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Kim Y, Lee S, Jo G, Kwon A, Kang J, Kim J, Huh K, Yi W, Heo M, Choi S. Comparative analysis of clinical image evaluation charts for panoramic radiography. Oral Radiol 2024; 40:520-529. [PMID: 38977537 PMCID: PMC11379777 DOI: 10.1007/s11282-024-00765-3] [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: 03/27/2024] [Accepted: 06/30/2024] [Indexed: 07/10/2024]
Abstract
OBJECTIVE To compare and analyze professional (P chart) and simple (S chart) clinical image evaluation charts for evaluating panoramic radiograph image quality. METHODS Ten evaluators assessed 285 clinical panoramic radiograph images. The evaluators were divided into oral and maxillofacial radiologists (OMFR, n = 5) and general dentist (dentists not specializing in oral and maxillofacial radiology, G, n = 5) groups. For image evaluation, P and S charts provided by the Korean Academy of Oral and Maxillofacial Radiology were used. Scores of items for each evaluation chart were used to compare the reliability, correlation, evaluation scores, evaluation time, and preference, and statistical analyses were performed using IBM SPSS Statistics. RESULTS The S chart showed similar levels of evaluation scores at shorter evaluation time, as compared to the P chart. In the results for each evaluation chart, all analyzed correlations were statistically significant. Total score, image density/contrast/sharpness, and overall image quality items showed a very high positive correlation in the P chart. While the overall range of correlation coefficients was relatively lower in the S chart than the P chart, the same items showed high correlation coefficients. In the preference evaluation, both the professional and generalist groups preferred the S chart. CONCLUSIONS A comparative analysis with the P chart, revisions, and upgrades are needed for the S chart items that showed low correlations in this study, such as artifacts, coverage area, and patient movement.
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Affiliation(s)
- Yeonhee Kim
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Samsun Lee
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea.
| | - Gyudong Jo
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Ahyoung Kwon
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Juhee Kang
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Joeun Kim
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Kyunghoe Huh
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Wonjin Yi
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Minsuk Heo
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Soonchul Choi
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
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Erturk M, Öziç MÜ, Tassoker M. Deep Convolutional Neural Network for Automated Staging of Periodontal Bone Loss Severity on Bite-wing Radiographs: An Eigen-CAM Explainability Mapping Approach. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01218-3. [PMID: 39147888 DOI: 10.1007/s10278-024-01218-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 07/16/2024] [Accepted: 07/29/2024] [Indexed: 08/17/2024]
Abstract
Periodontal disease is a significant global oral health problem. Radiographic staging is critical in determining periodontitis severity and treatment requirements. This study aims to automatically stage periodontal bone loss using a deep learning approach using bite-wing images. A total of 1752 bite-wing images were used for the study. Radiological examinations were classified into 4 groups. Healthy (normal), no bone loss; stage I (mild destruction), bone loss in the coronal third (< 15%); stage II (moderate destruction), bone loss is in the coronal third and from 15 to 33% (15-33%); stage III-IV (severe destruction), bone loss extending from the middle third to the apical third with furcation destruction (> 33%). All images were converted to 512 × 400 dimensions using bilinear interpolation. The data was divided into 80% training validation and 20% testing. The classification module of the YOLOv8 deep learning model was used for the artificial intelligence-based classification of the images. Based on four class results, it was trained using fivefold cross-validation after transfer learning and fine tuning. After the training, 20% of test data, which the system had never seen, were analyzed using the artificial intelligence weights obtained in each cross-validation. Training and test results were calculated with average accuracy, precision, recall, and F1-score performance metrics. Test images were analyzed with Eigen-CAM explainability heat maps. In the classification of bite-wing images as healthy, mild destruction, moderate destruction, and severe destruction, training performance results were 86.100% accuracy, 84.790% precision, 82.350% recall, and 84.411% F1-score, and test performance results were 83.446% accuracy, 81.742% precision, 80.883% recall, and 81.090% F1-score. The deep learning model gave successful results in staging periodontal bone loss in bite-wing images. Classification scores were relatively high for normal (no bone loss) and severe bone loss in bite-wing images, as they are more clearly visible than mild and moderate damage.
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Affiliation(s)
- Mediha Erturk
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Konya, Turkey
| | - Muhammet Üsame Öziç
- Faculty of Technology Department of Biomedical Engineering, Pamukkale University, Denizli, Turkey.
| | - Melek Tassoker
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Konya, Turkey
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Zhang W, Wang Y, Ye L, Zhou Y. Distribution and influencing factors of pulp stones based on CBCT: a retrospective observational study from southwest China. BMC Oral Health 2024; 24:947. [PMID: 39148066 PMCID: PMC11325625 DOI: 10.1186/s12903-024-04727-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 08/08/2024] [Indexed: 08/17/2024] Open
Abstract
BACKGROUND Pulp stones are a type of pulp calcification, the presence of which tends to hinder endodontic treatment. Thus, this retrospective study aimed to analyze the distribution of pulp stones in the population in southwest China and identify the influencing factors. MATERIALS Cone-beam computed tomography (CBCT) scans of 5066 teeth of 200 patients (91 males and 109 females) aged 16-45 years were evaluated. Pulp stones were marked as either present or absent when distinct radiopaque masses were found in the pulp cavity, then evaluated the occurrence of pulp stones with regard to tooth type, sex, age group, and contact it with tooth status. The Pearson chi-square test and nonparametric test were used for statistical analysis. RESULTS Pulp stones were detected in 49.0% of patients and 7.4% of teeth, respectively. The incidence in females was 1.9 times higher than in males (OR = 1.9, 95% CI = 1.1-3.3, p = 0.001). Pulp stones were most prevalent in patients 36-45 years of age. Furthermore, in the age range of 16-45 years, the likelihood of finding pulp stones increased 1.1 times per year with age (OR = 1.1, 95% CI = 1.0-1.1, p = 0.032). A higher incidence of pulp stones was observed in the maxilla and molars. Of the 5066 teeth studied, pulp stones were more common in non-intact teeth. CONCLUSION Nearly half of the population in southwest China had pulp stones. Pulp stones were found significantly more often in females, maxilla, and non-intact teeth, and their frequency increased with age. For dentists, understanding the distribution of pulp stones is crucial for the proper design of root canal treatment (RCT). TRIAL REGISTRATION This study was approved by the Ethics Committee of the Affiliated Hospital of Stomatology, Southwest Medical University (certificate number: 20220818001).
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Affiliation(s)
- Wantong Zhang
- Department of Preventive Dentistry, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, 646000, China
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Luzhou, 646000, China
- Institute of Stomatology, Southwest Medical University, Luzhou, 646000, China
| | - Yao Wang
- Department of Preventive Dentistry, The Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, 646000, China.
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Luzhou, 646000, China.
- Institute of Stomatology, Southwest Medical University, Luzhou, 646000, China.
| | - Lin Ye
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Luzhou, 646000, China
- Institute of Stomatology, Southwest Medical University, Luzhou, 646000, China
| | - Yan Zhou
- Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Luzhou, 646000, China
- Institute of Stomatology, Southwest Medical University, Luzhou, 646000, China
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Pul U, Schwendicke F. Artificial intelligence for detecting periapical radiolucencies: A systematic review and meta-analysis. J Dent 2024; 147:105104. [PMID: 38851523 DOI: 10.1016/j.jdent.2024.105104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 05/24/2024] [Accepted: 05/27/2024] [Indexed: 06/10/2024] Open
Abstract
OBJECTIVES Dentists' diagnostic accuracy in detecting periapical radiolucency varies considerably. This systematic review and meta-analysis aimed to investigate the accuracy of artificial intelligence (AI) for detecting periapical radiolucency. DATA Studies reporting diagnostic accuracy and utilizing AI for periapical radiolucency detection, published until November 2023, were eligible for inclusion. Meta-analysis was conducted using the online MetaDTA Tool to calculate pooled sensitivity and specificity. Risk of bias was evaluated using QUADAS-2. SOURCES A comprehensive search was conducted in PubMed/MEDLINE, ScienceDirect, and Institute of Electrical and Electronics Engineers (IEEE) Xplore databases. Studies reporting diagnostic accuracy and utilizing AI tools for periapical radiolucency detection, published until November 2023, were eligible for inclusion. STUDY SELECTION We identified 210 articles, of which 24 met the criteria for inclusion in the review. All but one study used one type of convolutional neural network. The body of evidence comes with an overall unclear to high risk of bias and several applicability concerns. Four of the twenty-four studies were included in a meta-analysis. AI showed a pooled sensitivity and specificity of 0.94 (95 % CI = 0.90-0.96) and 0.96 (95 % CI = 0.91-0.98), respectively. CONCLUSIONS AI demonstrated high specificity and sensitivity for detecting periapical radiolucencies. However, the current landscape suggests a need for diverse study designs beyond traditional diagnostic accuracy studies. Prospective real-life randomized controlled trials using heterogeneous data are needed to demonstrate the true value of AI. CLINICAL SIGNIFICANCE Artificial intelligence tools seem to have the potential to support detecting periapical radiolucencies on imagery. Notably, nearly all studies did not test fully fledged software systems but measured the mere accuracy of AI models in diagnostic accuracy studies. The true value of currently available AI-based software for lesion detection on both 2D and 3D radiographs remains uncertain.
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Affiliation(s)
- Utku Pul
- University for Digital Technologies in Medicine and Dentistry, Wiltz, Luxembourg
| | - Falk Schwendicke
- Conservative Dentistry and Periodontology, LMU Klinikum, Goethestr. 70, Munich 80336, Germany.
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Qutieshat A, Al Rusheidi A, Al Ghammari S, Alarabi A, Salem A, Zelihic M. Comparative analysis of diagnostic accuracy in endodontic assessments: dental students vs. artificial intelligence. Diagnosis (Berl) 2024; 11:259-265. [PMID: 38696271 DOI: 10.1515/dx-2024-0034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 04/22/2024] [Indexed: 05/04/2024]
Abstract
OBJECTIVES This study evaluates the comparative diagnostic accuracy of dental students and artificial intelligence (AI), specifically a modified ChatGPT 4, in endodontic assessments related to pulpal and apical conditions. The findings are intended to offer insights into the potential role of AI in augmenting dental education. METHODS Involving 109 dental students divided into junior (54) and senior (55) groups, the study compared their diagnostic accuracy against ChatGPT's across seven clinical scenarios. Juniors had the American Association of Endodontists (AEE) terminology assistance, while seniors relied on prior knowledge. Accuracy was measured against a gold standard by experienced endodontists, using statistical analysis including Kruskal-Wallis and Dwass-Steel-Critchlow-Fligner tests. RESULTS ChatGPT achieved significantly higher accuracy (99.0 %) compared to seniors (79.7 %) and juniors (77.0 %). Median accuracy was 100.0 % for ChatGPT, 85.7 % for seniors, and 82.1 % for juniors. Statistical tests indicated significant differences between ChatGPT and both student groups (p<0.001), with no notable difference between the student cohorts. CONCLUSIONS The study reveals AI's capability to outperform dental students in diagnostic accuracy regarding endodontic assessments. This underscores AIs potential as a reference tool that students could utilize to enhance their understanding and diagnostic skills. Nevertheless, the potential for overreliance on AI, which may affect the development of critical analytical and decision-making abilities, necessitates a balanced integration of AI with human expertise and clinical judgement in dental education. Future research is essential to navigate the ethical and legal frameworks for incorporating AI tools such as ChatGPT into dental education and clinical practices effectively.
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Affiliation(s)
- Abubaker Qutieshat
- Adult Restorative Dentistry, 442177 Oman Dental College , Muscat, Oman
- Restorative Dentistry, Dundee Dental Hospital and School, University of Dundee, Dundee, UK
| | | | | | | | - Abdurahman Salem
- Dental Technology, 1796 School of Health & Society, University of Bolton , Greater Manchester, UK
| | - Maja Zelihic
- Forbes School of Business and Technology, 191123 University of Arizona Global Campus , Chandler, AZ, USA
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Zanini LGK, Rubira-Bullen IRF, Nunes FDLDS. A Systematic Review on Caries Detection, Classification, and Segmentation from X-Ray Images: Methods, Datasets, Evaluation, and Open Opportunities. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1824-1845. [PMID: 38429559 PMCID: PMC11300762 DOI: 10.1007/s10278-024-01054-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/19/2023] [Accepted: 01/02/2024] [Indexed: 03/03/2024]
Abstract
Dental caries occurs from the interaction between oral bacteria and sugars, generating acids that damage teeth over time. The importance of X-ray images for detecting oral problems is undeniable in dentistry. With technological advances, it is feasible to identify these lesions using techniques such as deep learning, machine learning, and image processing. Therefore, the survey and systematization of these methods are essential to determining the main computational approaches for identifying caries in X-ray images. In this systematic review, we investigated the primary computational methods used for classifying, detecting, and segmenting caries in X-ray images. Following the PRISMA methodology, we selected relevant studies and analyzed their methods, strengths, limitations, imaging modalities, evaluation metrics, datasets, and classification techniques. The review encompassed 42 studies retrieved from the Science Direct, IEEExplore, ACM Digital, and PubMed databases from the Computer Science and Health areas. The results indicate that 12% of the included articles utilized public datasets, with deep learning being the predominant approach, accounting for 69% of the studies. The majority of these studies (76%) focused on classifying dental caries, either in binary or multiclass classification. Panoramic imaging was the most commonly used radiographic modality, representing 29% of the cases studied. Overall, our systematic review provides a comprehensive overview of the computational methods employed in identifying caries in radiographic images and highlights trends, patterns, and challenges in this research field.
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Affiliation(s)
- Luiz Guilherme Kasputis Zanini
- Department of Computer Engineering and Digital Systems, University of São Paulo, Av. Prof. Luciano Gualberto 158, São Paulo, 05508-010, São Paulo, Brazil.
| | | | - Fátima de Lourdes Dos Santos Nunes
- Department of Computer Engineering and Digital Systems, University of São Paulo, Av. Prof. Luciano Gualberto 158, São Paulo, 05508-010, São Paulo, Brazil
- School of Arts, Sciences and Humanities, University of São Paulo, Rua Arlindo Béttio, 1000, São Paulo, 03828-000, São Paulo, Brazil
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Dot G, Chaurasia A, Dubois G, Savoldelli C, Haghighat S, Azimian S, Taramsari AR, Sivaramakrishnan G, Issa J, Dubey A, Schouman T, Gajny L. DentalSegmentator: Robust open source deep learning-based CT and CBCT image segmentation. J Dent 2024; 147:105130. [PMID: 38878813 DOI: 10.1016/j.jdent.2024.105130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/08/2024] [Accepted: 06/12/2024] [Indexed: 06/30/2024] Open
Abstract
OBJECTIVES Segmentation of anatomical structures on dento-maxillo-facial (DMF) computed tomography (CT) or cone beam computed tomography (CBCT) scans is increasingly needed in digital dentistry. The main aim of this research was to propose and evaluate a novel open source tool called DentalSegmentator for fully automatic segmentation of five anatomical structures on DMF CT and CBCT scans: maxilla/upper skull, mandible, upper teeth, lower teeth, and the mandibular canal. METHODS A retrospective sample of 470 CT and CBCT scans was used as a training/validation set. The performance and generalizability of the tool was evaluated by comparing segmentations provided by experts and automatic segmentations in two hold-out test datasets: an internal dataset of 133 CT and CBCT scans acquired before orthognathic surgery and an external dataset of 123 CBCT scans randomly sampled from routine examinations in 5 institutions. RESULTS The mean overall results in the internal test dataset (n = 133) were a Dice similarity coefficient (DSC) of 92.2 ± 6.3 % and a normalised surface distance (NSD) of 98.2 ± 2.2 %. The mean overall results on the external test dataset (n = 123) were a DSC of 94.2 ± 7.4 % and a NSD of 98.4 ± 3.6 %. CONCLUSIONS The results obtained from this highly diverse dataset demonstrate that this tool can provide fully automatic and robust multiclass segmentation for DMF CT and CBCT scans. To encourage the clinical deployment of DentalSegmentator, the pre-trained nnU-Net model has been made publicly available along with an extension for the 3D Slicer software. CLINICAL SIGNIFICANCE DentalSegmentator open source 3D Slicer extension provides a free, robust, and easy-to-use approach to obtaining patient-specific three-dimensional models from CT and CBCT scans. These models serve various purposes in a digital dentistry workflow, such as visualization, treatment planning, intervention, and follow-up.
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Affiliation(s)
- Gauthier Dot
- UFR Odontologie, Universite Paris Cité, Paris, France; Service de Medecine Bucco-Dentaire, AP-HP, Hopital Pitie-Salpetriere, Paris, France; Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers Institute of Technology, Paris, France.
| | - Akhilanand Chaurasia
- Department of Oral Medicine and Radiology, Faculty of Dental Sciences, King George Medical University, Lucknow, Uttar Pradesh, India
| | - Guillaume Dubois
- Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers Institute of Technology, Paris, France; Materialise France, Malakoff, France
| | - Charles Savoldelli
- Department of Oral and Maxillofacial Surgery, Head and Neck Institute, University Hospital of Nice, France
| | - Sara Haghighat
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI On Health, Berlin, Germany
| | - Sarina Azimian
- Research Committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | | | - Julien Issa
- Department of Diagnostics, Chair of Practical Clinical Dentistry, Poznan University of Medical Sciences, Poznan, Poland; Doctoral School, Poznan University of Medical Sciences, Poznan, Poland
| | - Abhishek Dubey
- Department of Oral Medicine and Radiology, Maharana Pratap Dental College, Kanpur, India
| | - Thomas Schouman
- Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers Institute of Technology, Paris, France; AP-HP, Hopital Pitie-Salpetriere, Service de Chirurgie Maxillo-Faciale, Medecine Sorbonne Universite, Paris, France
| | - Laurent Gajny
- Institut de Biomecanique Humaine Georges Charpak, Arts et Metiers Institute of Technology, Paris, France
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Kim P, Seo B, De Silva H. Concordance of clinician, Chat-GPT4, and ORAD diagnoses against histopathology in Odontogenic Keratocysts and tumours: a 15-Year New Zealand retrospective study. Oral Maxillofac Surg 2024:10.1007/s10006-024-01284-5. [PMID: 39060850 DOI: 10.1007/s10006-024-01284-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 07/19/2024] [Indexed: 07/28/2024]
Abstract
BACKGROUND This research aimed to investigate the concordance between clinical impressions and histopathologic diagnoses made by clinicians and artificial intelligence tools for odontogenic keratocyst (OKC) and Odontogenic tumours (OT) in a New Zealand population from 2008 to 2023. METHODS Histopathological records from the Oral Pathology Centre, University of Otago (2008-2023) were examined to identify OKCs and OT. Specimen referral details, histopathologic reports, and clinician differential diagnoses, as well as those provided by ORAD and Chat-GPT4, were documented. Data were analyzed using SPSS, and concordance between provisional and histopathologic diagnoses was ascertained. RESULTS Of the 34,225 biopsies, 302 and 321 samples were identified as OTs and OKCs. Concordance rates were 43.2% for clinicians, 45.6% for ORAD, and 41.4% for Chat-GPT4. Corresponding Kappa value against histological diagnosis were 0.23, 0.13 and 0.14. Surgeons achieved a higher concordance rate (47.7%) compared to non-surgeons (29.82%). Odds ratio of having concordant diagnosis using Chat-GPT4 and ORAD were between 1.4 and 2.8 (p < 0.05). ROC-AUC and PR-AUC were similar between the groups (Clinician 0.62/0.42, ORAD 0.58/0.28, Char-GPT4 0.63/0.37) for ameloblastoma and for OKC (Clinician 0.64/0.78, ORAD 0.66/0.77, Char-GPT4 0.60/0.71). CONCLUSION Clinicians with surgical training achieved higher concordance rate when it comes to OT and OKC. Chat-GPT4 and Bayesian approach (ORAD) have shown potential in enhancing diagnostic capabilities.
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Affiliation(s)
- Paul Kim
- Oral and Maxillofacial Surgery Registrar, Dunedin Hospital, Dunedin, New Zealand.
| | - Benedict Seo
- Department of Oral Diagnostic and Surgical Sciences, University of Otago, Dunedin, New Zealand
| | - Harsha De Silva
- Department of Oral Diagnostic and Surgical Sciences, University of Otago, Dunedin, New Zealand
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Kazimierczak W, Kazimierczak N, Issa J, Wajer R, Wajer A, Kalka S, Serafin Z. Endodontic Treatment Outcomes in Cone Beam Computed Tomography Images-Assessment of the Diagnostic Accuracy of AI. J Clin Med 2024; 13:4116. [PMID: 39064157 PMCID: PMC11278304 DOI: 10.3390/jcm13144116] [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: 06/11/2024] [Revised: 07/09/2024] [Accepted: 07/12/2024] [Indexed: 07/28/2024] Open
Abstract
Background/Objectives: The aim of this study was to assess the diagnostic accuracy of the AI-driven platform Diagnocat for evaluating endodontic treatment outcomes using cone beam computed tomography (CBCT) images. Methods: A total of 55 consecutive patients (15 males and 40 females, aged 12-70 years) referred for CBCT imaging were included. CBCT images were analyzed using Diagnocat's AI platform, which assessed parameters such as the probability of filling, adequate obturation, adequate density, overfilling, voids in filling, short filling, and root canal number. The images were also evaluated by two experienced human readers. Diagnostic accuracy metrics (accuracy, precision, recall, and F1 score) were assessed and compared to the readers' consensus, which served as the reference standard. Results: The AI platform demonstrated high diagnostic accuracy for most parameters, with perfect scores for the probability of filling (accuracy, precision, recall, F1 = 100%). Adequate obturation showed moderate performance (accuracy = 84.1%, precision = 66.7%, recall = 92.3%, and F1 = 77.4%). Adequate density (accuracy = 95.5%, precision, recall, and F1 = 97.2%), overfilling (accuracy = 95.5%, precision = 86.7%, recall = 100%, and F1 = 92.9%), and short fillings (accuracy = 95.5%, precision = 100%, recall = 86.7%, and F1 = 92.9%) also exhibited strong performance. The performance of AI for voids in filling detection (accuracy = 88.6%, precision = 88.9%, recall = 66.7%, and F1 = 76.2%) highlighted areas for improvement. Conclusions: The AI platform Diagnocat showed high diagnostic accuracy in evaluating endodontic treatment outcomes using CBCT images, indicating its potential as a valuable tool in dental radiology.
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Affiliation(s)
- Wojciech Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, University Hospital No. 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13–15, 85-067 Bydgoszcz, Poland
| | - Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Julien Issa
- Chair of Practical Clinical Dentistry, Department of Diagnostics, Poznań University of Medical Sciences, 61-701 Poznań, Poland
| | - Róża Wajer
- Department of Radiology and Diagnostic Imaging, University Hospital No. 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
| | - Adrian Wajer
- Dental Primus, Poznańska 18, 88-100 Inowrocław, Poland
| | - Sandra Kalka
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, University Hospital No. 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13–15, 85-067 Bydgoszcz, Poland
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Alrashed S, Dutra V, Chu TMG, Yang CC, Lin WS. Influence of exposure protocol, voxel size, and artifact removal algorithm on the trueness of segmentation utilizing an artificial-intelligence-based system. J Prosthodont 2024; 33:574-583. [PMID: 38305665 DOI: 10.1111/jopr.13827] [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: 06/28/2023] [Accepted: 01/09/2024] [Indexed: 02/03/2024] Open
Abstract
PURPOSE To evaluate the effects of exposure protocol, voxel sizes, and artifact removal algorithms on the trueness of segmentation in various mandible regions using an artificial intelligence (AI)-based system. MATERIALS AND METHODS Eleven dry human mandibles were scanned using a cone beam computed tomography (CBCT) scanner under differing exposure protocols (standard and ultra-low), voxel sizes (0.15 mm, 0.3 mm, and 0.45 mm), and with or without artifact removal algorithm. The resulting datasets were segmented using an AI-based system, exported as 3D models, and compared to reference files derived from a white-light laboratory scanner. Deviation measurement was performed using a computer-aided design (CAD) program and recorded as root mean square (RMS). The RMS values were used as a representation of the trueness of the AI-segmented 3D models. A 4-way ANOVA was used to assess the impact of voxel size, exposure protocol, artifact removal algorithm, and location on RMS values (α = 0.05). RESULTS Significant effects were found with voxel size (p < 0.001) and location (p < 0.001), but not with exposure protocol (p = 0.259) or artifact removal algorithm (p = 0.752). Standard exposure groups had significantly lower RMS values than the ultra-low exposure groups in the mandible body with 0.3 mm (p = 0.014) or 0.45 mm (p < 0.001) voxel sizes, the symphysis with a 0.45 mm voxel size (p = 0.011), and the whole mandible with a 0.45 mm voxel size (p = 0.001). Exposure protocol did not affect RMS values at teeth and alveolar bone (p = 0.544), mandible angles (p = 0.380), condyles (p = 0.114), and coronoids (p = 0.806) locations. CONCLUSION This study informs optimal exposure protocol and voxel size choices in CBCT imaging for true AI-based automatic segmentation with minimal radiation. The artifact removal algorithm did not influence the trueness of AI segmentation. When using an ultra-low exposure protocol to minimize patient radiation exposure in AI segmentations, a voxel size of 0.15 mm is recommended, while a voxel size of 0.45 mm should be avoided.
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Affiliation(s)
- Safa Alrashed
- Oral Biology PhD program in the College of Dentistry, Division of Restorative and Prosthetic Dentistry, The Ohio State University, Columbus, Ohio, USA
| | - Vinicius Dutra
- Department of Oral Pathology, Medicine, and Radiology, Indiana University School of Dentistry, Indianapolis, Indiana, USA
| | - Tien-Min G Chu
- Department of Biomedical Sciences and Comprehensive Care, Indiana University School of Dentistry, Indianapolis, Indiana, USA
| | - Chao-Chieh Yang
- Department of Prosthodontics, Indiana University School of Dentistry, Indianapolis, Indiana, USA
- Advanced Education Program in Prosthodontics, Department of Prosthodontics, Indiana University School of Dentistry, Indianapolis, Indiana, USA
| | - Wei-Shao Lin
- Department of Prosthodontics, Indiana University School of Dentistry, Indianapolis, Indiana, USA
- Advanced Education Program in Prosthodontics, Department of Prosthodontics, Indiana University School of Dentistry, Indianapolis, Indiana, USA
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11
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Abesi F, Talachi F, Ezoji F. Performance of different cone-beam computed tomography scan modes with and without metal artifact reduction in detection of recurrent dental caries under various restorative materials. Pol J Radiol 2024; 89:e281-e291. [PMID: 39040560 PMCID: PMC11262014 DOI: 10.5114/pjr/188257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 05/03/2024] [Indexed: 07/24/2024] Open
Abstract
Purpose We aimed to compare the diagnostic performance of different cone-beam computed tomography (CBCT) scan modes with and without the application of a metal artifact reduction (MAR) option under 5 different restorative materials. Material and methods Our research was an in vitro study with 150 caries-free premolars and molars. The teeth were randomly divided into experimental (with artificially induced caries, n = 75) and control (without caries, n = 75) groups and were prepared based on 5 types of restorative materials, including conventional composites (Filtek Z250, Gradia), flow composite, glass ionomer, and amalgam. The teeth were examined under 2 CBCT scan modes (high-resolution [HIRes] and standard) with and without MAR application. Finally, the diagnostic accuracy index values (area under the receiver operating characteristic curve [AUC], sensitivity, and specificity) were calculated. Results The AUC of standard scan mode with the MAR option was significantly lower than that of HIRes with MAR (p = 0.018) and without MAR option (p = 0.011) in detecting recurrent caries. Also, without MAR option, the diagnostic accuracy (AUC) of the standard mode was significantly lower than that of the HIRes (p = 0.020). Similar findings were observed for sensitivity and specificity. Moreover, diagnostic performance of standard and HIRes scan modes with and without MAR in the amalgam group was lower than that in other restorative material groups. Conclusions Diagnostic performance of HIRes CBCT mode was higher than that of standard mode for recurrent caries and remained unaffected by MAR application. However, the accuracy in detecting recurrent caries was lower in the amalgam group compared with other restorative material groups.
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Affiliation(s)
- Farida Abesi
- Dental Materials Research Centre, Department of Oral and Maxillofacial Radiology, Dental Faculty, Babol University of Medical Sciences, Babol, Iran
| | | | - Fariba Ezoji
- Dental Materials Research Centre, Health Research Institute, Faculty of Dentistry, Babol University of Medical Sciences, Babol, Iran
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12
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Chen S, Yang Y, Wu W, Wei R, Wang Z, Tay FR, Hu J, Ma J. Classification of Caries Based on CBCT: A Deep Learning Network Interpretability Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01143-5. [PMID: 38806951 DOI: 10.1007/s10278-024-01143-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 04/16/2024] [Accepted: 05/16/2024] [Indexed: 05/30/2024]
Abstract
This study aimed to create a caries classification scheme based on cone-beam computed tomography (CBCT) and develop two deep learning models to improve caries classification accuracy. A total of 2713 axial slices were obtained from CBCT images of 204 carious teeth. Both classification models were trained and tested using the same pretrained classification networks on the dataset, including ResNet50_vd, MobileNetV3_large_ssld, and ResNet50_vd_ssld. The first model was used directly to classify the original images (direct classification model). The second model incorporated a presegmentation step for interpretation (interpretable classification model). Performance evaluation metrics including accuracy, precision, recall, and F1 score were calculated. The Local Interpretable Model-agnostic Explanations (LIME) method was employed to elucidate the decision-making process of the two models. In addition, a minimum distance between caries and pulp was introduced for determining the treatment strategies for type II carious teeth. The direct model that utilized the ResNet50_vd_ssld network achieved top accuracy, precision, recall, and F1 score of 0.700, 0.786, 0.606, and 0.616, respectively. Conversely, the interpretable model consistently yielded metrics surpassing 0.917, irrespective of the network employed. The LIME algorithm confirmed the interpretability of the classification models by identifying key image features for caries classification. Evaluation of treatment strategies for type II carious teeth revealed a significant negative correlation (p < 0.01) with the minimum distance. These results demonstrated that the CBCT-based caries classification scheme and the two classification models appeared to be acceptable tools for the diagnosis and categorization of dental caries.
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Affiliation(s)
- Surong Chen
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Yan Yang
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Weiwei Wu
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Ruonan Wei
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Zezhou Wang
- West China School of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Franklin R Tay
- Department of Endodontics, Dental College of Georgia, Augusta University, Augusta, GA, USA
| | - Jingyu Hu
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China.
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China.
| | - Jingzhi Ma
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China.
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China.
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13
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Kazimierczak W, Wajer R, Wajer A, Kiian V, Kloska A, Kazimierczak N, Janiszewska-Olszowska J, Serafin Z. Periapical Lesions in Panoramic Radiography and CBCT Imaging-Assessment of AI's Diagnostic Accuracy. J Clin Med 2024; 13:2709. [PMID: 38731237 PMCID: PMC11084607 DOI: 10.3390/jcm13092709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 04/30/2024] [Accepted: 05/02/2024] [Indexed: 05/13/2024] Open
Abstract
Background/Objectives: Periapical lesions (PLs) are frequently detected in dental radiology. Accurate diagnosis of these lesions is essential for proper treatment planning. Imaging techniques such as orthopantomogram (OPG) and cone-beam CT (CBCT) imaging are used to identify PLs. The aim of this study was to assess the diagnostic accuracy of artificial intelligence (AI) software Diagnocat for PL detection in OPG and CBCT images. Methods: The study included 49 patients, totaling 1223 teeth. Both OPG and CBCT images were analyzed by AI software and by three experienced clinicians. All the images were obtained in one patient cohort, and findings were compared to the consensus of human readers using CBCT. The AI's diagnostic accuracy was compared to a reference method, calculating sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Results: The AI's sensitivity for OPG images was 33.33% with an F1 score of 32.73%. For CBCT images, the AI's sensitivity was 77.78% with an F1 score of 84.00%. The AI's specificity was over 98% for both OPG and CBCT images. Conclusions: The AI demonstrated high sensitivity and high specificity in detecting PLs in CBCT images but lower sensitivity in OPG images.
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Affiliation(s)
- Wojciech Kazimierczak
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, University Hospital no 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Róża Wajer
- Department of Radiology and Diagnostic Imaging, University Hospital no 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
| | - Adrian Wajer
- Dental Primus, Poznańska 18, 88-100 Inowrocław, Poland
| | - Veronica Kiian
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Anna Kloska
- The Faculty of Medicine, Bydgoszcz University of Science and Technology, Kaliskiego 7, 85-796 Bydgoszcz, Poland
| | - Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Joanna Janiszewska-Olszowska
- Department of Interdisciplinary Dentistry, Pomeranian Medical University in Szczecin, Al. Powstańców Wlkp. 72, 70-111 Szczecin, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, University Hospital no 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
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14
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Gurgel M, Alvarez MA, Aristizabal JF, Baquero B, Gillot M, Al Turkestani N, Miranda F, Castillo AAD, Bianchi J, de Oliveira Ruellas AC, Ioshida M, Yatabe M, Rey D, Prieto J, Cevidanes L. Automated artificial intelligence-based three-dimensional comparison of orthodontic treatment outcomes with and without piezocision surgery. Orthod Craniofac Res 2024; 27:321-331. [PMID: 38009409 PMCID: PMC10949222 DOI: 10.1111/ocr.12737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 09/26/2023] [Accepted: 11/12/2023] [Indexed: 11/28/2023]
Abstract
OBJECTIVE(S) This study aims to evaluate the influence of the piezocision surgery in the orthodontic biomechanics, as well as in the magnitude and direction of tooth movement in the mandibular arch using novel artificial intelligence (AI)-automated tools. MATERIALS AND METHODS Nineteen patients, who had piezocision performed in the lower arch at the beginning of treatment with the goal of accelerating tooth movement, were compared to 19 patients who did not receive piezocision. Cone beam computed tomography (CBCT) and intraoral scans (IOS) were acquired before and after orthodontic treatment. AI-automated dental tools were used to segment and locate landmarks in dental crowns from IOS and root canals from CBCT scans to quantify 3D tooth movement. Differences in mesial-distal, buccolingual, intrusion and extrusion linear movements, as well as tooth long axis angulation and rotation were compared. RESULTS The treatment time for the control and experimental groups were 13.2 ± 5.06 and 13 ± 5.52 months respectively (P = .176). Overall, anterior and posterior tooth movement presented similar 3D linear and angular changes in the groups. The piezocision group demonstrated greater (P = .01) mesial long axis angulation of lower right first premolar (4.4 ± 6°) compared with control group (0.02 ± 4.9°), while the mesial rotation was significantly smaller (P = .008) in the experimental group (0.5 ± 7.8°) than in the control (8.5 ± 9.8°) considering the same tooth. CONCLUSION The open source-automated dental tools facilitated the clinicians' assessment of piezocision treatment outcomes. The piezocision surgery prior to the orthodontic treatment did not decrease the treatment time and did not influence in the orthodontic biomechanics, leading to similar tooth movements compared to conventional treatment.
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Affiliation(s)
- Marcela Gurgel
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, Michigan, USA
| | | | | | - Baptiste Baquero
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, Michigan, USA
| | - Maxime Gillot
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, Michigan, USA
| | - Najla Al Turkestani
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, Michigan, USA
| | - Felicia Miranda
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, Michigan, USA
| | - Aron Aliaga-Del Castillo
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, Michigan, USA
| | - Jonas Bianchi
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Marcos Ioshida
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, Michigan, USA
| | - Marilia Yatabe
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, Michigan, USA
| | - Diego Rey
- Department of Orthodontics, CES University, Medellin, Colombia
| | - Juan Prieto
- Department of Computer Sciences, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Lucia Cevidanes
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, Michigan, USA
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15
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Calazans MAA, Pontual ADA, Pontual MLDA, Ferreira FABS, Santos A, Alcoforado MDLMG, Ramos-Perez FMDM, Madeiro F. A system for automatic classification of endodontic treatment quality in CBCT. Clin Oral Investig 2024; 28:223. [PMID: 38507031 DOI: 10.1007/s00784-024-05599-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 03/02/2024] [Indexed: 03/22/2024]
Abstract
OBJECTIVES An evaluation of the effectiveness of a new computational system proposed for automatic classification, developed based on a Siamese network combined with Convolutional Neural Networks (CNNs), is presented. It aims to identify endodontic technical errors using Cone Beam Computed Tomography (CBCT). The study also aims to compare the performance of the automatic classification system with that of dentists. METHODS One thousand endodontically treated maxillary molars sagittal and coronal reconstructions were evaluated for the quality of the endodontic treatment and the presence of periapical hypodensities by three board-certified dentists and by an oral and maxillofacial radiologist. The proposed classification system was based on a Siamese network combined with EfficientNet B1 or EfficientNet B7 networks. Accuracy, sensivity, precision, specificity, and F1-score values were calculated for automated artificial systems and dentists. Chi-square tests were performed. RESULTS The performances were obtained for EfficienteNet B1, EfficientNet B7 and dentists. Regarding accuracy, sensivity and specificity, the best results were obtained with EfficientNet B1. Concerning precision and F1-score, the best results were obtained with EfficientNet B7. The presence of periapical hypodensity lesions was associated with endodontic technical errors. In contrast, the absence of endodontic technical errors was associated with the absence of hypodensity. CONCLUSIONS Quality evaluation of the endodontic treatment performed by dentists and by Siamese Network combined with EfficientNet B7 or EfficientNet B1 networks was comparable with a slight superiority for the Siamese Network. CLINICAL RELEVANCE CNNs have the potential to be used as a support and standardization tool in assessing endodontic treatment quality in clinical practice.
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Affiliation(s)
| | - Andréa Dos Anjos Pontual
- Departamento de Clínica e Odontologia Preventiva, Universidade Federal de Pernambuco, Recife, 50670-420, Pernambuco, Brasil
| | - Maria Luíza Dos Anjos Pontual
- Departamento de Clínica e Odontologia Preventiva, Universidade Federal de Pernambuco, Recife, 50670-420, Pernambuco, Brasil
| | - Felipe Alberto B S Ferreira
- Unidade Acadêmica do Cabo de Santo Agostinho, Universidade Federal Rural de Pernambuco, Cabo de Santo Agostinho, 54518-430, Pernambuco, Brasil
| | - Andrezza Santos
- Departamento de Clínica e Odontologia Preventiva, Universidade Federal de Pernambuco, Recife, 50670-420, Pernambuco, Brasil
| | | | | | - Francisco Madeiro
- Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife, 50720-001, Pernambuco, Brasil
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16
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Ndiaye AD, Gasqui MA, Millioz F, Perard M, Leye Benoist F, Grosgogeat B. Exploring the Methodological Approaches of Studies on Radiographic Databases Used in Cariology to Feed Artificial Intelligence: A Systematic Review. Caries Res 2024; 58:117-140. [PMID: 38342096 DOI: 10.1159/000536277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 01/04/2024] [Indexed: 02/13/2024] Open
Abstract
INTRODUCTION A growing number of studies on diagnostic imaging show superior efficiency and accuracy of computer-aided diagnostic systems compared to those of certified dentists. This methodological systematic review aimed to evaluate the different methodological approaches used by studies focusing on machine learning and deep learning that have used radiographic databases to classify, detect, and segment dental caries. METHODS The protocol was registered in PROSPERO before data collection (CRD42022348097). Literature research was performed in MEDLINE, Embase, IEEE Xplore, and Web of Science until December 2022, without language restrictions. Studies and surveys using a dental radiographic database for the classification, detection, or segmentation of carious lesions were sought. Records deemed eligible were retrieved and further assessed for inclusion by two reviewers who resolved any discrepancies through consensus. A third reviewer was consulted when any disagreements or discrepancies persisted between the two reviewers. After data extraction, the same reviewers assessed the methodological quality using the CLAIM and QUADAS-AI checklists. RESULTS After screening 325 articles, 35 studies were eligible and included. The bitewing was the most commonly used radiograph (n = 17) at the time when detection (n = 15) was the most explored computer vision task. The sample sizes used ranged from 95 to 38,437, while the augmented training set ranged from 300 to 315,786. Convolutional neural network was the most commonly used model. The mean completeness of CLAIM items was 49% (SD ± 34%). The applicability of the CLAIM checklist items revealed several weaknesses in the methodology of the selected studies: most of the studies were monocentric, and only 9% of them used an external test set when evaluating the model's performance. The QUADAS-AI tool revealed that only 43% of the studies included in this systematic review were at low risk of bias concerning the standard reference domain. CONCLUSION This review demonstrates that the overall scientific quality of studies conducted to feed artificial intelligence algorithms is low. Some improvement in the design and validation of studies can be made with the development of a standardized guideline for the reproducibility and generalizability of results and, thus, their clinical applications.
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Affiliation(s)
- Amadou Diaw Ndiaye
- Service d'Odontologie Conservatrice-Endodontie, Université Cheikh Anta Diop, Dakar, Senegal,
| | - Marie Agnès Gasqui
- Laboratoire des Multimatériaux et Interfaces (LMI), UMR CNRS, Université Claude Bernard Lyon 1, Lyon, France
- Service d'Odontologie, Hospices Civils de Lyon, Lyon, France
| | - Fabien Millioz
- CREATIS (Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé) - CNRS UMR - INSERM U1294 - Université Claude Bernard Lyon 1 - INSA Lyon, Lyon - Université Jean Monnet Saint-Etienne, Saint-Etienne, France
| | - Matthieu Perard
- University Rennes, INSERM, Rennes, France
- CHU Rennes, Rennes, France
| | - Fatou Leye Benoist
- Service d'Odontologie Conservatrice-Endodontie, Université Cheikh Anta Diop, Dakar, Senegal
| | - Brigitte Grosgogeat
- Laboratoire des Multimatériaux et Interfaces (LMI), UMR CNRS, Université Claude Bernard Lyon 1, Lyon, France
- Service d'Odontologie, Hospices Civils de Lyon, Lyon, France
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Kazimierczak N, Kazimierczak W, Serafin Z, Nowicki P, Nożewski J, Janiszewska-Olszowska J. AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review. J Clin Med 2024; 13:344. [PMID: 38256478 PMCID: PMC10816993 DOI: 10.3390/jcm13020344] [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: 11/19/2023] [Revised: 12/29/2023] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
The advent of artificial intelligence (AI) in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. Its usage in orthodontic practices worldwide has increased with the availability of various AI applications and tools. This review explores the principles of AI, its applications in orthodontics, and its implementation in clinical practice. A comprehensive literature review was conducted, focusing on AI applications in dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision making, and patient telemonitoring. Due to study heterogeneity, no meta-analysis was possible. AI has demonstrated high efficacy in all these areas, but variations in performance and the need for manual supervision suggest caution in clinical settings. The complexity and unpredictability of AI algorithms call for cautious implementation and regular manual validation. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for successful integration into orthodontic practice.
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Affiliation(s)
- Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Wojciech Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Paweł Nowicki
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Jakub Nożewski
- Department of Emeregncy Medicine, University Hospital No 2 in Bydgoszcz, Ujejskiego 75, 85-168 Bydgoszcz, Poland
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Hadzic A, Urschler M, Press JNA, Riedl R, Rugani P, Štern D, Kirnbauer B. Evaluating a Periapical Lesion Detection CNN on a Clinically Representative CBCT Dataset-A Validation Study. J Clin Med 2023; 13:197. [PMID: 38202204 PMCID: PMC10779652 DOI: 10.3390/jcm13010197] [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: 11/20/2023] [Revised: 12/20/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024] Open
Abstract
The aim of this validation study was to comprehensively evaluate the performance and generalization capability of a deep learning-based periapical lesion detection algorithm on a clinically representative cone-beam computed tomography (CBCT) dataset and test for non-inferiority. The evaluation involved 195 CBCT images of adult upper and lower jaws, where sensitivity and specificity metrics were calculated for all teeth, stratified by jaw, and stratified by tooth type. Furthermore, each lesion was assigned a periapical index score based on its size to enable a score-based evaluation. Non-inferiority tests were conducted with proportions of 90% for sensitivity and 82% for specificity. The algorithm achieved an overall sensitivity of 86.7% and a specificity of 84.3%. The non-inferiority test indicated the rejection of the null hypothesis for specificity but not for sensitivity. However, when excluding lesions with a periapical index score of one (i.e., very small lesions), the sensitivity improved to 90.4%. Despite the challenges posed by the dataset, the algorithm demonstrated promising results. Nevertheless, further improvements are needed to enhance the algorithm's robustness, particularly in detecting very small lesions and the handling of artifacts and outliers commonly encountered in real-world clinical scenarios.
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Affiliation(s)
- Arnela Hadzic
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036 Graz, Austria; (A.H.); (R.R.)
| | - Martin Urschler
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036 Graz, Austria; (A.H.); (R.R.)
| | - Jan-Niclas Aaron Press
- Division of Oral Surgery and Orthodontics, Medical University of Graz, 8010 Graz, Austria (P.R.); (B.K.)
| | - Regina Riedl
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036 Graz, Austria; (A.H.); (R.R.)
| | - Petra Rugani
- Division of Oral Surgery and Orthodontics, Medical University of Graz, 8010 Graz, Austria (P.R.); (B.K.)
| | - Darko Štern
- Institute of Computer Graphics and Vision, Graz University of Technology, 8010 Graz, Austria
| | - Barbara Kirnbauer
- Division of Oral Surgery and Orthodontics, Medical University of Graz, 8010 Graz, Austria (P.R.); (B.K.)
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Li X, Zhao D, Xie J, Wen H, Liu C, Li Y, Li W, Wang S. Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis. BMC Oral Health 2023; 23:1017. [PMID: 38114946 PMCID: PMC10729340 DOI: 10.1186/s12903-023-03751-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/08/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND The development of deep learning (DL) algorithms for use in dentistry is an emerging trend. Periodontitis is one of the most prevalent oral diseases, which has a notable impact on the life quality of patients. Therefore, it is crucial to classify periodontitis accurately and efficiently. This systematic review aimed to identify the application of DL for the classification of periodontitis and assess the accuracy of this approach. METHODS A literature search up to November 2023 was implemented through EMBASE, PubMed, Web of Science, Scopus, and Google Scholar databases. Inclusion and exclusion criteria were used to screen eligible studies, and the quality of the studies was evaluated by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology with the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool. Random-effects inverse-variance model was used to perform the meta-analysis of a diagnostic test, with which pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) were calculated, and a summary receiver operating characteristic (SROC) plot was constructed. RESULTS Thirteen studies were included in the meta-analysis. After excluding an outlier, the pooled sensitivity, specificity, positive LR, negative LR and DOR were 0.88 (95%CI 0.82-0.92), 0.82 (95%CI 0.72-0.89), 4.9 (95%CI 3.2-7.5), 0.15 (95%CI 0.10-0.22) and 33 (95%CI 19-59), respectively. The area under the SROC was 0.92 (95%CI 0.89-0.94). CONCLUSIONS The accuracy of DL-based classification of periodontitis is high, and this approach could be employed in the future to reduce the workload of dental professionals and enhance the consistency of classification.
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Affiliation(s)
- Xin Li
- School of Public Health, National Institute for Data Science in Health and Medicine, Capital Medical University, Beijing, China
| | - Dan Zhao
- Department of Implant Dentistry, Beijing Stomatological Hospital, Capital Medical University, Beijing, China
| | - Jinxuan Xie
- School of Public Health, National Institute for Data Science in Health and Medicine, Capital Medical University, Beijing, China
| | - Hao Wen
- City University of Hong Kong, Hong Kong SAR, China
| | - Chunhua Liu
- City University of Hong Kong, Hong Kong SAR, China
| | - Yajie Li
- School of Public Health, National Institute for Data Science in Health and Medicine, Capital Medical University, Beijing, China
| | - Wenbin Li
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Songlin Wang
- Salivary Gland Disease Center and Beijing Key Laboratory of Tooth Regeneration and Function Reconstruction, Beijing Laboratory of Oral Health and Beijing Stomatological Hospital, Capital Medical University, Beijing, 100050, China.
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20
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Surlari Z, Budală DG, Lupu CI, Stelea CG, Butnaru OM, Luchian I. Current Progress and Challenges of Using Artificial Intelligence in Clinical Dentistry-A Narrative Review. J Clin Med 2023; 12:7378. [PMID: 38068430 PMCID: PMC10707023 DOI: 10.3390/jcm12237378] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 11/25/2023] [Accepted: 11/27/2023] [Indexed: 07/25/2024] Open
Abstract
The concept of machines learning and acting like humans is what is meant by the phrase "artificial intelligence" (AI). Several branches of dentistry are increasingly relying on artificial intelligence (AI) tools. The literature usually focuses on AI models. These AI models have been used to detect and diagnose a wide range of conditions, including, but not limited to, dental caries, vertical root fractures, apical lesions, diseases of the salivary glands, maxillary sinusitis, maxillofacial cysts, cervical lymph node metastasis, osteoporosis, cancerous lesions, alveolar bone loss, the need for orthodontic extractions or treatments, cephalometric analysis, age and gender determination, and more. The primary contemporary applications of AI in the dental field are in undergraduate teaching and research. Before these methods can be used in everyday dentistry, however, the underlying technology and user interfaces need to be refined.
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Affiliation(s)
- Zinovia Surlari
- Department of Fixed Protheses, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania;
| | - Dana Gabriela Budală
- Department of Implantology, Removable Prostheses, Dental Prostheses Technology, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania;
| | - Costin Iulian Lupu
- Department of Dental Management, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Carmen Gabriela Stelea
- Department of Oral Surgery, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Oana Maria Butnaru
- Department of Biophysics, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania;
| | - Ionut Luchian
- Department of Periodontology, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 16 Universității Street, 700115 Iasi, Romania;
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21
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Yeshua T, Ladyzhensky S, Abu-Nasser A, Abdalla-Aslan R, Boharon T, Itzhak-Pur A, Alexander A, Chaurasia A, Cohen A, Sosna J, Leichter I, Nadler C. Deep learning for detection and 3D segmentation of maxillofacial bone lesions in cone beam CT. Eur Radiol 2023; 33:7507-7518. [PMID: 37191921 DOI: 10.1007/s00330-023-09726-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/30/2023] [Accepted: 04/21/2023] [Indexed: 05/17/2023]
Abstract
OBJECTIVES To develop an automated deep-learning algorithm for detection and 3D segmentation of incidental bone lesions in maxillofacial CBCT scans. METHODS The dataset included 82 cone beam CT (CBCT) scans, 41 with histologically confirmed benign bone lesions (BL) and 41 control scans (without lesions), obtained using three CBCT devices with diverse imaging protocols. Lesions were marked in all axial slices by experienced maxillofacial radiologists. All cases were divided into sub-datasets: training (20,214 axial images), validation (4530 axial images), and testing (6795 axial images). A Mask-RCNN algorithm segmented the bone lesions in each axial slice. Analysis of sequential slices was used for improving the Mask-RCNN performance and classifying each CBCT scan as containing bone lesions or not. Finally, the algorithm generated 3D segmentations of the lesions and calculated their volumes. RESULTS The algorithm correctly classified all CBCT cases as containing bone lesions or not, with an accuracy of 100%. The algorithm detected the bone lesion in axial images with high sensitivity (95.9%) and high precision (98.9%) with an average dice coefficient of 83.5%. CONCLUSIONS The developed algorithm detected and segmented bone lesions in CBCT scans with high accuracy and may serve as a computerized tool for detecting incidental bone lesions in CBCT imaging. CLINICAL RELEVANCE Our novel deep-learning algorithm detects incidental hypodense bone lesions in cone beam CT scans, using various imaging devices and protocols. This algorithm may reduce patients' morbidity and mortality, particularly since currently, cone beam CT interpretation is not always preformed. KEY POINTS • A deep learning algorithm was developed for automatic detection and 3D segmentation of various maxillofacial bone lesions in CBCT scans, irrespective of the CBCT device or the scanning protocol. • The developed algorithm can detect incidental jaw lesions with high accuracy, generates a 3D segmentation of the lesion, and calculates the lesion volume.
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Affiliation(s)
- Talia Yeshua
- Department of Applied Physics, The Jerusalem College of Technology, Jerusalem, Israel
| | - Shmuel Ladyzhensky
- Department of Applied Physics, The Jerusalem College of Technology, Jerusalem, Israel
| | - Amal Abu-Nasser
- Oral Maxillofacial Imaging, Department of Oral Medicine, Sedation and Imaging, Faculty of Dental Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ragda Abdalla-Aslan
- Department of Oral Medicine, Sedation and Imaging, Faculty of Dental Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Oral and Maxillofacial Surgery, Rambam Health Care Campus, Haifa, Israel
| | - Tami Boharon
- Department of Software Engineering, The Jerusalem College of Technology, Jerusalem, Israel
| | - Avital Itzhak-Pur
- Department of Software Engineering, The Jerusalem College of Technology, Jerusalem, Israel
| | - Asher Alexander
- Department of Software Engineering, The Jerusalem College of Technology, Jerusalem, Israel
| | - Akhilanand Chaurasia
- Department of Oral Medicine and Radiology, King George's Medical University, Lucknow, India
| | - Adir Cohen
- Department of Oral and Maxillofacial Surgery, Faculty of Dental Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Jacob Sosna
- Department of Radiology, Faculty of Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Isaac Leichter
- Department of Applied Physics, The Jerusalem College of Technology, Jerusalem, Israel
- Department of Radiology, Faculty of Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Chen Nadler
- Department of Oral Medicine, Sedation and Imaging, Faculty of Dental Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel.
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22
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Orhan K, Aktuna Belgin C, Manulis D, Golitsyna M, Bayrak S, Aksoy S, Sanders A, Önder M, Ezhov M, Shamshiev M, Gusarev M, Shlenskii V. Determining the reliability of diagnosis and treatment using artificial intelligence software with panoramic radiographs. Imaging Sci Dent 2023; 53:199-208. [PMID: 37799743 PMCID: PMC10548159 DOI: 10.5624/isd.20230109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/07/2023] [Accepted: 07/10/2023] [Indexed: 10/07/2023] Open
Abstract
Purpose The objective of this study was to evaluate the accuracy and effectiveness of an artificial intelligence (AI) program in identifying dental conditions using panoramic radiographs (PRs), as well as to assess the appropriateness of its treatment recommendations. Material and Methods PRs from 100 patients (representing 4497 teeth) with known clinical examination findings were randomly selected from a university database. Three dentomaxillofacial radiologists and the Diagnocat AI software evaluated these PRs. The evaluations were focused on various dental conditions and treatments, including canal filling, caries, cast post and core, dental calculus, fillings, furcation lesions, implants, lack of interproximal tooth contact, open margins, overhangs, periapical lesions, periodontal bone loss, short fillings, voids in root fillings, overfillings, pontics, root fragments, impacted teeth, artificial crowns, missing teeth, and healthy teeth. Results The AI demonstrated almost perfect agreement (exceeding 0.81) in most of the assessments when compared to the ground truth. The sensitivity was very high (above 0.8) for the evaluation of healthy teeth, artificial crowns, dental calculus, missing teeth, fillings, lack of interproximal contact, periodontal bone loss, and implants. However, the sensitivity was low for the assessment of caries, periapical lesions, pontic voids in the root canal, and overhangs. Conclusion Despite the limitations of this study, the synthesized data suggest that AI-based decision support systems can serve as a valuable tool in detecting dental conditions, when used with PR for clinical dental applications.
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Affiliation(s)
- Kaan Orhan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
| | - Ceren Aktuna Belgin
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Hatay Mustafa Kemal University, Hatay, Turkey
| | | | | | - Seval Bayrak
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Abant İzzet Baysal University, Bolu, Turkey
| | - Secil Aksoy
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, Nicosia, Cyprus
| | | | - Merve Önder
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
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Bennasar C, García I, Gonzalez-Cid Y, Pérez F, Jiménez J. Second Opinion for Non-Surgical Root Canal Treatment Prognosis Using Machine Learning Models. Diagnostics (Basel) 2023; 13:2742. [PMID: 37685280 PMCID: PMC10487079 DOI: 10.3390/diagnostics13172742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 08/08/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
Although the association between risk factors and non-surgical root canal treatment (NSRCT) failure has been extensively studied, methods to predict the outcomes of NSRCT are in an early stage, and dentists currently make the treatment prognosis based mainly on their clinical experience. Since this involves different sources of error, we investigated the use of machine learning (ML) models as a second opinion to support the clinical decision on whether to perform NSRCT. We undertook a retrospective study of 119 confirmed and not previously treated Apical Periodontitis cases that received the same treatment by the same specialist. For each patient, we recorded the variables from a newly proposed data collection template and defined a binary outcome: Success if the lesion clears and failure otherwise. We conducted tests for detecting the association between the variables and the outcome and selected a set of variables as the initial inputs into four ML algorithms: Logistic Regression (LR), Random Forest (RF), Naive-Bayes (NB), and K Nearest Neighbors (KNN). According to our results, RF and KNN significantly improve (p-values < 0.05) the sensitivity and accuracy of the dentist's treatment prognosis. Taking our results as a proof of concept, we conclude that future randomized clinical trials are worth designing to test the clinical utility of ML models as a second opinion for NSRCT prognosis.
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Affiliation(s)
- Catalina Bennasar
- ADEMA, School of Dentistry, University of the Balearic Islands, 07122 Palma de Mallorca, Spain;
| | - Irene García
- Department of Mathematical Sciences and Informatics, University of the Balearic Islands, 07120 Palma de Mallorca, Spain; (I.G.); (Y.G.-C.)
| | - Yolanda Gonzalez-Cid
- Department of Mathematical Sciences and Informatics, University of the Balearic Islands, 07120 Palma de Mallorca, Spain; (I.G.); (Y.G.-C.)
| | - Francesc Pérez
- Dental Public Health Service, IB-Salut, Balearic Islands, 07003 Palma de Mallorca, Spain;
- TotIA Artificial Intelligence for Dentistry, 07006 Palma de Mallorca, Spain
| | - Juan Jiménez
- ADEMA, School of Dentistry, University of the Balearic Islands, 07122 Palma de Mallorca, Spain;
- TotIA Artificial Intelligence for Dentistry, 07006 Palma de Mallorca, Spain
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Sivari E, Senirkentli GB, Bostanci E, Guzel MS, Acici K, Asuroglu T. Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review. Diagnostics (Basel) 2023; 13:2512. [PMID: 37568875 PMCID: PMC10416832 DOI: 10.3390/diagnostics13152512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019-May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics.
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Affiliation(s)
- Esra Sivari
- Department of Computer Engineering, Cankiri Karatekin University, Cankiri 18100, Turkey
| | | | - Erkan Bostanci
- Department of Computer Engineering, Ankara University, Ankara 06830, Turkey
| | | | - Koray Acici
- Department of Artificial Intelligence and Data Engineering, Ankara University, Ankara 06830, Turkey
| | - Tunc Asuroglu
- Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
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Ileșan RR, Beyer M, Kunz C, Thieringer FM. Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software. Bioengineering (Basel) 2023; 10:604. [PMID: 37237673 PMCID: PMC10215609 DOI: 10.3390/bioengineering10050604] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
Medical image segmentation, whether semi-automatically or manually, is labor-intensive, subjective, and needs specialized personnel. The fully automated segmentation process recently gained importance due to its better design and understanding of CNNs. Considering this, we decided to develop our in-house segmentation software and compare it to the systems of established companies, an inexperienced user, and an expert as ground truth. The companies included in the study have a cloud-based option that performs accurately in clinical routine (dice similarity coefficient of 0.912 to 0.949) with an average segmentation time ranging from 3'54″ to 85'54″. Our in-house model achieved an accuracy of 94.24% compared to the best-performing software and had the shortest mean segmentation time of 2'03″. During the study, developing in-house segmentation software gave us a glimpse into the strenuous work that companies face when offering clinically relevant solutions. All the problems encountered were discussed with the companies and solved, so both parties benefited from this experience. In doing so, we demonstrated that fully automated segmentation needs further research and collaboration between academics and the private sector to achieve full acceptance in clinical routines.
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Affiliation(s)
- Robert R. Ileșan
- Department of Oral and Cranio-Maxillofacial Surgery, University Hospital Basel, 4031 Basel, Switzerland; (M.B.); (C.K.); (F.M.T.)
| | - Michel Beyer
- Department of Oral and Cranio-Maxillofacial Surgery, University Hospital Basel, 4031 Basel, Switzerland; (M.B.); (C.K.); (F.M.T.)
- Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland
| | - Christoph Kunz
- Department of Oral and Cranio-Maxillofacial Surgery, University Hospital Basel, 4031 Basel, Switzerland; (M.B.); (C.K.); (F.M.T.)
| | - Florian M. Thieringer
- Department of Oral and Cranio-Maxillofacial Surgery, University Hospital Basel, 4031 Basel, Switzerland; (M.B.); (C.K.); (F.M.T.)
- Medical Additive Manufacturing Research Group (Swiss MAM), Department of Biomedical Engineering, University of Basel, 4123 Allschwil, Switzerland
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Issa J, Jaber M, Rifai I, Mozdziak P, Kempisty B, Dyszkiewicz-Konwińska M. Diagnostic Test Accuracy of Artificial Intelligence in Detecting Periapical Periodontitis on Two-Dimensional Radiographs: A Retrospective Study and Literature Review. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59040768. [PMID: 37109726 PMCID: PMC10142688 DOI: 10.3390/medicina59040768] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 04/12/2023] [Accepted: 04/14/2023] [Indexed: 04/29/2023]
Abstract
This study aims to evaluate the diagnostic accuracy of artificial intelligence in detecting apical pathosis on periapical radiographs. A total of twenty anonymized periapical radiographs were retrieved from the database of Poznan University of Medical Sciences. These radiographs displayed a sequence of 60 visible teeth. The evaluation of the radiographs was conducted using two methods (manual and automatic), and the results obtained from each technique were afterward compared. For the ground-truth method, one oral and maxillofacial radiology expert with more than ten years of experience and one trainee in oral and maxillofacial radiology evaluated the radiographs by classifying teeth as healthy and unhealthy. A tooth was considered unhealthy when periapical periodontitis related to this tooth had been detected on the radiograph. At the same time, a tooth was classified as healthy when no periapical radiolucency was detected on the periapical radiographs. Then, the same radiographs were evaluated by artificial intelligence, Diagnocat (Diagnocat Ltd., San Francisco, CA, USA). Diagnocat (Diagnocat Ltd., San Francisco, CA, USA) correctly identified periapical lesions on periapical radiographs with a sensitivity of 92.30% and identified healthy teeth with a specificity of 97.87%. The recorded accuracy and F1 score were 96.66% and 0.92, respectively. The artificial intelligence algorithm misdiagnosed one unhealthy tooth (false negative) and over-diagnosed one healthy tooth (false positive) compared to the ground-truth results. Diagnocat (Diagnocat Ltd., San Francisco, CA, USA) showed an optimum accuracy for detecting periapical periodontitis on periapical radiographs. However, more research is needed to assess the diagnostic accuracy of artificial intelligence-based algorithms in dentistry.
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Affiliation(s)
- Julien Issa
- Department of Diagnostics, University of Medical Sciences, Bukowska 70, 60-812 Poznan, Poland
- Doctoral School, Poznań University of Medical Sciences, Bukowska 70, 60-812 Poznan, Poland
| | - Mouna Jaber
- Faculty of Dentistry, Poznan University of Medical Sciences, 60-812 Poznan, Poland
| | - Ismail Rifai
- Department of Restorative Dentistry and Endodontics, Universitat Internacional de Catalunya, Josep Trueta, s/n, 08195 Sant Cugat del Vallès, Spain
| | - Paul Mozdziak
- Prestage Department of Poultry Sciences, North Carolina State University, Raleigh, NC 27695, USA
- Physiology Graduate Faculty, North Carolina State University, Raleigh, NC 27695, USA
| | - Bartosz Kempisty
- Physiology Graduate Faculty, North Carolina State University, Raleigh, NC 27695, USA
- Division of Anatomy, Department of Human Morphology and Embryology, Wroclaw Medical University, Chalubinskiego 6a, 50-368 Wroclaw, Poland
- Department of Veterinary Surgery, Institute of Veterinary Medicine, Nicolaus Copernicus University in Torun, Gagarina 7, 87-100 Torun, Poland
- Center of Assisted Reproduction, Department of Obstetrics and Gynaecology, University Hospital and Masaryk University, Jihlavska 20, 62500 Brno, Czech Republic
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Vodanović M, Subašić M, Milošević D, Savić Pavičin I. Artificial Intelligence in Medicine and Dentistry. Acta Stomatol Croat 2023; 57:70-84. [PMID: 37288152 PMCID: PMC10243707 DOI: 10.15644/asc57/1/8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/01/2023] [Indexed: 09/14/2023] Open
Abstract
INTRODUCTION Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. OBJECTIVE Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. CONCLUSION The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.
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Affiliation(s)
- Marin Vodanović
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
| | - Marko Subašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Denis Milošević
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Ivana Savić Pavičin
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
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Evans LM, Sözümert E, Keenan BE, Wood CE, du Plessis A. A Review of Image-Based Simulation Applications in High-Value Manufacturing. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:1495-1552. [PMID: 36685137 PMCID: PMC9847465 DOI: 10.1007/s11831-022-09836-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 10/15/2022] [Indexed: 06/17/2023]
Abstract
Image-Based Simulation (IBSim) is the process by which a digital representation of a real geometry is generated from image data for the purpose of performing a simulation with greater accuracy than with idealised Computer Aided Design (CAD) based simulations. Whilst IBSim originates in the biomedical field, the wider adoption of imaging for non-destructive testing and evaluation (NDT/NDE) within the High-Value Manufacturing (HVM) sector has allowed wider use of IBSim in recent years. IBSim is invaluable in scenarios where there exists a non-negligible variation between the 'as designed' and 'as manufactured' state of parts. It has also been used for characterisation of geometries too complex to accurately draw with CAD. IBSim simulations are unique to the geometry being imaged, therefore it is possible to perform part-specific virtual testing within batches of manufactured parts. This novel review presents the applications of IBSim within HVM, whereby HVM is the value provided by a manufactured part (or conversely the potential cost should the part fail) rather than the actual cost of manufacturing the part itself. Examples include fibre and aggregate composite materials, additive manufacturing, foams, and interface bonding such as welding. This review is divided into the following sections: Material Characterisation; Characterisation of Manufacturing Techniques; Impact of Deviations from Idealised Design Geometry on Product Design and Performance; Customisation and Personalisation of Products; IBSim in Biomimicry. Finally, conclusions are drawn, and observations made on future trends based on the current state of the literature.
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Affiliation(s)
- Llion Marc Evans
- Faculty of Science and Engineering, Swansea University, Swansea, SA1 8EN UK
- United Kingdom Atomic Energy Authority, Culham Science Centre, Abingdon, Oxfordshire OX14 3DB UK
| | - Emrah Sözümert
- Faculty of Science and Engineering, Swansea University, Swansea, SA1 8EN UK
| | - Bethany E. Keenan
- Cardiff School of Engineering, Cardiff University, Cardiff, CF24 3AA UK
| | - Charles E. Wood
- School of Mechanical & Design Engineering, University of Portsmouth, Portsmouth, PO1 3DJ UK
| | - Anton du Plessis
- Object Research Systems, Montreal, H3B 1A7 Canada
- Research Group 3DInnovation, Stellenbosch University, Stellenbosch, 7602 South Africa
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Hung KF, Yeung AWK, Bornstein MM, Schwendicke F. Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging. Dentomaxillofac Radiol 2023; 52:20220335. [PMID: 36472627 PMCID: PMC9793453 DOI: 10.1259/dmfr.20220335] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/08/2022] [Accepted: 11/11/2022] [Indexed: 12/12/2022] Open
Abstract
Personalized medicine refers to the tailoring of diagnostics and therapeutics to individuals based on one's biological, social, and behavioral characteristics. While personalized dental medicine is still far from being a reality, advanced artificial intelligence (AI) technologies with improved data analytic approaches are expected to integrate diverse data from the individual, setting, and system levels, which may facilitate a deeper understanding of the interaction of these multilevel data and therefore bring us closer to more personalized, predictive, preventive, and participatory dentistry, also known as P4 dentistry. In the field of dentomaxillofacial imaging, a wide range of AI applications, including several commercially available software options, have been proposed to assist dentists in the diagnosis and treatment planning of various dentomaxillofacial diseases, with performance similar or even superior to that of specialists. Notably, the impact of these dental AI applications on treatment decision, clinical and patient-reported outcomes, and cost-effectiveness has so far been assessed sparsely. Such information should be further investigated in future studies to provide patients, providers, and healthcare organizers a clearer picture of the true usefulness of AI in daily dental practice.
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Affiliation(s)
- Kuo Feng Hung
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Andy Wai Kan Yeung
- Division of Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Michael M. Bornstein
- Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Berlin, Germany
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Hung KF, Ai QYH, Wong LM, Yeung AWK, Li DTS, Leung YY. Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases. Diagnostics (Basel) 2022; 13:110. [PMID: 36611402 PMCID: PMC9818323 DOI: 10.3390/diagnostics13010110] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/23/2022] [Accepted: 12/24/2022] [Indexed: 12/31/2022] Open
Abstract
The increasing use of computed tomography (CT) and cone beam computed tomography (CBCT) in oral and maxillofacial imaging has driven the development of deep learning and radiomics applications to assist clinicians in early diagnosis, accurate prognosis prediction, and efficient treatment planning of maxillofacial diseases. This narrative review aimed to provide an up-to-date overview of the current applications of deep learning and radiomics on CT and CBCT for the diagnosis and management of maxillofacial diseases. Based on current evidence, a wide range of deep learning models on CT/CBCT images have been developed for automatic diagnosis, segmentation, and classification of jaw cysts and tumors, cervical lymph node metastasis, salivary gland diseases, temporomandibular (TMJ) disorders, maxillary sinus pathologies, mandibular fractures, and dentomaxillofacial deformities, while CT-/CBCT-derived radiomics applications mainly focused on occult lymph node metastasis in patients with oral cancer, malignant salivary gland tumors, and TMJ osteoarthritis. Most of these models showed high performance, and some of them even outperformed human experts. The models with performance on par with human experts have the potential to serve as clinically practicable tools to achieve the earliest possible diagnosis and treatment, leading to a more precise and personalized approach for the management of maxillofacial diseases. Challenges and issues, including the lack of the generalizability and explainability of deep learning models and the uncertainty in the reproducibility and stability of radiomic features, should be overcome to gain the trust of patients, providers, and healthcare organizers for daily clinical use of these models.
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Affiliation(s)
- Kuo Feng Hung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Qi Yong H. Ai
- Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lun M. Wong
- Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Dion Tik Shun Li
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Yiu Yan Leung
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
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Kim C, Jeong H, Park W, Kim D. Tooth-Related Disease Detection System Based on Panoramic Images and Optimization Through Automation: Development Study. JMIR Med Inform 2022; 10:e38640. [DOI: 10.2196/38640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/11/2022] [Accepted: 08/11/2022] [Indexed: 11/07/2022] Open
Abstract
Background
Early detection of tooth-related diseases in patients plays a key role in maintaining their dental health and preventing future complications. Since dentists are not overly attentive to tooth-related diseases that may be difficult to judge visually, many patients miss timely treatment. The 5 representative tooth-related diseases, that is, coronal caries or defect, proximal caries, cervical caries or abrasion, periapical radiolucency, and residual root can be detected on panoramic images. In this study, a web service was constructed for the detection of these diseases on panoramic images in real time, which helped shorten the treatment planning time and reduce the probability of misdiagnosis.
Objective
This study designed a model to assess tooth-related diseases in panoramic images by using artificial intelligence in real time. This model can perform an auxiliary role in the diagnosis of tooth-related diseases by dentists and reduce the treatment planning time spent through telemedicine.
Methods
For learning the 5 tooth-related diseases, 10,000 panoramic images were modeled: 4206 coronal caries or defects, 4478 proximal caries, 6920 cervical caries or abrasion, 8290 periapical radiolucencies, and 1446 residual roots. To learn the model, the fast region-based convolutional network (Fast R-CNN), residual neural network (ResNet), and inception models were used. Learning about the 5 tooth-related diseases completely did not provide accurate information on the diseases because of indistinct features present in the panoramic pictures. Therefore, 1 detection model was applied to each tooth-related disease, and the models for each of the diseases were integrated to increase accuracy.
Results
The Fast R-CNN model showed the highest accuracy, with an accuracy of over 90%, in diagnosing the 5 tooth-related diseases. Thus, Fast R-CNN was selected as the final judgment model as it facilitated the real-time diagnosis of dental diseases that are difficult to judge visually from radiographs and images, thereby assisting the dentists in their treatment plans.
Conclusions
The Fast R-CNN model showed the highest accuracy in the real-time diagnosis of dental diseases and can therefore play an auxiliary role in shortening the treatment planning time after the dentists diagnose the tooth-related disease. In addition, by updating the captured panoramic images of patients on the web service developed in this study, we are looking forward to increasing the accuracy of diagnosing these 5 tooth-related diseases. The dental diagnosis system in this study takes 2 minutes for diagnosing 5 diseases in 1 panoramic image. Therefore, this system plays an effective role in setting a dental treatment schedule.
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Thurzo A, Strunga M, Havlínová R, Reháková K, Urban R, Surovková J, Kurilová V. Smartphone-Based Facial Scanning as a Viable Tool for Facially Driven Orthodontics? SENSORS (BASEL, SWITZERLAND) 2022; 22:s22207752. [PMID: 36298103 PMCID: PMC9607180 DOI: 10.3390/s22207752] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 10/05/2022] [Accepted: 10/11/2022] [Indexed: 05/28/2023]
Abstract
The current paradigm shift in orthodontic treatment planning is based on facially driven diagnostics. This requires an affordable, convenient, and non-invasive solution for face scanning. Therefore, utilization of smartphones' TrueDepth sensors is very tempting. TrueDepth refers to front-facing cameras with a dot projector in Apple devices that provide real-time depth data in addition to visual information. There are several applications that tout themselves as accurate solutions for 3D scanning of the face in dentistry. Their clinical accuracy has been uncertain. This study focuses on evaluating the accuracy of the Bellus3D Dental Pro app, which uses Apple's TrueDepth sensor. The app reconstructs a virtual, high-resolution version of the face, which is available for download as a 3D object. In this paper, sixty TrueDepth scans of the face were compared to sixty corresponding facial surfaces segmented from CBCT. Difference maps were created for each pair and evaluated in specific facial regions. The results confirmed statistically significant differences in some facial regions with amplitudes greater than 3 mm, suggesting that current technology has limited applicability for clinical use. The clinical utilization of facial scanning for orthodontic evaluation, which does not require accuracy in the lip region below 3 mm, can be considered.
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Affiliation(s)
- Andrej Thurzo
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Martin Strunga
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Romana Havlínová
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Katarína Reháková
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Renata Urban
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Jana Surovková
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia
| | - Veronika Kurilová
- Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovičova 3, 81219 Bratislava, Slovakia
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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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Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review. Healthcare (Basel) 2022; 10:healthcare10101892. [PMID: 36292339 PMCID: PMC9602147 DOI: 10.3390/healthcare10101892] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/30/2022] [Accepted: 08/31/2022] [Indexed: 12/04/2022] Open
Abstract
Within the ever-growing healthcare industry, dental informatics is a burgeoning field of study. One of the major obstacles to the health care system’s transformation is obtaining knowledge and insightful data from complex, high-dimensional, and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, heterogeneous, poorly documented, and generally unstructured electronic health records, imaging, sensor data, and text. There were still certain restrictions even after many current techniques were used to extract more robust and useful elements from the data for analysis. New effective paradigms for building end-to-end learning models from complex data are provided by the most recent deep learning technology breakthroughs. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for dental informatics problems and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to some drawbacks and the need for better technique development and provide new perspectives about this exciting new development in the field.
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Calazans MAA, Ferreira FABS, Alcoforado MDLMG, dos Santos A, Pontual ADA, Madeiro F. Automatic Classification System for Periapical Lesions in Cone-Beam Computed Tomography. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22176481. [PMID: 36080940 PMCID: PMC9459969 DOI: 10.3390/s22176481] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 08/18/2022] [Accepted: 08/24/2022] [Indexed: 05/29/2023]
Abstract
Imaging examinations are of remarkable importance for diagnostic support in Dentistry. Imaging techniques allow analysis of dental and maxillofacial tissues (e.g., bone, dentine, and enamel) that are inaccessible through clinical examination, which aids in the diagnosis of diseases as well as treatment planning. The analysis of imaging exams is not trivial; so, it is usually performed by oral and maxillofacial radiologists. The increasing demand for imaging examinations motivates the development of an automatic classification system for diagnostic support, as proposed in this paper, in which we aim to classify teeth as healthy or with endodontic lesion. The classification system was developed based on a Siamese Network combined with the use of convolutional neural networks with transfer learning for VGG-16 and DenseNet-121 networks. For this purpose, a database with 1000 sagittal and coronal sections of cone-beam CT scans was used. The results in terms of accuracy, recall, precision, specificity, and F1-score show that the proposed system has a satisfactory classification performance. The innovative automatic classification system led to an accuracy of about 70%. The work is pioneer since, to the authors knowledge, no other previous work has used a Siamese Network for the purpose of classifying teeth as healthy or with endodontic lesion, based on cone-beam computed tomography images.
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Affiliation(s)
| | - Felipe Alberto B. S. Ferreira
- Unidade Acadêmica do Cabo de Santo Agostinho, Universidade Federal Rural de Pernambuco (UFRPE), Cabo de Santo Agostinho 54518-430, Brazil
| | | | - Andrezza dos Santos
- Departamento de Clínica e Odontologia Preventiva, Universidade Federal de Pernambuco (UFPE), Recife 50670-420, Brazil
| | - Andréa dos Anjos Pontual
- Departamento de Clínica e Odontologia Preventiva, Universidade Federal de Pernambuco (UFPE), Recife 50670-420, Brazil
| | - Francisco Madeiro
- Escola Politécnica de Pernambuco, Universidade de Pernambuco (UPE), Recife 50720-001, Brazil
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Okazaki S, Mine Y, Iwamoto Y, Urabe S, Mitsuhata C, Nomura R, Kakimoto N, Murayama T. Analysis of the feasibility of using deep learning for multiclass classification of dental anomalies on panoramic radiographs. Dent Mater J 2022; 41:889-895. [PMID: 36002296 DOI: 10.4012/dmj.2022-098] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The aim of the feasibility study was to construct deep learning models for the classification of multiple dental anomalies in panoramic radiographs. Panoramic radiographs with single supernumerary teeth and/or odontomas were considered the "case" group; panoramic radiographs with no dental anomalies were considered the "control" group. The dataset comprised 150 panoramic radiographs: 50 each of no dental anomalies, single supernumerary teeth, and odontomas. To classify the panoramic radiographs into case and control categories, we employed AlexNet, which is a convolutional neural network model. AlexNet was able to classify whole panoramic radiographs into two or three classes, according to the presence or absence of supernumerary teeth or odontomas. The performance metrics of the three-class classification were 70%, 70.8%, 70%, and 69.7% for accuracy, precision, sensitivity, and F1 score, respectively, in the macro average. These results support the feasibility of using deep learning to detect multiple dental anomalies in panoramic radiographs.
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Affiliation(s)
- Shota Okazaki
- Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Yuichi Mine
- Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Yuko Iwamoto
- Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Shiho Urabe
- Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Chieko Mitsuhata
- Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Ryota Nomura
- Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Naoya Kakimoto
- Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Takeshi Murayama
- Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
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Mladenovic R, Milosavljevic M, Stanisic D, Vasovic M. Importance of Artificial Intelligence in the analysis of children's CBCT imaging by dental students. J Dent Educ 2022. [PMID: 35848735 DOI: 10.1002/jdd.13060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/01/2022] [Accepted: 07/15/2022] [Indexed: 11/09/2022]
Affiliation(s)
- Rasa Mladenovic
- Department of Dentistry, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Marko Milosavljevic
- Department of Dentistry, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Dragana Stanisic
- Department of Dentistry, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Miroslav Vasovic
- Department of Dentistry, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
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Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis. Healthcare (Basel) 2022; 10:healthcare10071269. [PMID: 35885796 PMCID: PMC9320442 DOI: 10.3390/healthcare10071269] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/25/2022] [Accepted: 06/30/2022] [Indexed: 12/29/2022] Open
Abstract
This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.
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Thurzo A, Jančovičová V, Hain M, Thurzo M, Novák B, Kosnáčová H, Lehotská V, Varga I, Kováč P, Moravanský N. Human Remains Identification Using Micro-CT, Chemometric and AI Methods in Forensic Experimental Reconstruction of Dental Patterns after Concentrated Sulphuric Acid Significant Impact. Molecules 2022; 27:molecules27134035. [PMID: 35807281 PMCID: PMC9268125 DOI: 10.3390/molecules27134035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 06/21/2022] [Accepted: 06/21/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Teeth, in humans, represent the most resilient tissues. However, exposure to concentrated acids might lead to their dissolving, thus making human identification difficult. Teeth often contain dental restorations from materials that are even more resilient to acid impact. This paper aims to introduce a novel method for the 3D reconstruction of dental patterns as a crucial step for the digital identification of dental records. (2) With a combination of modern methods, including micro-computed tomography, cone-beam computer tomography, and attenuated total reflection, in conjunction with Fourier transform infrared spectroscopy and artificial intelligence convolutional neural network algorithms, this paper presents a method for 3D-dental-pattern reconstruction, and human remains identification. Our research studies the morphology of teeth, bone, and dental materials (amalgam, composite, glass-ionomer cement) under different periods of exposure to 75% sulfuric acid. (3) Our results reveal a significant volume loss in bone, enamel, dentine, as well as glass-ionomer cement. The results also reveal a significant resistance by the composite and amalgam dental materials to the impact of sulfuric acid, thus serving as strong parts in the dental-pattern mosaic. This paper also probably introduces the first successful artificial intelligence application in automated-forensic-CBCT segmentation. (4) Interdisciplinary cooperation, utilizing the mentioned technologies, can solve the problem of human remains identification with a 3D reconstruction of dental patterns and their 2D projections over existing ante-mortem records.
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Affiliation(s)
- Andrej Thurzo
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia;
- Institute of Forensic Medical Expertise, Expert institute, Boženy Němcovej 8, 81104 Bratislava, Slovakia;
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University in Bratislava, Sasinkova 4, 81272 Bratislava, Slovakia;
- Correspondence: (A.T.); (N.M.)
| | - Viera Jančovičová
- Department of Graphic Arts Technology and Applied Photochemistry, Institute of Natural and Synthetic Polymers, Faculty of Chemical and Food Technology, Slovak University of Technology in Bratislava, Radlinského 9, 81237 Bratislava, Slovakia;
| | - Miroslav Hain
- Institute of Measurement Science, Slovak Academy of Sciences, Dúbravská Cesta 9, 84104 Bratislava, Slovakia;
| | - Milan Thurzo
- Department of Anthropology, Faculty of Natural Sciences, Comenius University in Bratislava, 84215 Bratislava, Slovakia;
| | - Bohuslav Novák
- Department of Stomatology and Maxillofacial Surgery, Faculty of Medicine, Comenius University in Bratislava, 81250 Bratislava, Slovakia;
| | - Helena Kosnáčová
- Department of Simulation and Virtual Medical Education, Faculty of Medicine, Comenius University in Bratislava, Sasinkova 4, 81272 Bratislava, Slovakia;
- Department of Genetics, Cancer Research Institute, Biomedical Research Center, Slovak Academy of Sciences, Dubravska Cesta 9, 84505 Bratislava, Slovakia
| | - Viera Lehotská
- 2nd Department of Radiology, Faculty of Medicine, Comenius University in Bratislava, Heydukova 10, 81250 Bratislava, Slovakia;
| | - Ivan Varga
- Institute of Histology and Embryology, Faculty of Medicine, Comenius University in Bratislava, 81372 Bratislava, Slovakia;
| | - Peter Kováč
- Institute of Forensic Medical Expertise, Expert institute, Boženy Němcovej 8, 81104 Bratislava, Slovakia;
| | - Norbert Moravanský
- Institute of Forensic Medical Expertise, Expert institute, Boženy Němcovej 8, 81104 Bratislava, Slovakia;
- Institute of Forensic Medicine, Faculty of Medicine Comenius University in Bratislava, Sasinkova 4, 81108 Bratislava, Slovakia
- Correspondence: (A.T.); (N.M.)
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Lahoud P, Jacobs R, Boisse P, EzEldeen M, Ducret M, Richert R. Precision medicine using patient-specific modelling: state of the art and perspectives in dental practice. Clin Oral Investig 2022; 26:5117-5128. [PMID: 35687196 DOI: 10.1007/s00784-022-04572-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 05/30/2022] [Indexed: 12/25/2022]
Abstract
The dental practice has largely evolved in the last 50 years following a better understanding of the biomechanical behaviour of teeth and its supporting structures, as well as developments in the fields of imaging and biomaterials. However, many patients still encounter treatment failures; this is related to the complex nature of evaluating the biomechanical aspects of each clinical situation due to the numerous patient-specific parameters, such as occlusion and root anatomy. In parallel, the advent of cone beam computed tomography enabled researchers in the field of odontology as well as clinicians to gather and model patient data with sufficient accuracy using image processing and finite element technologies. These developments gave rise to a new precision medicine concept that proposes to individually assess anatomical and biomechanical characteristics and adapt treatment options accordingly. While this approach is already applied in maxillofacial surgery, its implementation in dentistry is still restricted. However, recent advancements in artificial intelligence make it possible to automate several parts of the laborious modelling task, bringing such user-assisted decision-support tools closer to both clinicians and researchers. Therefore, the present narrative review aimed to present and discuss the current literature investigating patient-specific modelling in dentistry, its state-of-the-art applications, and research perspectives.
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Affiliation(s)
- Pierre Lahoud
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU, Leuven, Belgium.,Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium.,Periodontology and Oral Microbiology, Department of Oral Health Sciences, KU Leuven, Leuven, Belgium
| | - Reinhilde Jacobs
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU, Leuven, Belgium.,Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium.,Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden
| | - Philippe Boisse
- Laboratoire de Mécanique Des Contacts Et Structures, UMR 5259, CNRS/INSA, Villeurbanne, France
| | - Mostafa EzEldeen
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU, Leuven, Belgium.,Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium.,Department of Oral Health Sciences, KU Leuven and Paediatric Dentistry and Special Dental Care, University Hospitals Leuven, Leuven, Belgium
| | - Maxime Ducret
- Hospices Civils de Lyon, PAM d'Odontologie, Lyon, France.,Faculty of Odontology, Lyon 1 University, Lyon, France.,Laboratoire de Biologie Tissulaire Et Ingénierie Thérapeutique, UMR5305 CNRS/UCBL, Lyon, France
| | - Raphael Richert
- Laboratoire de Mécanique Des Contacts Et Structures, UMR 5259, CNRS/INSA, Villeurbanne, France. .,Hospices Civils de Lyon, PAM d'Odontologie, Lyon, France. .,Faculty of Odontology, Lyon 1 University, Lyon, France.
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Dental Undergraduates and Interns’ Awareness, Attitudes, and Perception of Radiological Protection. Int J Dent 2022; 2022:5812627. [PMID: 35585962 PMCID: PMC9110231 DOI: 10.1155/2022/5812627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 04/11/2022] [Accepted: 04/25/2022] [Indexed: 11/18/2022] Open
Abstract
Medical ionizing radiation is widely used in hospitals, in particular dental clinics, and in medical research to facilitate the diagnosis and treatment of patients. The awareness, attitude, and perception of ionizing radiation exposure among dental undergraduate students and interns in radiological investigations and dental care clinics were investigated. A cross-sectional study was conducted; 17 questions were designed online using the software “QuestionPro,” which was licensed to the University of Imam Abdulrahman Bin Faisal. Participants included senior medical dental students from Imam Abdulrahman Bin Faisal University in their third to fifth years, as well as interns from King Fahad University Hospital and private dental care clinics. A total of 855 participants viewed, 360 started the questionnaire, and 258 (72%) completed it online. Overall, knowledge was lacking; 32% of respondents incorrectly believed that magnetic resonance imaging and ultrasound used ionizing radiation, while 38% were unsure. Dental X-rays were deemed harmful by 40% (n = 104) of respondents. According to 33% (n = 85) of participants, there is no radiation scatter during an X-ray or CT scan, while 30% (n = 76) are unsure. Respondents (44%; n = 104) were unaware of the radiation dose from a chest radiograph and (45%; n = 116) overestimated the radiation dose. The effects of ionizing radiation on healthy tissue are known to more than half of the participants (54%). According to 39% of respondents, digital radiography exposes them to less radiation than traditional radiography. In terms of radiation protection and hazard, 46% said personal monitoring badges should be always worn and 58% (n = 150) said lead aprons should be used on a regular basis. 63% of the subjects had received radiation protection education, such as formal lectures, tutorials, or workshops, while 37% (n = 95) had not. 53% of the respondents were not aware of the international recommendations from the International Commission on Radiological Protection. When asked if they would follow radiation protection protocols if they opened a private dental clinical practice in the future, 50% (n = 129) said they would.
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Potential and impact of artificial intelligence algorithms in dento-maxillofacial radiology. Clin Oral Investig 2022; 26:5535-5555. [PMID: 35438326 DOI: 10.1007/s00784-022-04477-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 03/25/2022] [Indexed: 12/20/2022]
Abstract
OBJECTIVES Novel artificial intelligence (AI) learning algorithms in dento-maxillofacial radiology (DMFR) are continuously being developed and improved using advanced convolutional neural networks. This review provides an overview of the potential and impact of AI algorithms in DMFR. MATERIALS AND METHODS A narrative review was conducted on the literature on AI algorithms in DMFR. RESULTS In the field of DMFR, AI algorithms were mainly proposed for (1) automated detection of dental caries, periapical pathologies, root fracture, periodontal/peri-implant bone loss, and maxillofacial cysts/tumors; (2) classification of mandibular third molars, skeletal malocclusion, and dental implant systems; (3) localization of cephalometric landmarks; and (4) improvement of image quality. Data insufficiency, overfitting, and the lack of interpretability are the main issues in the development and use of image-based AI algorithms. Several strategies have been suggested to address these issues, such as data augmentation, transfer learning, semi-supervised training, few-shot learning, and gradient-weighted class activation mapping. CONCLUSIONS Further integration of relevant AI algorithms into one fully automatic end-to-end intelligent system for possible multi-disciplinary applications is very likely to be a field of increased interest in the future. CLINICAL RELEVANCE This review provides dental practitioners and researchers with a comprehensive understanding of the current development, performance, issues, and prospects of image-based AI algorithms in DMFR.
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Baniulyte G, Ali K. Artificial intelligence - can it be used to outsmart oral cancer? Evid Based Dent 2022; 23:12-13. [PMID: 35338317 DOI: 10.1038/s41432-022-0238-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 02/07/2022] [Indexed: 11/10/2022]
Abstract
Data Sources Electronic search on PubMed, Cochrane, Scopus, Embase, Google Scholar, Saudi Digital Library and Web of Science, and hand searching carried out for studies published January 2000-March 2021. Language was restricted to English.Study selection Original research studies involving artificial intelligence technology for oral cancer diagnosis and prognosis prediction were considered. The studies had to provide quantitative data of their evaluation analysis. The exclusion criteria were reported. No limit was set on study design.Data extraction and synthesis The initial search yielded 628 articles. Following deduplication, 340 full-text articles were screened. QUADAS-2 tool was used to assess the quality of the included studies regarding diagnostic accuracy.Results A total of 16 studies were included with various study designs: 14 cross-sectional, one cohort and one retrospective study. Six studies reviewed the diagnosis aspect. All studies indicate an overall positive trend of artificial intelligence technology.Conclusions Artificial intelligence appears to have good accuracy in oral cancer diagnosis and its prediction.
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Affiliation(s)
- G Baniulyte
- Academic Clinical Fellow in Oral Surgery, Oral and Maxillofacial Department, Royal Devon and Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK
| | - K Ali
- Qatar University, College of Dental Medicine, QU Health, Doha 2713, Qatar
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Rashid U, Javid A, Khan AR, Liu L, Ahmed A, Khalid O, Saleem K, Meraj S, Iqbal U, Nawaz R. A hybrid mask RCNN-based tool to localize dental cavities from real-time mixed photographic images. PeerJ Comput Sci 2022; 8:e888. [PMID: 35494840 PMCID: PMC9044255 DOI: 10.7717/peerj-cs.888] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
Nearly 3.5 billion humans have oral health issues, including dental caries, which requires dentist-patient exposure in oral examinations. The automated approaches identify and locate carious regions from dental images by localizing and processing either colored photographs or X-ray images taken via specialized dental photography cameras. The dentists' interpretation of carious regions is difficult since the detected regions are masked using solid coloring and limited to a particular dental image type. The software-based automated tools to localize caries from dental images taken via ordinary cameras requires further investigation. This research provided a mixed dataset of dental photographic (colored or X-ray) images, instantiated a deep learning approach to enhance the existing dental image carious regions' localization procedure, and implemented a full-fledged tool to present carious regions via simple dental images automatically. The instantiation mainly exploits the mixed dataset of dental images (colored photographs or X-rays) collected from multiple sources and pre-trained hybrid Mask RCNN to localize dental carious regions. The evaluations performed by the dentists showed that the correctness of annotated datasets is up to 96%, and the accuracy of the proposed system is between 78% and 92%. Moreover, the system achieved the overall satisfaction level of dentists above 80%.
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Affiliation(s)
- Umer Rashid
- Department of Computer Science, Quaid-e-Azam University, Islamabad, Pakistan
| | - Aiman Javid
- Department of Computer Science, Quaid-e-Azam University, Islamabad, Pakistan
| | - Abdur Rehman Khan
- Department of Computer Science, Quaid-e-Azam University, Islamabad, Pakistan
| | - Leo Liu
- School of Business and Law, The Manchester Metropolitan University, Manchester, United Kingdom
| | - Adeel Ahmed
- Department of Computer Science, Quaid-e-Azam University, Islamabad, Pakistan
| | - Osman Khalid
- Department of Computer Science, COMSATS University, Islamabad, Pakistan
| | - Khalid Saleem
- Department of Computer Science, Quaid-e-Azam University, Islamabad, Pakistan
| | - Shaista Meraj
- Department of Radiology, Bolton NHS Foundation Trust, Bolton, United Kingdom
| | - Uzair Iqbal
- Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad Chiniot-Faisalabad, Pakistan
| | - Raheel Nawaz
- School of Business and Law, The Manchester Metropolitan University, Manchester, United Kingdom
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Artificial Intelligence Application in Assessment of Panoramic Radiographs. Diagnostics (Basel) 2022; 12:diagnostics12010224. [PMID: 35054390 PMCID: PMC8774336 DOI: 10.3390/diagnostics12010224] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/12/2022] [Accepted: 01/14/2022] [Indexed: 12/04/2022] Open
Abstract
The aim of this study was to assess the reliability of the artificial intelligence (AI) automatic evaluation of panoramic radiographs (PRs). Thirty PRs, covering at least six teeth with the possibility of assessing the marginal and apical periodontium, were uploaded to the Diagnocat (LLC Diagnocat, Moscow, Russia) account, and the radiologic report of each was generated as the basis of automatic evaluation. The same PRs were manually evaluated by three independent evaluators with 12, 15, and 28 years of experience in dentistry, respectively. The data were collected in such a way as to allow statistical analysis with SPSS Statistics software (IBM, Armonk, NY, USA). A total of 90 reports were created for 30 PRs. The AI protocol showed very high specificity (above 0.9) in all assessments compared to ground truth except from periodontal bone loss. Statistical analysis showed a high interclass correlation coefficient (ICC > 0.75) for all interevaluator assessments, proving the good credibility of the ground truth and the reproducibility of the reports. Unacceptable reliability was obtained for caries assessment (ICC = 0.681) and periapical lesions assessment (ICC = 0.619). The tested AI system can be helpful as an initial evaluation of screening PRs, giving appropriate credibility reports and suggesting additional diagnostic methods for more accurate evaluation if needed.
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Guided Insertion of Temporary Anchorage Device in Form of Orthodontic Titanium Miniscrews with Customized 3D Templates—A Systematic Review with Meta-Analysis of Clinical Studies. COATINGS 2021. [DOI: 10.3390/coatings11121488] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
(1) Background: Miniscrew insertion, using a surgical guide, aims to avoid possible adverse effects or complications. With the higher availability of both 3D imaging and printing, 3D surgical guides have been used more frequently in orthodontics. The aim of the present systematic review was to find scientific clinical evidence concerning the precision of the 3D guided insertion of miniscrews for temporary orthodontic anchorage. (2) Methods: Literature searches were performed in the following five search engines: Pubmed (Medline), Pubmed Central, Scopus, Web of Science and Embase on 10 September 2021 (articles from 1950 to 10 September 2021). A meta-analysis was performed using the random-effect model, with Standardized Mean Differences (SMD) and 95% confidence intervals (95% CI) calculated as effect estimates. The heterogeneity was assessed quantitatively. (3) Results: The search strategy identified 671 potential articles. After the removal of duplicates, 530 articles were analyzed. Subsequently, 487 papers were excluded, because they were not associated with the subject of the study. Of the remaining 43 papers, 34 were excluded because they did not meet the methodological criteria. Finally, only nine papers were subjected to a qualitative analysis. (4) Conclusions: The current literature concerning guided miniscrew insertion reveals, for the most part, a low methodological level. High-quality clinical trials are in the minority. The use of surgical guides increases insertion accuracy, stability and reduces the failure rate of orthodontic miniscrews. Tooth-borne insertion guides supported on the edges of the teeth ensure a higher insertion precision compared to mucosa-borne ones. The study protocol was registered in PROSPERO under the number CRD42021267248.
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