based on multiple landmarks for growing patients. Angle Orthod 2021; 92:226-232. [PMID: 34605860 DOI: 10.2319/010121-1.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Accepted: 07/01/2021] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVES To determine if an automated superimposition method using six landmarks (Sella, Nasion, Porion, Orbitale, Basion, and Pterygoid) would be more suitable than the traditional Sella-Nasion (SN) method to evaluate growth changes. MATERIALS AND METHODS Serial lateral cephalograms at an average interval of 2.7 years were taken on 268 growing children who had not undergone orthodontic treatment. The T1 and T2 lateral images were manually traced. Three different superimposition methods: Björk's structural method, conventional SN, and the multiple landmark (ML) superimposition methods were applied. Bjork's structural method was used as the gold standard. Comparisons among the superimposition methods were carried out by measuring the linear distances between Anterior Nasal Spine, point A, point B, and Pogonion using each superimposition method. Multiple linear regression analysis was performed to identify factors that could affect the accuracy of the superimpositions. RESULTS The ML superimposition method demonstrated smaller differences from Björk's method than the conventional SN method did. Greater differences among the cephalometric landmarks tested resulted when: the designated point was farther from the cranial base, the T1 age was older, and the more time elapsed between T1 and T2. CONCLUSIONS From the results of this study in growing patients, the ML superimposition method seems to be more similar to Björk's structural method than the SN superimposition method. A major advantage of the ML method is likely to be that it can be applied automatically and may be just as reliable as manual superimposition methods.
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Lo Giudice A, Ronsivalle V, Spampinato C, Leonardi R. Fully automatic segmentation of the mandible based on convolutional neural networks (CNNs). Orthod Craniofac Res 2021; 24 Suppl 2:100-107. [PMID: 34553817 DOI: 10.1111/ocr.12536] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 05/15/2021] [Accepted: 06/10/2021] [Indexed: 12/31/2022]
Abstract
OBJECTIVES To evaluate the accuracy of automatic deep learning-based method for fully automatic segmentation of the mandible from CBCTs. SETTING AND SAMPLE POPULATION CBCT-derived mandible fully automatic segmentation. METHODS Forty CBCT scans from healthy patients (20 females and 20 males, mean age 23.37 ± 3.34) were collected, and a manual mandible segmentation was carried out by using Mimics software. Twenty CBCT scans were randomly selected and used for training the artificial intelligence model file. The remaining 20 CBCT segmentation masks were used to test the accuracy of the CNN automatic method by comparing the segmentation volumes of the 3D models obtained with automatic and manual segmentations. The accuracy of the CNN-based method was also assessed by using the DICE Score coefficient (DSC) and by the surface-to-surface matching technique. The intraclass correlation coefficient (ICC) and Dahlberg's formula were used respectively to test the intra-observer reliability and method error. Independent Student's t test was used for between-groups volumetric comparison. RESULTS Measurements were highly correlated with an ICC value of 0.937, while the method error was 0.24 mm3 . A difference of 0.71 (±0.49) cm3 was found between the methodologies, but it was not statistically significant (P > .05). The matching percentage detected was 90.35% (±1.88) (tolerance 0.5 mm) and 96.32% ± 1.97% (tolerance 1.0 mm). The differences, measured as DSC in percentage, between the assessments done with both methods were, respectively, 2.8% and 3.1%. CONCLUSION The tested deep learning CNN-based technology is accurate and performs as well as an experienced image reader but at much higher speed, which is of significant clinical relevance.
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Affiliation(s)
- Antonino Lo Giudice
- Department of Orthodontics, School of Dentistry, University of Catania, Catania, Italy
| | - Vincenzo Ronsivalle
- Department of Orthodontics, School of Dentistry, University of Catania, Catania, Italy
| | - Concetto Spampinato
- Department of Computer and Telecommunications Engineering, University of Catania, Catania, Italy
| | - Rosalia Leonardi
- Department of Orthodontics, School of Dentistry, University of Catania, Catania, Italy
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Reyes LT, Knorst JK, Ortiz FR, Ardenghi TM. Scope and challenges of machine learning-based diagnosis and prognosis in clinical dentistry: A literature review. J Clin Transl Res 2021; 7:523-539. [PMID: 34541366 PMCID: PMC8445629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 04/17/2021] [Accepted: 05/24/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Machine learning (ML) has emerged as a branch of artificial intelligence dealing with the analysis of large amounts of data. The applications of ML algorithms have also expanded to health care, including dentistry. Recent advances in this field point to future improvements in diagnostic techniques and the prognosis of various diseases of the teeth and other maxillofacial structures. AIM The aim of this literature review is to describe the basis for ML being applied to different dental sub-fields in recent years, to identify typical algorithms used in the studies, and to summarize the scope and challenges of using these techniques in dental clinical practice. RELEVANCE FOR PATIENTS The proficiency of emerging technologies that have begun to show encouraging results in the diagnosis and prognosis of oral diseases can improve the precision in the selection of treatment for patients. It is necessary to understand the challenges associated with using these tools to effectively use them in dental services and ensure a higher quality of care for patients.
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Affiliation(s)
- Lilian Toledo Reyes
- Department of Stomatology, School of Dentistry, Federal University of Santa Maria, Santa Maria, Brazil
| | - Jessica Klöckner Knorst
- Department of Stomatology, School of Dentistry, Federal University of Santa Maria, Santa Maria, Brazil
| | - Fernanda Ruffo Ortiz
- Department of Stomatology, School of Dentistry, Federal University of Santa Maria, Santa Maria, Brazil
| | - Thiago Machado Ardenghi
- Department of Stomatology, School of Dentistry, Federal University of Santa Maria, Santa Maria, Brazil,
Corresponding author Thiago Machado Ardenghi Departamento de Estomatologia, Faculdade de Odontologia da Universidade Federal de Santa Maria, Av. Roraima, 1000, Cidade Universitária - 26F, 97015-372, Santa Maria, RS, Brazil. Fax: +55.55-3220-9272 E-mail:
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Monill-González A, Rovira-Calatayud L, d'Oliveira NG, Ustrell-Torrent JM. Artificial intelligence in orthodontics: Where are we now? A scoping review. Orthod Craniofac Res 2021; 24 Suppl 2:6-15. [PMID: 34270881 DOI: 10.1111/ocr.12517] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Accepted: 06/29/2021] [Indexed: 12/15/2022]
Abstract
OBJECTIVE This scoping review aims to determine the applications of Artificial Intelligence (AI) that are extensively employed in the field of Orthodontics, to evaluate its benefits, and to discuss its potential implications in this speciality. Recent decades have witnessed enormous changes in our profession. The arrival of new and more aesthetic options in orthodontic treatment, the transition to a fully digital workflow, the emergence of temporary anchorage devices and new imaging methods all provide both patients and professionals with a new focus in orthodontic care. MATERIALS AND METHODS This review was performed following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. The electronic literature search was performed through MEDLINE/PubMed, Scopus, Web of Science, Cochrane and IEEE Xplore databases with a 11-year time restriction: January 2010 till March 2021. No additional manual searches were performed. RESULTS The electronic literature search initially returned 311 records, and 115 after removing duplicate references. Finally, the application of the inclusion criteria resulted in 17 eligible publications in the qualitative synthesis review. CONCLUSION The analysed studies demonstrated that Convolution Neural Networks can be used for the automatic detection of anatomical reference points on radiological images. In the growth and development research area, the Cervical Vertebral Maturation stage can be determined using an Artificial Neural Network model and obtain the same results as expert human observers. AI technology can also improve the diagnostic accuracy for orthodontic treatments, thereby helping the orthodontist work more accurately and efficiently.
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Affiliation(s)
- Anna Monill-González
- Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | | | - Nuno Gustavo d'Oliveira
- Department of Odontostomatology - Orthodontics. Coordinator of the Master of Orthodontics, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Josep M Ustrell-Torrent
- Department of Odontostomatology - Orthodontics, Oral Health and Masticatory System Group (IDIBELL), University of Barcelona, Barcelona, Spain
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Bichu YM, Hansa I, Bichu AY, Premjani P, Flores-Mir C, Vaid NR. Applications of artificial intelligence and machine learning in orthodontics: a scoping review. Prog Orthod 2021; 22:18. [PMID: 34219198 PMCID: PMC8255249 DOI: 10.1186/s40510-021-00361-9] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/12/2021] [Indexed: 12/15/2022] Open
Abstract
Introduction This scoping review aims to provide an overview of the existing evidence on the use of artificial intelligence (AI) and machine learning (ML) in orthodontics, its translation into clinical practice, and what limitations do exist that have precluded their envisioned application. Methods A scoping review of the literature was carried out following the PRISMA-ScR guidelines. PubMed was searched until July 2020. Results Sixty-two articles fulfilled the inclusion criteria. A total of 43 out of the 62 studies (69.35%) were published this last decade. The majority of these studies were from the USA (11), followed by South Korea (9) and China (7). The number of studies published in non-orthodontic journals (36) was more extensive than in orthodontic journals (26). Artificial Neural Networks (ANNs) were found to be the most commonly utilized AI/ML algorithm (13 studies), followed by Convolutional Neural Networks (CNNs), Support Vector Machine (SVM) (9 studies each), and regression (8 studies). The most commonly studied domains were diagnosis and treatment planning—either broad-based or specific (33), automated anatomic landmark detection and/or analyses (19), assessment of growth and development (4), and evaluation of treatment outcomes (2). The different characteristics and distribution of these studies have been displayed and elucidated upon therein. Conclusion This scoping review suggests that there has been an exponential increase in the number of studies involving various orthodontic applications of AI and ML. The most commonly studied domains were diagnosis and treatment planning, automated anatomic landmark detection and/or analyses, and growth and development assessment. Supplementary Information The online version contains supplementary material available at 10.1186/s40510-021-00361-9.
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Affiliation(s)
| | | | | | | | - Carlos Flores-Mir
- Department of Orthodontics, University of Alberta, Edmonton, Alberta, Canada
| | - Nikhilesh R Vaid
- Department of Orthodontics, European University College, Dubai, United Arab Emirates
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Mohammad-Rahimi H, Nadimi M, Rohban MH, Shamsoddin E, Lee VY, Motamedian SR. Machine learning and orthodontics, current trends and the future opportunities: A scoping review. Am J Orthod Dentofacial Orthop 2021; 160:170-192.e4. [PMID: 34103190 DOI: 10.1016/j.ajodo.2021.02.013] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 01/01/2021] [Accepted: 02/01/2021] [Indexed: 12/29/2022]
Abstract
INTRODUCTION In recent years, artificial intelligence (AI) has been applied in various ways in medicine and dentistry. Advancements in AI technology show promising results in the practice of orthodontics. This scoping review aimed to investigate the effectiveness of AI-based models employed in orthodontic landmark detection, diagnosis, and treatment planning. METHODS A precise search of electronic databases was conducted, including PubMed, Google Scholar, Scopus, and Embase (English publications from January 2010 to July 2020). Quality Assessment and Diagnostic Accuracy Tool 2 (QUADAS-2) was used to assess the quality of the articles included in this review. RESULTS After applying inclusion and exclusion criteria, 49 articles were included in the final review. AI technology has achieved state-of-the-art results in various orthodontic applications, including automated landmark detection on lateral cephalograms and photography images, cervical vertebra maturation degree determination, skeletal classification, orthodontic tooth extraction decisions, predicting the need for orthodontic treatment or orthognathic surgery, and facial attractiveness. Most of the AI models used in these applications are based on artificial neural networks. CONCLUSIONS AI can help orthodontists save time and provide accuracy comparable to the trained dentists in diagnostic assessments and prognostic predictions. These systems aim to boost performance and enhance the quality of care in orthodontics. However, based on current studies, the most promising application was cephalometry landmark detection, skeletal classification, and decision making on tooth extractions.
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Affiliation(s)
| | - Mohadeseh Nadimi
- Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Erfan Shamsoddin
- National Institute for Medical Research Development, Tehran, Iran
| | | | - Saeed Reza Motamedian
- Department of Orthodontics, School of Dentistry, & Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Etemad L, Wu TH, Heiner P, Liu J, Lee S, Chao WL, Zaytoun ML, Guez C, Lin FC, Jackson CB, Ko CC. Machine learning from clinical data sets of a contemporary decision for orthodontic tooth extraction. Orthod Craniofac Res 2021; 24 Suppl 2:193-200. [PMID: 34031981 DOI: 10.1111/ocr.12502] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 04/29/2021] [Accepted: 05/14/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To examine the robustness of the published machine learning models in the prediction of extraction vs non-extraction for a diverse US sample population seen by multiple providers. SETTING AND SAMPLE POPULATION Diverse group of 838 patients (208 extraction, 630 non-extraction) were consecutively enrolled. MATERIALS AND METHODS Two sets of input features (117 and 22) including clinical and cephalometric variables were identified based on previous studies. Random forest (RF) and multilayer perception (MLP) models were trained using these feature sets on the sample population and evaluated using measures including accuracy (ACC) and balanced accuracy (BA). A technique to identify incongruent data was used to explore underlying characteristics of the data set and split all samples into 2 groups (G1 and G2) for further model training. RESULTS Performance of the models (75%-79% ACC and 72%-76% BA) on the total sample population was lower than in previous research. Models were retrained and evaluated using G1 and G2 separately, and individual group MLP models yielded improved accuracy for G1 (96% ACC and 94% BA) and G2 (88% ACC and 85% BA). RF feature ranking showed differences between top features for G1 (maxillary crowding, mandibular crowding and L1-NB) and G2 (age, mandibular crowding and lower lip to E-plane). CONCLUSIONS An incongruent data pattern exists in a consecutively enrolled patient population. Future work with incongruent data segregation and advanced artificial intelligence algorithms is needed to improve the generalization ability to make it ready to support clinical decision-making.
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Affiliation(s)
- Lily Etemad
- Division of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA
| | - Tai-Hsien Wu
- Division of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA
| | - Parker Heiner
- College of Dentistry, The Ohio State University, Columbus, OH, USA
| | - Jie Liu
- Division of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA
| | - Sanghee Lee
- Division of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA
| | - Wei-Lun Chao
- Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, OH, USA
| | | | | | - Feng-Chang Lin
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Ching-Chang Ko
- Division of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA
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Hwang HW, Moon JH, Kim MG, Donatelli RE, Lee SJ. Evaluation of automated cephalometric analysis based on the latest deep learning method. Angle Orthod 2021; 91:329-335. [PMID: 33434275 DOI: 10.2319/021220-100.1] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 11/01/2020] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVES To compare an automated cephalometric analysis based on the latest deep learning method of automatically identifying cephalometric landmarks (AI) with previously published AI according to the test style of the worldwide AI challenges at the International Symposium on Biomedical Imaging conferences held by the Institute of Electrical and Electronics Engineers (IEEE ISBI). MATERIALS AND METHODS This latest AI was developed by using a total of 1983 cephalograms as training data. In the training procedures, a modification of a contemporary deep learning method, YOLO version 3 algorithm, was applied. Test data consisted of 200 cephalograms. To follow the same test style of the AI challenges at IEEE ISBI, a human examiner manually identified the IEEE ISBI-designated 19 cephalometric landmarks, both in training and test data sets, which were used as references for comparison. Then, the latest AI and another human examiner independently detected the same landmarks in the test data set. The test results were compared by the measures that appeared at IEEE ISBI: the success detection rate (SDR) and the success classification rates (SCR). RESULTS SDR of the latest AI in the 2-mm range was 75.5% and SCR was 81.5%. These were greater than any other previous AIs. Compared to the human examiners, AI showed a superior success classification rate in some cephalometric analysis measures. CONCLUSIONS This latest AI seems to have superior performance compared to previous AI methods. It also seems to demonstrate cephalometric analysis comparable to human examiners.
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Wang H, Minnema J, Batenburg KJ, Forouzanfar T, Hu FJ, Wu G. Multiclass CBCT Image Segmentation for Orthodontics with Deep Learning. J Dent Res 2021; 100:943-949. [PMID: 33783247 PMCID: PMC8293763 DOI: 10.1177/00220345211005338] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Accurate segmentation of the jaw (i.e., mandible and maxilla) and the teeth in cone beam computed tomography (CBCT) scans is essential for orthodontic diagnosis and treatment planning. Although various (semi)automated methods have been proposed to segment the jaw or the teeth, there is still a lack of fully automated segmentation methods that can simultaneously segment both anatomic structures in CBCT scans (i.e., multiclass segmentation). In this study, we aimed to train and validate a mixed-scale dense (MS-D) convolutional neural network for multiclass segmentation of the jaw, the teeth, and the background in CBCT scans. Thirty CBCT scans were obtained from patients who had undergone orthodontic treatment. Gold standard segmentation labels were manually created by 4 dentists. As a benchmark, we also evaluated MS-D networks that segmented the jaw or the teeth (i.e., binary segmentation). All segmented CBCT scans were converted to virtual 3-dimensional (3D) models. The segmentation performance of all trained MS-D networks was assessed by the Dice similarity coefficient and surface deviation. The CBCT scans segmented by the MS-D network demonstrated a large overlap with the gold standard segmentations (Dice similarity coefficient: 0.934 ± 0.019, jaw; 0.945 ± 0.021, teeth). The MS-D network–based 3D models of the jaw and the teeth showed minor surface deviations when compared with the corresponding gold standard 3D models (0.390 ± 0.093 mm, jaw; 0.204 ± 0.061 mm, teeth). The MS-D network took approximately 25 s to segment 1 CBCT scan, whereas manual segmentation took about 5 h. This study showed that multiclass segmentation of jaw and teeth was accurate and its performance was comparable to binary segmentation. The MS-D network trained for multiclass segmentation would therefore make patient-specific orthodontic treatment more feasible by strongly reducing the time required to segment multiple anatomic structures in CBCT scans.
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Affiliation(s)
- H Wang
- Department of Oral and Maxillofacial Surgery/Pathology, 3D Innovation Lab, Amsterdam Movement Sciences, Amsterdam UMC, Academic Centre for Dentistry Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - J Minnema
- Department of Oral and Maxillofacial Surgery/Pathology, 3D Innovation Lab, Amsterdam Movement Sciences, Amsterdam UMC, Academic Centre for Dentistry Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - K J Batenburg
- Centrum Wiskunde and Informatica, Amsterdam, the Netherlands
| | - T Forouzanfar
- Department of Oral and Maxillofacial Surgery/Pathology, 3D Innovation Lab, Amsterdam Movement Sciences, Amsterdam UMC, Academic Centre for Dentistry Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - F J Hu
- Institute of Information Technology, Zhejiang Shuren University, Hangzhou, China
| | - G Wu
- Department of Oral and Maxillofacial Surgery/Pathology, 3D Innovation Lab, Amsterdam Movement Sciences, Amsterdam UMC, Academic Centre for Dentistry Amsterdam, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.,Department of Oral Implantology and Prosthetic Dentistry, Academic Centre for Dentistry Amsterdam, University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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32
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Migliorati M, Cevidanes L, Sinfonico G, Drago S, Dalessandri D, Isola G, Biavati AS. Three dimensional movement analysis of maxillary impacted canine using TADs: a pilot study. Head Face Med 2021; 17:1. [PMID: 33451343 PMCID: PMC7809730 DOI: 10.1186/s13005-020-00252-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 12/21/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The aim of the present study was to compare two different anchorage systems efficiency to disinclude impacted maxillary canines using as evaluation tool superimposed Cone Beam Computed Tomography (CBCTs). METHODS The study has been conducted with two parallel groups with an allocation ratio of 1:1. Group test received treatment using as anchorage a miniscrew, control group was treated using an anchorage unit a trans palatal arch (TPA). Both groups received a calibrated traction force of 50 g. CBCT before treatment and 3 months after traction were superimposed and canine tip and root movement were evaluated in mm/month ratio. RESULTS No differences were observed between groups for apex displacement, tip displacement and observation timespan. Twenty-two patients (12 female, 10 male, mean age:13.4 years) undergoing orthodontic treatment for impacted maxillary canines were recruited for this study. No differences were observed between groups for apex displacement, tip displacement and observation timespan. CONCLUSIONS The present pilot study provided no evidence that indirect anchorage on miniscrews could make canine disimpaction faster than anchorage on a TPA. An apex root movement of 0.4-0.8 mm per month was found, while average canine tip movement ranged between 1.08 mm and 1.96 mm per month. No miniscrews failures were observed. TRIAL REGISTRATION The study reports the preliminary results of the randomized clinical trial registered at www.register.clinicaltrials.gov (registration number: NCT01717417 ).
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Affiliation(s)
- Marco Migliorati
- Orthodontics Department, School of Dentistry University of Genova largo Rosanna Benzi, 10 16132, Genoa, Italy.
| | - Lucia Cevidanes
- Department of Orthodontics and Pediatric Dentistry, University of Michigan, School of Dentistry, Ann Arbor, USA
| | | | - Sara Drago
- Orthodontics Department, School of Dentistry University of Genova largo Rosanna Benzi, 10 16132, Genoa, Italy
| | - Domenico Dalessandri
- Department Of Orthodontics, School of Dentistry, University of Brescia, Brescia, Italy
| | - Gaetano Isola
- Department of General Surgery and Surgical-Medical Specialties, University of Catania, Catania, Italy
| | - Armando Silvestrini Biavati
- Orthodontics Department, School of Dentistry University of Genova largo Rosanna Benzi, 10 16132, Genoa, Italy
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Li C, Lin L, Zheng Z, Chung CH. A User-Friendly Protocol for Mandibular Segmentation of CBCT Images for Superimposition and Internal Structure Analysis. J Clin Med 2021; 10:jcm10010127. [PMID: 33401443 PMCID: PMC7796406 DOI: 10.3390/jcm10010127] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/22/2020] [Accepted: 12/23/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Since cone-beam computed tomography (CBCT) technology has been widely adopted in orthodontics, multiple attempts have been made to devise techniques for mandibular segmentation and 3D superimposition. Unfortunately, as the software utilized in these methods are not specifically designed for orthodontics, complex procedures are often necessary to analyze each case. Thus, this study aimed to establish an orthodontist-friendly protocol for segmenting the mandible from CBCT images that maintains access to the internal anatomic structures. METHODS The "sculpting tool" in the Dolphin 3D Imaging software was used for segmentation. The segmented mandible images were saved as STL files for volume matching in the 3D Slicer to validate the repeatability of the current protocol and were exported as DICOM files for internal structure analysis and voxel-based superimposition. RESULTS The mandibles of all tested CBCT datasets were successfully segmented. The volume matching analysis showed high consistency between two independent segmentations for each mandible. The intraclass correlation coefficient (ICC) analysis on 20 additional CBCT mandibular segmentations further demonstrated the high consistency of the current protocol. Moreover, all of the anatomical structures for superimposition identified by the American Board of Orthodontics were found in the voxel-based superimposition, demonstrating the ability to conduct precise internal structure analyses with the segmented images. CONCLUSION An efficient and precise protocol to segment the mandible while retaining access to the internal structures was developed on the basis of CBCT images.
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Affiliation(s)
- Chenshuang Li
- Department of Orthodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
- Correspondence: (C.L.); (C.-H.C.); Tel.: +1-215-898-7130 (C.-H.C.)
| | - Leanne Lin
- Department of Orthodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Zhong Zheng
- Division of Growth and Development, Section of Orthodontics, School of Dentistry, University of California, Los Angeles, CA 90095, USA;
- Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Chun-Hsi Chung
- Department of Orthodontics, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
- Correspondence: (C.L.); (C.-H.C.); Tel.: +1-215-898-7130 (C.-H.C.)
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Kolahi J, Seyedhamzeh M, Dunning D, Kalbasi N. Re-Imagining Machine Learning in Dental Research: A Lesson Learned from the COVID-19 Pandemic. DENTAL HYPOTHESES 2021. [DOI: 10.4103/denthyp.denthyp_169_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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