1
|
Ammar N, Kühnisch J. Diagnostic performance of artificial intelligence-aided caries detection on bitewing radiographs: a systematic review and meta-analysis. JAPANESE DENTAL SCIENCE REVIEW 2024; 60:128-136. [PMID: 38450159 PMCID: PMC10917640 DOI: 10.1016/j.jdsr.2024.02.001] [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: 11/18/2023] [Revised: 02/02/2024] [Accepted: 02/19/2024] [Indexed: 03/08/2024] Open
Abstract
The accuracy of artificial intelligence-aided (AI) caries diagnosis can vary considerably depending on numerous factors. This review aimed to assess the diagnostic accuracy of AI models for caries detection and classification on bitewing radiographs. Publications after 2010 were screened in five databases. A customized risk of bias (RoB) assessment tool was developed and applied to the 14 articles that met the inclusion criteria out of 935 references. Dataset sizes ranged from 112 to 3686 radiographs. While 86 % of the studies reported a model with an accuracy of ≥80 %, most exhibited unclear or high risk of bias. Three studies compared the model's diagnostic performance to dentists, in which the models consistently showed higher average sensitivity. Five studies were included in a bivariate diagnostic random-effects meta-analysis for overall caries detection. The diagnostic odds ratio was 55.8 (95 % CI= 28.8 - 108.3), and the summary sensitivity and specificity were 0.87 (0.76 - 0.94) and 0.89 (0.75 - 0.960), respectively. Independent meta-analyses for dentin and enamel caries detection were conducted and showed sensitivities of 0.84 (0.80 - 0.87) and 0.71 (0.66 - 0.75), respectively. Despite the promising diagnostic performance of AI models, the lack of high-quality, adequately reported, and externally validated studies highlight current challenges and future research needs.
Collapse
Affiliation(s)
- Nour Ammar
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilian University of Munich, Munich 80336, Germany
- Department of Pediatric Dentistry and Dental Public Health, Faculty of Dentistry, Alexandria University, Alexandria 21257, Egypt
| | - Jan Kühnisch
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilian University of Munich, Munich 80336, Germany
| |
Collapse
|
2
|
Hartoonian S, Hosseini M, Yousefi I, Mahdian M, Ghazizadeh Ahsaie M. Applications of artificial intelligence in dentomaxillofacial imaging: a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 138:641-655. [PMID: 38637235 DOI: 10.1016/j.oooo.2023.12.790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/02/2023] [Accepted: 12/22/2023] [Indexed: 04/20/2024]
Abstract
BACKGROUND Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities. STUDY DESIGN A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another. RESULTS The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection. CONCLUSION Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.
Collapse
Affiliation(s)
- Serlie Hartoonian
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Matine Hosseini
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Iman Yousefi
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mina Mahdian
- Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Mitra Ghazizadeh Ahsaie
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| |
Collapse
|
3
|
Magat G, Altındag A, Pertek Hatipoglu F, Hatipoglu O, Bayrakdar İS, Celik O, Orhan K. Automatic deep learning detection of overhanging restorations in bitewing radiographs. Dentomaxillofac Radiol 2024; 53:468-477. [PMID: 39024043 PMCID: PMC11440037 DOI: 10.1093/dmfr/twae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/09/2024] [Accepted: 05/27/2024] [Indexed: 07/20/2024] Open
Abstract
OBJECTIVES This study aimed to assess the effectiveness of deep convolutional neural network (CNN) algorithms for the detecting and segmentation of overhanging dental restorations in bitewing radiographs. METHODS A total of 1160 anonymized bitewing radiographs were used to progress the artificial intelligence (AI) system for the detection and segmentation of overhanging restorations. The data were then divided into three groups: 80% for training (930 images, 2399 labels), 10% for validation (115 images, 273 labels), and 10% for testing (115 images, 306 labels). A CNN model known as You Only Look Once (YOLOv5) was trained to detect overhanging restorations in bitewing radiographs. After utilizing the remaining 115 radiographs to evaluate the efficacy of the proposed CNN model, the accuracy, sensitivity, precision, F1 score, and area under the receiver operating characteristic curve (AUC) were computed. RESULTS The model demonstrated a precision of 90.9%, a sensitivity of 85.3%, and an F1 score of 88.0%. Furthermore, the model achieved an AUC of 0.859 on the receiver operating characteristic (ROC) curve. The mean average precision (mAP) at an intersection over a union (IoU) threshold of 0.5 was notably high at 0.87. CONCLUSIONS The findings suggest that deep CNN algorithms are highly effective in the detection and diagnosis of overhanging dental restorations in bitewing radiographs. The high levels of precision, sensitivity, and F1 score, along with the significant AUC and mAP values, underscore the potential of these advanced deep learning techniques in revolutionizing dental diagnostic procedures.
Collapse
Affiliation(s)
- Guldane Magat
- Necmettin Erbakan University Dentistry Faculty, Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Konya, Meram, Turkey, 42090, Turkey
| | - Ali Altındag
- Necmettin Erbakan University Dentistry Faculty, Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Konya, Meram, Turkey, 42090, Turkey
| | | | - Omer Hatipoglu
- Department of Restorative Dentistry, Nigde Omer Halisdemir University, Nigde, 51240, Turkey
| | - İbrahim Sevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, 26040, Turkey
- Department of Mathematics-Computer, Eskisehir Osmangazi University Faculty of Science, Eskisehir, 26040, Turkey
- CranioCatch Company, Eskisehir, 26040, Turkey
| | - Ozer Celik
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, 26040, Turkey
- Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, 06800, Turkey
| | - Kaan Orhan
- Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, 06800, Turkey
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, 06500, Turkey
| |
Collapse
|
4
|
Al-Khalifa KS, Ahmed WM, Azhari AA, Qaw M, Alsheikh R, Alqudaihi F, Alfaraj A. The Use of Artificial Intelligence in Caries Detection: A Review. Bioengineering (Basel) 2024; 11:936. [PMID: 39329679 PMCID: PMC11428802 DOI: 10.3390/bioengineering11090936] [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: 07/07/2024] [Revised: 08/20/2024] [Accepted: 09/11/2024] [Indexed: 09/28/2024] Open
Abstract
Advancements in artificial intelligence (AI) have significantly impacted the field of dentistry, particularly in diagnostic imaging for caries detection. This review critically examines the current state of AI applications in caries detection, focusing on the performance and accuracy of various AI techniques. We evaluated 40 studies from the past 23 years, carefully selected for their relevance and quality. Our analysis highlights the potential of AI, especially convolutional neural networks (CNNs), to improve diagnostic accuracy and efficiency in detecting dental caries. The findings underscore the transformative potential of AI in clinical dental practice.
Collapse
Affiliation(s)
- Khalifa S. Al-Khalifa
- Department of Preventive Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Walaa Magdy Ahmed
- Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (W.M.A.); (A.A.A.)
| | - Amr Ahmed Azhari
- Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (W.M.A.); (A.A.A.)
| | - Masoumah Qaw
- Department of Restorative Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (M.Q.); (R.A.)
| | - Rasha Alsheikh
- Department of Restorative Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (M.Q.); (R.A.)
| | - Fatema Alqudaihi
- Department of Restorative Dentistry, Khobar Dental Complex, Eastern Health Cluster, Dammam 32253, Saudi Arabia;
| | - Amal Alfaraj
- Department of Prosthodontics and Dental Implantology, College of Dentistry, King Faisal University, Al-Ahsa 31982, Saudi Arabia;
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
Szabó V, Szabó BT, Orhan K, Veres DS, Manulis D, Ezhov M, Sanders A. Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs. J Dent 2024; 147:105105. [PMID: 38821394 DOI: 10.1016/j.jdent.2024.105105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 05/21/2024] [Accepted: 05/28/2024] [Indexed: 06/02/2024] Open
Abstract
OBJECTIVES This study aimed to assess the reliability of AI-based system that assists the healthcare processes in the diagnosis of caries on intraoral radiographs. METHODS The proximal surfaces of the 323 selected teeth on the intraoral radiographs were evaluated by two independent observers using an AI-based (Diagnocat) system. The presence or absence of carious lesions was recorded during Phase 1. After 4 months, the AI-aided human observers evaluated the same radiographs (Phase 2), and the advanced convolutional neural network (CNN) reassessed the radiographic data (Phase 3). Subsequently, data reflecting human disagreements were excluded (Phase 4). For each phase, the Cohen and Fleiss kappa values, as well as the sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy of Diagnocat, were calculated. RESULTS During the four phases, the range of Cohen kappa values between the human observers and Diagnocat were κ=0.66-1, κ=0.58-0.7, and κ=0.49-0.7. The Fleiss kappa values were κ=0.57-0.8. The sensitivity, specificity and diagnostic accuracy values ranged between 0.51-0.76, 0.88-0.97 and 0.76-0.86, respectively. CONCLUSIONS The Diagnocat CNN supports the evaluation of intraoral radiographs for caries diagnosis, as determined by consensus between human and AI system observers. CLINICAL SIGNIFICANCE Our study may aid in the understanding of deep learning-based systems developed for dental imaging modalities for dentists and contribute to expanding the body of results in the field of AI-supported dental radiology..
Collapse
Affiliation(s)
- Viktor Szabó
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, Budapest, Hungary
| | - Bence Tamás Szabó
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, Budapest, Hungary.
| | - Kaan Orhan
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, Budapest, Hungary; Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey; Medical Design Application, and Research Center (MEDITAM), Ankara University, Ankara, Turkey
| | - Dániel Sándor Veres
- Department of Biophysics and Radiation Biology, Semmelweis University, Budapest, Hungary
| | | | | | | |
Collapse
|
7
|
Liu Y, Xia K, Cen Y, Ying S, Zhao Z. Artificial intelligence for caries detection: a novel diagnostic tool using deep learning algorithms. Oral Radiol 2024; 40:375-384. [PMID: 38498223 DOI: 10.1007/s11282-024-00741-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 01/26/2024] [Indexed: 03/20/2024]
Abstract
OBJECTIVES The aim of this study was to develop an assessment tool for automatic detection of dental caries in periapical radiographs using convolutional neural network (CNN) architecture. METHODS A novel diagnostic model named ResNet + SAM was established using numerous periapical radiographs (4278 images) annotated by medical experts to automatically detect dental caries. The performance of the model was compared to the traditional CNNs (VGG19, ResNet-50), and the dentists. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique shows the region of interest in the image for the CNNs. RESULTS ResNet + SAM demonstrated significantly improved performance compared to the modified ResNet-50 model, with an average F1 score of 0.886 (95% CI 0.855-0.918), accuracy of 0.885 (95% CI 0.862-0.901) and AUC of 0.954 (95% CI 0.924-0.980). The comparison between the performance of the model and the dentists revealed that the model achieved higher accuracy than that of the junior dentists. With the assist of the tool, the dentists achieved superior metrics with a mean F1 score of 0.827 and the interobserver agreement for dental caries is enhanced from 0.592/0.610 to 0.706/0.723. CONCLUSIONS According to the results obtained from the experiments, the automatic assessment tool using the ResNet + SAM model shows remarkable performance and has excellent possibilities in identifying dental caries. The use of the assessment tool in clinical practice can be of great benefit as a clinical decision-making support in dentistry and reduce the workload of dentists.
Collapse
Affiliation(s)
- Yiliang Liu
- College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu, 610065, China
- State Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, 610064, Sichuan, China
| | - Kai Xia
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, No. 14, 3rd section, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Yueyan Cen
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, No. 14, 3rd section, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Sancong Ying
- College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu, 610065, China.
- State Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, 610064, Sichuan, China.
| | - Zhihe Zhao
- State Key Laboratory of Oral Diseases and National Clinical Research Center for Oral Diseases, Department of Orthodontics, West China Hospital of Stomatology, Sichuan University, No. 14, 3rd section, South Renmin Road, Chengdu, 610041, Sichuan, China
| |
Collapse
|
8
|
Dai F, Liu Q, Guo Y, Xie R, Wu J, Deng T, Zhu H, Deng L, Song L. Convolutional neural networks combined with classification algorithms for the diagnosis of periodontitis. Oral Radiol 2024; 40:357-366. [PMID: 38393548 DOI: 10.1007/s11282-024-00739-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 01/03/2024] [Indexed: 02/25/2024]
Abstract
OBJECTIVES We aim to develop a deep learning model based on a convolutional neural network (CNN) combined with a classification algorithm (CA) to assist dentists in quickly and accurately diagnosing the stage of periodontitis. MATERIALS AND METHODS Periapical radiographs (PERs) and clinical data were collected. The CNNs including Alexnet, VGG16, and ResNet18 were trained on PER to establish the PER-CNN models for no periodontal bone loss (PBL) and PBL. The CAs including random forest (RF), support vector machine (SVM), naive Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN) were added to the PER-CNN model for control, stage I, stage II and stage III/IV periodontitis. Heat map was produced using a gradient-weighted class activation mapping method to visualize the regions of interest of the PER-Alexnet model. Clustering analysis was performed based on the ten PER-CNN scores and the clinical characteristics. RESULTS The accuracy of the PER-Alexnet and PER-VGG16 models with the higher performance was 0.872 and 0.853, respectively. The accuracy of the PER-Alexnet + RF model with the highest performance for control, stage I, stage II and stage III/IV was 0.968, 0.960, 0.835 and 0.842, respectively. Heat map showed that the regions of interest predicted by the model were periodontitis bone lesions. We found that age and smoking were significantly related to periodontitis based on the PER-Alexnet scores. CONCLUSION The PER-Alexnet + RF model has reached high performance for whole-case periodontal diagnosis. The CNN models combined with CA can assist dentists in quickly and accurately diagnosing the stage of periodontitis.
Collapse
Affiliation(s)
- Fang Dai
- Center of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, Nanchang, 330000, Jiangxi, China
- The Institute of Periodontal Disease, Nanchang University, Nanchang, China
- JXHC Key Laboratory of Periodontology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Qiangdong Liu
- Center of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, Nanchang, 330000, Jiangxi, China
- The Second Clinical Medical School, Nanchang University, Nanchang, China
- The Institute of Periodontal Disease, Nanchang University, Nanchang, China
- JXHC Key Laboratory of Periodontology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yuchen Guo
- The Second Clinical Medical School, Nanchang University, Nanchang, China
| | - Ruixiang Xie
- School of Life Sciences, Nanchang University, Nanchang, China
| | - Jingting Wu
- Center of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, Nanchang, 330000, Jiangxi, China
- The Institute of Periodontal Disease, Nanchang University, Nanchang, China
- JXHC Key Laboratory of Periodontology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Tian Deng
- Center of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, Nanchang, 330000, Jiangxi, China
- The Institute of Periodontal Disease, Nanchang University, Nanchang, China
- JXHC Key Laboratory of Periodontology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Hongbiao Zhu
- Center of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, Nanchang, 330000, Jiangxi, China
- The Institute of Periodontal Disease, Nanchang University, Nanchang, China
- JXHC Key Laboratory of Periodontology, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Libin Deng
- School of Public Health, Nanchang University, No.1299, Xuefu Avenue, Nanchang, 330000, Jiangxi, China.
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, China.
- The Institute of Periodontal Disease, Nanchang University, Nanchang, China.
- JXHC Key Laboratory of Periodontology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
| | - Li Song
- Center of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, No.1, Minde Road, Nanchang, 330000, Jiangxi, China.
- The Institute of Periodontal Disease, Nanchang University, Nanchang, China.
- JXHC Key Laboratory of Periodontology, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
| |
Collapse
|
9
|
Boldt J, Schuster M, Krastl G, Schmitter M, Pfundt J, Stellzig-Eisenhauer A, Kunz F. Developing the Benchmark: Establishing a Gold Standard for the Evaluation of AI Caries Diagnostics. J Clin Med 2024; 13:3846. [PMID: 38999411 PMCID: PMC11242122 DOI: 10.3390/jcm13133846] [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: 06/06/2024] [Revised: 06/26/2024] [Accepted: 06/28/2024] [Indexed: 07/14/2024] Open
Abstract
Background/Objectives: The aim of this study was to establish a histology-based gold standard for the evaluation of artificial intelligence (AI)-based caries detection systems on proximal surfaces in bitewing images. Methods: Extracted human teeth were used to simulate intraoral situations, including caries-free teeth, teeth with artificially created defects and teeth with natural proximal caries. All 153 simulations were radiographed from seven angles, resulting in 1071 in vitro bitewing images. Histological examination of the carious lesion depth was performed twice by an expert. A total of thirty examiners analyzed all the radiographs for caries. Results: We generated in vitro bitewing images to evaluate the performance of AI-based carious lesion detection against a histological gold standard. All examiners achieved a sensitivity of 0.565, a Matthews correlation coefficient (MCC) of 0.578 and an area under the curve (AUC) of 76.1. The histology receiver operating characteristic (ROC) curve significantly outperformed the examiners' ROC curve (p < 0.001). All examiners distinguished induced defects from true caries in 54.6% of cases and correctly classified 99.8% of all teeth. Expert caries classification of the histological images showed a high level of agreement (intraclass correlation coefficient (ICC) = 0.993). Examiner performance varied with caries depth (p ≤ 0.008), except between E2 and E1 lesions (p = 1), while central beam eccentricity, gender, occupation and experience had no significant influence (all p ≥ 0.411). Conclusions: This study successfully established an unbiased dataset to evaluate AI-based caries detection on bitewing surfaces and compare it to human judgement, providing a standardized assessment for fair comparison between AI technologies and helping dental professionals to select reliable diagnostic tools.
Collapse
Affiliation(s)
- Julian Boldt
- Department of Prosthetic Dentistry, University Hospital Würzburg, 97070 Würzburg, Germany
| | - Matthias Schuster
- Department of Prosthetic Dentistry, University Hospital Würzburg, 97070 Würzburg, Germany
| | - Gabriel Krastl
- Center of Dental Traumatology, Department of Conservative Dentistry and Periodontology, University Hospital Würzburg, 97070 Würzburg, Germany
| | - Marc Schmitter
- Department of Prosthetic Dentistry, University Hospital Würzburg, 97070 Würzburg, Germany
| | - Jonas Pfundt
- Department of Prosthetic Dentistry, University Hospital Würzburg, 97070 Würzburg, Germany
| | | | - Felix Kunz
- Department of Orthodontics, University Hospital Würzburg, 97070 Würzburg, Germany
| |
Collapse
|
10
|
Mărginean AC, Mureşanu S, Hedeşiu M, Dioşan L. Teeth segmentation and carious lesions segmentation in panoramic X-ray images using CariSeg, a networks' ensemble. Heliyon 2024; 10:e30836. [PMID: 38803980 PMCID: PMC11128823 DOI: 10.1016/j.heliyon.2024.e30836] [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: 01/11/2024] [Revised: 03/27/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024] Open
Abstract
Background Dental cavities are common oral diseases that can lead to pain, discomfort, and eventually, tooth loss. Early detection and treatment of cavities can prevent these negative consequences. We propose CariSeg, an intelligent system composed of four neural networks that result in the detection of cavities in dental X-rays with 99.42% accuracy. Method The first model of CariSeg, trained using the U-Net architecture, segments the area of interest, the teeth, and crops the radiograph around it. The next component segments the carious lesions and it is an ensemble composed of three architectures: U-Net, Feature Pyramid Network, and DeeplabV3. For tooth identification two merged datasets were used: The Tufts Dental Database consisting of 1000 panoramic radiography images and another dataset of 116 anonymized panoramic X-rays, taken at Noor Medical Imaging Center, Qom. For carious lesion segmentation, a dataset consisting of 150 panoramic X-ray images was acquired from the Department of Oral and Maxillofacial Surgery and Radiology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca. Results The experiments demonstrate that our approach results in 99.42% accuracy and a mean 68.2% Dice coefficient. Conclusions AI helps in detecting carious lesions by analyzing dental X-rays and identifying cavities that might be missed by human observers, leading to earlier detection and treatment of cavities and resulting in better oral health outcomes.
Collapse
Affiliation(s)
- Andra Carmen Mărginean
- Computer Science Department, Babes Bolyai University, Mihail Kogalniceanu 1, Cluj-Napoca, 400347, Cluj, Romania
| | - Sorana Mureşanu
- Department of Oral and Maxillofacial Surgery and Radiology, Iuliu Haţieganu University of Medicine and Pharmacy, Victor Babes, 8, Cluj-Napoca, 400012, Cluj, Romania
| | - Mihaela Hedeşiu
- Department of Oral and Maxillofacial Surgery and Radiology, Iuliu Haţieganu University of Medicine and Pharmacy, Victor Babes, 8, Cluj-Napoca, 400012, Cluj, Romania
| | - Laura Dioşan
- Computer Science Department, Babes Bolyai University, Mihail Kogalniceanu 1, Cluj-Napoca, 400347, Cluj, Romania
| |
Collapse
|
11
|
Ying S, Huang F, Shen X, Liu W, He F. Performance comparison of multifarious deep networks on caries detection with tooth X-ray images. J Dent 2024; 144:104970. [PMID: 38556194 DOI: 10.1016/j.jdent.2024.104970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 03/11/2024] [Accepted: 03/24/2024] [Indexed: 04/02/2024] Open
Abstract
OBJECTIVES Deep networks have been preliminarily studied in caries diagnosis based on clinical X-ray images. However, the performance of different deep networks on caries detection is still unclear. This study aims to comprehensively compare the caries detection performances of recent multifarious deep networks with clinical dentist level as a bridge. METHODS Based on the self-collected periapical radiograph dataset in clinic, four most popular deep networks in two types, namely YOLOv5 and DETR object detection networks, and UNet and Trans-UNet segmentation networks, were included in the comparison study. Five dentists carried out the caries detection on the same testing dataset for reference. Key tooth-level metrics, including precision, sensitivity, specificity, F1-score and Youden index, were obtained, based on which statistical analysis was conducted. RESULTS The F1-score order of deep networks is YOLOv5 (0.87), Trans-UNet (0.86), DETR (0.82) and UNet (0.80) in caries detection. A same ranking order is found using the Youden index combining sensitivity and specificity, which are 0.76, 0.73, 0.69 and 0.64 respectively. A moderate level of concordance was observed between all networks and the gold standard. No significant difference (p > 0.05) was found between deep networks and between the well-trained network and dentists in caries detection. CONCLUSIONS Among investigated deep networks, YOLOv5 is recommended to be priority for caries detection in terms of its high metrics. The well-trained deep network could be used as a good assistance for dentists to detect and diagnose caries. CLINICAL SIGNIFICANCE The well-trained deep network shows a promising potential clinical application prospect. It can provide valuable support to healthcare professionals in facilitating detection and diagnosis of dental caries.
Collapse
Affiliation(s)
- Shunv Ying
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China
| | - Feng Huang
- School of Mechanical & Energy Engineering, Zhejiang University of Science & Technology, Hangzhou, 310023, China.
| | - Xiaoting Shen
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China
| | - Wei Liu
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China
| | - Fuming He
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China.
| |
Collapse
|
12
|
Schropp L, Sørensen APS, Devlin H, Matzen LH. Use of artificial intelligence software in dental education: A study on assisted proximal caries assessment in bitewing radiographs. EUROPEAN JOURNAL OF DENTAL EDUCATION : OFFICIAL JOURNAL OF THE ASSOCIATION FOR DENTAL EDUCATION IN EUROPE 2024; 28:490-496. [PMID: 37961027 DOI: 10.1111/eje.12973] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/14/2023] [Accepted: 10/13/2023] [Indexed: 11/15/2023]
Abstract
INTRODUCTION Teaching of dental caries diagnostics is an essential part of dental education. Diagnosing proximal caries is a challenging task, and automated systems applying artificial intelligence (AI) have been introduced to assist in this respect. Thus, the implementation of AI for teaching purposes may be considered. The aim of this study was to assess the impact of an AI software on students' ability to detect enamel-only proximal caries in bitewing radiographs (BWs) and to assess whether proximal tooth overlap interferes with caries detection. MATERIALS AND METHODS The study included 74 dental students randomly allocated to either a test or control group. At two sessions, both groups assessed proximal enamel caries in BWs. At the first session, the test group registered caries in 25 BWs using AI software (AssistDent®) and the control group without using AI. One month later, both groups detected caries in another 25 BWs in a clinical setup without using the software. The student's registrations were compared with a reference standard. Positive agreement (caries) and negative agreement (no caries) were calculated, and t-tests were applied to assess whether the test and control groups performed differently. Moreover, t-tests were applied to test whether proximal overlap interfered with caries registration. RESULTS At the first and second sessions, 56 and 52 tooth surfaces, respectively, were detected with enamel-only caries according to the reference standard. At session 1, no significant difference between the control (48%) and the test (42%) group was found for positive agreement (p = .08), whereas the negative agreement was higher for the test group (86% vs. 80%; p = .02). At session 2, there was no significant difference between the groups. The test group improved for positive agreement from session 1 to session 2 (p < .001), while the control group improved for negative agreement (p < .001). Thirty-eight per cent of the tooth surfaces overlapped, and the mean positive agreement and negative agreement were significantly lower for overlapping surfaces than non-overlapping surfaces (p < .001) in both groups. CONCLUSION Training with the AI software did not impact on dental students' ability to detect proximal enamel caries in bitewing radiographs although the positive agreement improved over time. It was revealed that proximal tooth overlap interfered with caries detection.
Collapse
Affiliation(s)
- Lars Schropp
- Oral Radiology, Department of Dentistry and Oral Health, Aarhus University, Aarhus C, Denmark
| | - Anders Peter Sejersdal Sørensen
- Oral Radiology, Department of Dentistry and Oral Health, Aarhus University, Aarhus C, Denmark
- Private practice, Tandlægerne Sydcentret, Kolding, Denmark
| | - Hugh Devlin
- Division of Dentistry, School of Medical Sciences, The University of Manchester, Manchester, UK
| | - Louise Hauge Matzen
- Oral Radiology, Department of Dentistry and Oral Health, Aarhus University, Aarhus C, Denmark
| |
Collapse
|
13
|
Chaves ET, Vinayahalingam S, van Nistelrooij N, Xi T, Romero VHD, Flügge T, Saker H, Kim A, Lima GDS, Loomans B, Huysmans MC, Mendes FM, Cenci MS. Detection of caries around restorations on bitewings using deep learning. J Dent 2024; 143:104886. [PMID: 38342368 DOI: 10.1016/j.jdent.2024.104886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 02/06/2024] [Accepted: 02/08/2024] [Indexed: 02/13/2024] Open
Abstract
OBJECTIVE Secondary caries lesions adjacent to restorations, a leading cause of restoration failure, require accurate diagnostic methods to ensure an optimal treatment outcome. Traditional diagnostic strategies rely on visual inspection complemented by radiographs. Recent advancements in artificial intelligence (AI), particularly deep learning, provide potential improvements in caries detection. This study aimed to develop a convolutional neural network (CNN)-based algorithm for detecting primary caries and secondary caries around restorations using bitewings. METHODS Clinical data from 7 general dental practices in the Netherlands, comprising 425 bitewings of 383 patients, were utilized. The study used the Mask-RCNN architecture, for instance, segmentation, supported by the Swin Transformer backbone. After data augmentation, model training was performed through a ten-fold cross-validation. The diagnostic accuracy of the algorithm was evaluated by calculating the area under the Free-Response Receiver Operating Characteristics curve, sensitivity, precision, and F1 scores. RESULTS The model achieved areas under FROC curves of 0.806 and 0.804, and F1-scores of 0.689 and 0.719 for primary and secondary caries detection, respectively. CONCLUSION An accurate CNN-based automated system was developed to detect primary and secondary caries lesions on bitewings, highlighting a significant advancement in automated caries diagnostics. CLINICAL SIGNIFICANCE An accurate algorithm that integrates the detection of both primary and secondary caries will permit the development of automated systems to aid clinicians in their daily clinical practice.
Collapse
Affiliation(s)
- Eduardo Trota Chaves
- Department of Dentistry, Research Institute for Medical Innovation, Radboud University Medical Center, Philips van Leydenlaan 25, Nijmegen, EX 6525, the Netherlands; Graduate Program in Dentistry, School of Dentistry, Federal University of Pelotas, Pelotas, Brazil.
| | - Shankeeth Vinayahalingam
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Centre, Postal Number 590, P.O. Box 9101, Nijmegen, HB 6500, the Netherlands
| | - Niels van Nistelrooij
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Centre, Postal Number 590, P.O. Box 9101, Nijmegen, HB 6500, the Netherlands; Department of Oral and Maxillofacial Surgery, Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Augustenburger Platz 1, Berlin 13353, Germany
| | - Tong Xi
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Centre, Postal Number 590, P.O. Box 9101, Nijmegen, HB 6500, the Netherlands
| | - Vitor Henrique Digmayer Romero
- Department of Dentistry, Research Institute for Medical Innovation, Radboud University Medical Center, Philips van Leydenlaan 25, Nijmegen, EX 6525, the Netherlands; Graduate Program in Dentistry, School of Dentistry, Federal University of Pelotas, Pelotas, Brazil
| | - Tabea Flügge
- Einstein Center for Digital Future, Wilhelmstraße 67, Berlin 10117, Germany
| | - Hadi Saker
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Centre, Postal Number 590, P.O. Box 9101, Nijmegen, HB 6500, the Netherlands
| | - Alexander Kim
- Department of Oral and Maxillofacial Surgery, Radboud University Medical Centre, Postal Number 590, P.O. Box 9101, Nijmegen, HB 6500, the Netherlands
| | - Giana da Silveira Lima
- Graduate Program in Dentistry, School of Dentistry, Federal University of Pelotas, Pelotas, Brazil
| | - Bas Loomans
- Department of Dentistry, Research Institute for Medical Innovation, Radboud University Medical Center, Philips van Leydenlaan 25, Nijmegen, EX 6525, the Netherlands
| | - Marie-Charlotte Huysmans
- Department of Dentistry, Research Institute for Medical Innovation, Radboud University Medical Center, Philips van Leydenlaan 25, Nijmegen, EX 6525, the Netherlands
| | - Fausto Medeiros Mendes
- Department of Dentistry, Research Institute for Medical Innovation, Radboud University Medical Center, Philips van Leydenlaan 25, Nijmegen, EX 6525, the Netherlands; Department of Pediatric Dentistry, School of Dentistry, University of São Paulo, São Paulo, Brazil
| | - Maximiliano Sergio Cenci
- Department of Dentistry, Research Institute for Medical Innovation, Radboud University Medical Center, Philips van Leydenlaan 25, Nijmegen, EX 6525, the Netherlands
| |
Collapse
|
14
|
Albano D, Galiano V, Basile M, Di Luca F, Gitto S, Messina C, Cagetti MG, Del Fabbro M, Tartaglia GM, Sconfienza LM. Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review. BMC Oral Health 2024; 24:274. [PMID: 38402191 PMCID: PMC10894487 DOI: 10.1186/s12903-024-04046-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 02/17/2024] [Indexed: 02/26/2024] Open
Abstract
BACKGROUND The aim of this systematic review is to evaluate the diagnostic performance of Artificial Intelligence (AI) models designed for the detection of caries lesion (CL). MATERIALS AND METHODS An electronic literature search was conducted on PubMed, Web of Science, SCOPUS, LILACS and Embase databases for retrospective, prospective and cross-sectional studies published until January 2023, using the following keywords: artificial intelligence (AI), machine learning (ML), deep learning (DL), artificial neural networks (ANN), convolutional neural networks (CNN), deep convolutional neural networks (DCNN), radiology, detection, diagnosis and dental caries (DC). The quality assessment was performed using the guidelines of QUADAS-2. RESULTS Twenty articles that met the selection criteria were evaluated. Five studies were performed on periapical radiographs, nine on bitewings, and six on orthopantomography. The number of imaging examinations included ranged from 15 to 2900. Four studies investigated ANN models, fifteen CNN models, and two DCNN models. Twelve were retrospective studies, six cross-sectional and two prospective. The following diagnostic performance was achieved in detecting CL: sensitivity from 0.44 to 0.86, specificity from 0.85 to 0.98, precision from 0.50 to 0.94, PPV (Positive Predictive Value) 0.86, NPV (Negative Predictive Value) 0.95, accuracy from 0.73 to 0.98, area under the curve (AUC) from 0.84 to 0.98, intersection over union of 0.3-0.4 and 0.78, Dice coefficient 0.66 and 0.88, F1-score from 0.64 to 0.92. According to the QUADAS-2 evaluation, most studies exhibited a low risk of bias. CONCLUSION AI-based models have demonstrated good diagnostic performance, potentially being an important aid in CL detection. Some limitations of these studies are related to the size and heterogeneity of the datasets. Future studies need to rely on comparable, large, and clinically meaningful datasets. PROTOCOL PROSPERO identifier: CRD42023470708.
Collapse
Affiliation(s)
- Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy.
| | | | - Mariachiara Basile
- Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy
| | - Filippo Di Luca
- Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy
| | - Salvatore Gitto
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Carmelo Messina
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Maria Grazia Cagetti
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - Massimo Del Fabbro
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Ospedale Maggiore Policlinico, UOC Maxillo-Facial Surgery and Dentistry Fondazione IRCCS Cà Granda, Milan, Italy
| | - Gianluca Martino Tartaglia
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Ospedale Maggiore Policlinico, UOC Maxillo-Facial Surgery and Dentistry Fondazione IRCCS Cà Granda, Milan, Italy
| | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| |
Collapse
|
15
|
ForouzeshFar P, Safaei AA, Ghaderi F, Hashemikamangar SS. Dental Caries diagnosis from bitewing images using convolutional neural networks. BMC Oral Health 2024; 24:211. [PMID: 38341526 PMCID: PMC10858561 DOI: 10.1186/s12903-024-03973-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 02/02/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Dental caries, also known as tooth decay, is a widespread and long-standing condition that affects people of all ages. This ailment is caused by bacteria that attach themselves to teeth and break down sugars, creating acid that gradually wears away at the tooth structure. Tooth discoloration, pain, and sensitivity to hot or cold foods and drinks are common symptoms of tooth decay. Although this condition is prevalent among all age groups, it is especially prevalent in children with baby teeth. Early diagnosis of dental caries is critical to preventing further decay and avoiding costly tooth repairs. Currently, dentists employ a time-consuming and repetitive process of manually marking tooth lesions after conducting radiographic exams. However, with the rapid development of artificial intelligence in medical imaging research, there is a chance to improve the accuracy and efficiency of dental diagnosis. METHODS This study introduces a data-driven model for accurately diagnosing dental decay through the use of Bitewing radiology images using convolutional neural networks. The dataset utilized in this research includes 713 patient images obtained from the Samin Maxillofacial Radiology Center located in Tehran, Iran. The images were captured between June 2020 and January 2022 and underwent processing via four distinct Convolutional Neural Networks. The images were resized to 100 × 100 and then divided into two groups: 70% (4219) for training and 30% (1813) for testing. The four networks employed in this study were AlexNet, ResNet50, VGG16, and VGG19. RESULTS Among different well-known CNN architectures compared in this study, the VGG19 model was found to be the most accurate, with a 93.93% accuracy. CONCLUSION This promising result indicates the potential for developing an automatic AI-based dental caries diagnostic model from Bitewing images. It has the potential to serve patients or dentists as a mobile app or cloud-based diagnosis service (clinical decision support system).
Collapse
Affiliation(s)
- Parsa ForouzeshFar
- Department of Data Science, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Ali Asghar Safaei
- Department of Medical Informatics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
- Department of Data Science, Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University, Tehran, Iran.
| | - Foad Ghaderi
- Department of Data Science, Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University, Tehran, Iran
- Human-Computer Interaction Lab, Electrical and Computer Engineering Department, Tarbiat Modares University, Tehran, Iran
| | | |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
Tichý A, Kunt L, Nagyová V, Kybic J. Automatic caries detection in bitewing radiographs-Part II: experimental comparison. Clin Oral Investig 2024; 28:133. [PMID: 38315246 PMCID: PMC10844156 DOI: 10.1007/s00784-024-05528-2] [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: 09/19/2023] [Accepted: 01/23/2024] [Indexed: 02/07/2024]
Abstract
OBJECTIVE The objective of this study was to compare the detection of caries in bitewing radiographs by multiple dentists with an automatic method and to evaluate the detection performance in the absence of a reliable ground truth. MATERIALS AND METHODS Four experts and three novices marked caries using bounding boxes in 100 bitewing radiographs. The same dataset was processed by an automatic object detection deep learning method. All annotators were compared in terms of the number of errors and intersection over union (IoU) using pairwise comparisons, with respect to the consensus standard, and with respect to the annotator of the training dataset of the automatic method. RESULTS The number of lesions marked by experts in 100 images varied between 241 and 425. Pairwise comparisons showed that the automatic method outperformed all dentists except the original annotator in the mean number of errors, while being among the best in terms of IoU. With respect to a consensus standard, the performance of the automatic method was best in terms of the number of errors and slightly below average in terms of IoU. Compared with the original annotator, the automatic method had the highest IoU and only one expert made fewer errors. CONCLUSIONS The automatic method consistently outperformed novices and performed as well as highly experienced dentists. CLINICAL SIGNIFICANCE The consensus in caries detection between experts is low. An automatic method based on deep learning can improve both the accuracy and repeatability of caries detection, providing a useful second opinion even for very experienced dentists.
Collapse
Affiliation(s)
- Antonín Tichý
- Institute of Dental Medicine, First Faculty of Medicine of the Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Lukáš Kunt
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Valéria Nagyová
- Institute of Dental Medicine, First Faculty of Medicine of the Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Jan Kybic
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
| |
Collapse
|
18
|
Pun MHJ. Comment on "Deep-learning approach for caries detection and segmentation on dental bitewing radiographs.". Oral Radiol 2024; 40:92. [PMID: 36773094 DOI: 10.1007/s11282-023-00672-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 01/24/2023] [Indexed: 02/12/2023]
Affiliation(s)
- Ming Hong Jim Pun
- Division of Oral and Maxillofacial Radiology, College of Dentistry, The Ohio State University, Columbus, OH, 43210, USA.
| |
Collapse
|
19
|
Bumann EE, Al-Qarni S, Chandrashekar G, Sabzian R, Bohaty B, Lee Y. A novel collaborative learning model for mixed dentition and fillings segmentation in panoramic radiographs. J Dent 2024; 140:104779. [PMID: 38007173 DOI: 10.1016/j.jdent.2023.104779] [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: 03/10/2023] [Revised: 11/10/2023] [Accepted: 11/11/2023] [Indexed: 11/27/2023] Open
Abstract
INTRODUCTION It is critical for dentists to identify and differentiate primary and permanent teeth, fillings, dental restorations and areas with pathological findings when reviewing dental radiographs to ensure that an accurate diagnosis is made and the optimal treatment can be planned. Unfortunately, dental radiographs are sometimes read incorrectly due to human error or low-quality images. While secondary or group review can help catch errors, many dentists work in practice alone and/or do not have time to review all of their patients' radiographs with another dentist. Artificial intelligence may facilitate the accurate interpretation of radiographs. To help support the review of panoramic radiographs, we developed a novel collaborative learning model that simultaneously identifies and differentiates primary and permanent teeth and detects fillings. METHODS We used publicly accessible dental panoramic radiographic images and images obtained from the University of Missouri-Kansas City, School of Dentistry to develop and optimize two high-performance classifiers: (1) a system for tooth segmentation that can differentiate primary and permanent teeth and (2) a system to detect dental fillings. RESULTS By utilizing these high-performance classifiers, we created models that can identify primary and permanent teeth (mean average precision [mAP] 95.32 % and performance [F-1] 92.50 %), as well as their associated dental fillings (mAP 91.53 % and F-1 91.00 %). We also designed a novel method for collaborative learning that utilizes these two classifiers to enhance recognition performance (mAP 94.09 % and F-1 93.41 %). CONCLUSIONS Our model improves upon the existing machine learning models to simultaneously identify and differentiate primary and permanent teeth, and to identify any associated fillings. CLINICAL SIGNIFICANCE Human error can lead to incorrect readings of panoramic radiographs. By developing artificial intelligence and machine learning methods to analyze panoramic radiographs, dentists can use this information to support their radiograph interpretations, help communicate the information to patients, and assist dental students learning to read radiographs.
Collapse
Affiliation(s)
- Erin Ealba Bumann
- Department of Oral and Craniofacial Sciences, University of Missouri-Kansas City, USA.
| | - Saeed Al-Qarni
- Department of Computer Science, University of Missouri-Kansas City, USA; Department of Computing and Informatics, Saudi Electronic University, Saudi Arabia
| | | | - Roya Sabzian
- Department of Oral and Craniofacial Sciences, University of Missouri-Kansas City, USA
| | - Brenda Bohaty
- Department of Pediatric Dentistry, University of Missouri-Kansas City, USA; Department of Pediatric Dentistry, Children's Mercy Hospital, USA
| | - Yugyung Lee
- Department of Computer Science, University of Missouri-Kansas City, USA
| |
Collapse
|
20
|
Kunt L, Kybic J, Nagyová V, Tichý A. Automatic caries detection in bitewing radiographs: part I-deep learning. Clin Oral Investig 2023; 27:7463-7471. [PMID: 37968358 DOI: 10.1007/s00784-023-05335-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 10/11/2023] [Indexed: 11/17/2023]
Abstract
OBJECTIVE The aim of this work was to assemble a large annotated dataset of bitewing radiographs and to use convolutional neural networks to automate the detection of dental caries in bitewing radiographs with human-level performance. MATERIALS AND METHODS A dataset of 3989 bitewing radiographs was created, and 7257 carious lesions were annotated using minimal bounding boxes. The dataset was then divided into 3 parts for the training (70%), validation (15%), and testing (15%) of multiple object detection convolutional neural networks (CNN). The tested CNN architectures included YOLOv5, Faster R-CNN, RetinaNet, and EfficientDet. To further improve the detection performance, model ensembling was used, and nested predictions were removed during post-processing. The models were compared in terms of the [Formula: see text] score and average precision (AP) with various thresholds of the intersection over union (IoU). RESULTS The twelve tested architectures had [Formula: see text] scores of 0.72-0.76. Their performance was improved by ensembling which increased the [Formula: see text] score to 0.79-0.80. The best-performing ensemble detected caries with the precision of 0.83, recall of 0.77, [Formula: see text], and AP of 0.86 at IoU=0.5. Small carious lesions were predicted with slightly lower accuracy (AP 0.82) than medium or large lesions (AP 0.88). CONCLUSIONS The trained ensemble of object detection CNNs detected caries with satisfactory accuracy and performed at least as well as experienced dentists (see companion paper, Part II). The performance on small lesions was likely limited by inconsistencies in the training dataset. CLINICAL SIGNIFICANCE Caries can be automatically detected using convolutional neural networks. However, detecting incipient carious lesions remains challenging.
Collapse
Affiliation(s)
- Lukáš Kunt
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Jan Kybic
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic.
| | - Valéria Nagyová
- Institute of Dental Medicine, First Faculty of Medicine of the Charles University and General University Hospital, Prague, Czech Republic
| | - Antonín Tichý
- Institute of Dental Medicine, First Faculty of Medicine of the Charles University and General University Hospital, Prague, Czech Republic
| |
Collapse
|
21
|
Tabatabaian F, Vora SR, Mirabbasi S. Applications, functions, and accuracy of artificial intelligence in restorative dentistry: A literature review. J ESTHET RESTOR DENT 2023; 35:842-859. [PMID: 37522291 DOI: 10.1111/jerd.13079] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 08/01/2023]
Abstract
OBJECTIVE The applications of artificial intelligence (AI) are increasing in restorative dentistry; however, the AI performance is unclear for dental professionals. The purpose of this narrative review was to evaluate the applications, functions, and accuracy of AI in diverse aspects of restorative dentistry including caries detection, tooth preparation margin detection, tooth restoration design, metal structure casting, dental restoration/implant detection, removable partial denture design, and tooth shade determination. OVERVIEW An electronic search was performed on Medline/PubMed, Embase, Web of Science, Cochrane, Scopus, and Google Scholar databases. English-language articles, published from January 1, 2000, to March 1, 2022, relevant to the aforementioned aspects were selected using the key terms of artificial intelligence, machine learning, deep learning, artificial neural networks, convolutional neural networks, clustering, soft computing, automated planning, computational learning, computer vision, and automated reasoning as inclusion criteria. A manual search was also performed. Therefore, 157 articles were included, reviewed, and discussed. CONCLUSIONS Based on the current literature, the AI models have shown promising performance in the mentioned aspects when being compared with traditional approaches in terms of accuracy; however, as these models are still in development, more studies are required to validate their accuracy and apply them to routine clinical practice. CLINICAL SIGNIFICANCE AI with its specific functions has shown successful applications with acceptable accuracy in diverse aspects of restorative dentistry. The understanding of these functions may lead to novel applications with optimal accuracy for AI in restorative dentistry.
Collapse
Affiliation(s)
- Farhad Tabatabaian
- Department of Oral Health Sciences, Faculty of Dentistry, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Siddharth R Vora
- Department of Oral Health Sciences, Faculty of Dentistry, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Shahriar Mirabbasi
- Department of Electrical and Computer Engineering, Faculty of Applied Science, The University of British Columbia, Vancouver, British Columbia, Canada
| |
Collapse
|
22
|
Singh N, Pandey A, Tikku AP, Verma P, Singh BP. Attitude, perception and barriers of dental professionals towards artificial intelligence. J Oral Biol Craniofac Res 2023; 13:584-588. [PMID: 37576799 PMCID: PMC10415790 DOI: 10.1016/j.jobcr.2023.06.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 06/27/2023] [Indexed: 08/15/2023] Open
Abstract
Aim To know attitudes, perceptions and barriers towards the use of Artificial Intelligence (AI) in dentistry in India among undergraduate and postgraduate students. Methodology A questionnaire-based cross-sectional study was conducted among participants pursuing graduation and postgraduation. The questionnaire consisted of 23 close-ended and 2 open-ended questions divided into various sections of attitude, perception and barriers. The data was analysed using Statistical Package for Social Sciences (SPSS) version 24.0. Result Out of 937 responses, 55.2% responded that they get information about AI from social media platforms. 51.3% of respondents have basic knowledge about the use of AI in dentistry. 59.6% agreed that AI can be used as a "definitive diagnostic tool" in the diagnosis of diseases. 66.5% agreed that AI can be used for radiographic diagnosis of tooth caries. 71.3% stated that AI can be used as a "treatment planning tool" in dentistry. 55.7% stated that AI should be part of undergraduate dental training. Conclusion This study concluded that both dental students are aware of the concept of AI. Participants were positive when asked if AI can increase the efficiency of diagnosis, prognosis and treatment planning procedures as well as in managing patient data. Both participants believed that the barriers to the introduction of AI in dentistry are a lack of technical resources and a lack of training personnel in college.
Collapse
Affiliation(s)
- Nishi Singh
- Department of Conservative Dentistry & Endodontics, Faculty of Dentistry, King George's Medical University (KGMU), Lucknow, UP, India
| | - Anushka Pandey
- Faculty of Dental Sciences, King George's Medical University (KGMU), Lucknow, UP, India
| | - Aseem Prakash Tikku
- Department of Conservative Dentistry & Endodontics, Faculty of Dental Sciences, King George's Medical University (KGMU), Lucknow, UP, India
| | - Promila Verma
- Department of Conservative Dentistry & Endodontics, Faculty of Dental Sciences, King George's Medical University (KGMU), Lucknow, UP, India
| | - Balendra Pratap Singh
- Department of Prosthodontics and Crown & Bridge, Faculty of Dental Sciences, King George's Medical University (KGMU), Lucknow, UP, India
| |
Collapse
|
23
|
Ahmed WM, Azhari AA, Fawaz KA, Ahmed HM, Alsadah ZM, Majumdar A, Carvalho RM. Artificial intelligence in the detection and classification of dental caries. J Prosthet Dent 2023:S0022-3913(23)00478-X. [PMID: 37640607 DOI: 10.1016/j.prosdent.2023.07.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 07/17/2023] [Accepted: 07/18/2023] [Indexed: 08/31/2023]
Abstract
STATEMENT OF PROBLEM Automated detection of dental caries could enhance early detection, save clinician time, and enrich treatment decisions. However, a reliable system is lacking. PURPOSE The purpose of this study was to train a deep learning model and to assess its ability to detect and classify dental caries. MATERIAL AND METHODS Bitewings radiographs with a 1876×1402-pixel resolution were collected, segmented, and anonymized with a radiographic image analysis software program and were identified and classified according to the modified King Abdulaziz University (KAU) classification for dental caries. The method was based on supervised learning algorithms trained on semantic segmentation tasks. RESULTS The mean score for the intersection-over-union of the model was 0.55 for proximal carious lesions on a 5-category segmentation assignment and a mean F1 score of 0.535 using 554 training samples. CONCLUSIONS The study validated the high potential for developing an accurate caries detection model that will expedite caries identification, assess clinician decision-making, and improve the quality of patient care.
Collapse
Affiliation(s)
- Walaa Magdy Ahmed
- Assistant Professor, Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia.
| | - Amr Ahmed Azhari
- Assistant Professor, Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Khaled Ahmed Fawaz
- Associate Professor, Department of Orthopedic Surgery, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Hani M Ahmed
- Assistant Professor, Department of Civil Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Zainab M Alsadah
- Consultant in Restorative Dentistry, Dental Department, East Jeddah General Hospital, Ministry of Health, Jeddah, Saudi Arabia
| | - Aritra Majumdar
- Graduate student, Department of Computer Science, Computer Science and Applications, Virginia Polytechnic Institute and State University, Blacksburg, Va
| | - Ricardo Marins Carvalho
- Professor, Department of Oral Biological and Medical Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, British Columbia, Canada
| |
Collapse
|
24
|
Lin YC, Chen MC, Chen CH, Chen MH, Liu KY, Chang CC. Fully automated film mounting in dental radiography: a deep learning model. BMC Med Imaging 2023; 23:109. [PMID: 37596563 PMCID: PMC10439602 DOI: 10.1186/s12880-023-01064-9] [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: 04/04/2023] [Accepted: 07/26/2023] [Indexed: 08/20/2023] Open
Abstract
BACKGROUND Dental film mounting is an essential but time-consuming task in dental radiography, with manual methods often prone to errors. This study aims to develop a deep learning (DL) model for accurate automated classification and mounting of both intraoral and extraoral dental radiography. METHOD The present study employed a total of 22,334 intraoral images and 1,035 extraoral images to train the model. The performance of the model was tested on an independent internal dataset and two external datasets from different institutes. Images were categorized into 32 tooth areas. The VGG-16, ResNet-18, and ResNet-101 architectures were used for pretraining, with the ResNet-101 ultimately being chosen as the final trained model. The model's performance was evaluated using metrics of accuracy, precision, recall, and F1 score. Additionally, we evaluated the influence of misalignment on the model's accuracy and time efficiency. RESULTS The ResNet-101 model outperformed VGG-16 and ResNet-18 models, achieving the highest accuracy of 0.976, precision of 0.969, recall of 0.984, and F1-score of 0.977 (p < 0.05). For intraoral images, the overall accuracy remained consistent across both internal and external datasets, ranging from 0.963 to 0.972, without significant differences (p = 0.348). For extraoral images, the accuracy consistently achieved the highest value of 1 across all institutes. The model's accuracy decreased as the tilt angle of the X-ray film increased. The model achieved the highest accuracy of 0.981 with correctly aligned films, while the lowest accuracy of 0.937 was observed for films exhibiting severe misalignment of ± 15° (p < 0.001). The average time required for the tasks of image rotation and classification for each image was 0.17 s, which was significantly faster than that of the manual process, which required 1.2 s (p < 0.001). CONCLUSION This study demonstrated the potential of DL-based models in automating dental film mounting with high accuracy and efficiency. The proper alignment of X-ray films is crucial for accurate classification by the model.
Collapse
Affiliation(s)
- Yu-Chun Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Meng-Chi Chen
- Department of Dentistry, Chang Gung Memorial Hospital at Taipei, Taipei, Taiwan
| | - Cheng-Hsueh Chen
- Department of Dentistry, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu, Taiwan
| | - Mu-Hsiung Chen
- Department of Dentistry, National Taiwan University Hospital, Taipei, Taiwan
| | - Kang-Yi Liu
- Department of Electrical Engineering, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd, Taipei, 10608, Taiwan
| | - Cheng-Chun Chang
- Department of Electrical Engineering, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd, Taipei, 10608, Taiwan.
| |
Collapse
|
25
|
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.
Collapse
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
| |
Collapse
|
26
|
Anil S, Porwal P, Porwal A. Transforming Dental Caries Diagnosis Through Artificial Intelligence-Based Techniques. Cureus 2023; 15:e41694. [PMID: 37575741 PMCID: PMC10413921 DOI: 10.7759/cureus.41694] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 08/15/2023] Open
Abstract
Diagnosing dental caries plays a pivotal role in preventing and treating tooth decay. However, traditional methods of diagnosing caries often fall short in accuracy and efficiency. Despite the endorsement of radiography as a diagnostic tool, the identification of dental caries through radiographic images can be influenced by individual interpretation. Incorporating artificial intelligence (AI) into diagnosing dental caries holds significant promise, potentially enhancing the precision and efficiency of diagnoses. This review introduces the fundamental concepts of AI, including machine learning and deep learning algorithms, and emphasizes their relevance and potential contributions to the diagnosis of dental caries. It further explains the process of gathering and pre-processing radiography data for AI examination. Additionally, AI techniques for dental caries diagnosis are explored, focusing on image processing, analysis, and classification models for predicting caries risk and severity. Deep learning applications in dental caries diagnosis using convolutional neural networks are presented. Furthermore, the integration of AI systems into dental practice is discussed, including the challenges and considerations for implementation as well as ethical and legal aspects. The breadth of AI technologies and their prospective utility in clinical scenarios for diagnosing dental caries from dental radiographs is presented. This review outlines the advancements of AI and its potential in revolutionizing dental caries diagnosis, encouraging further research and development in this rapidly evolving field.
Collapse
Affiliation(s)
| | - Priyanka Porwal
- Dentistry, Pushpagiri Institute of Medical Sciences and Research Centre, Tiruvalla, IND
| | - Amit Porwal
- Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan, SAU
| |
Collapse
|
27
|
The U-Net Approaches to Evaluation of Dental Bite-Wing Radiographs: An Artificial Intelligence Study. Diagnostics (Basel) 2023; 13:diagnostics13030453. [PMID: 36766557 PMCID: PMC9914538 DOI: 10.3390/diagnostics13030453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 01/17/2023] [Accepted: 01/24/2023] [Indexed: 01/28/2023] Open
Abstract
Bite-wing radiographs are one of the most used intraoral radiography techniques in dentistry. AI is extremely important in terms of more efficient patient care in the field of dentistry. The aim of this study was to perform a diagnostic evaluation on bite-wing radiographs with an AI model based on CNNs. In this study, 500 bite-wing radiographs in the radiography archive of Eskişehir Osmangazi University, Faculty of Dentistry, Department of Oral and Maxillofacial Radiology were used. The CranioCatch labeling program (CranioCatch, Eskisehir, Turkey) with tooth decays, crowns, pulp, restoration material, and root-filling material for five different diagnoses were made by labeling the segmentation technique. The U-Net architecture was used to develop the AI model. F1 score, sensitivity, and precision results of the study, respectively, caries 0.8818-0.8235-0.9491, crown; 0.9629-0.9285-1, pulp; 0.9631-0.9843-0.9429, with restoration material; and 0.9714-0.9622-0.9807 was obtained as 0.9722-0.9459-1 for the root filling material. This study has shown that an AI model can be used to automatically evaluate bite-wing radiographs and the results are promising. Owing to these automatically prepared charts, physicians in a clinical intense tempo will be able to work more efficiently and quickly.
Collapse
|
28
|
Azhari AA, Helal N, Sabri LM, Abduljawad A. Artificial intelligence (AI) in restorative dentistry: Performance of AI models designed for detection of interproximal carious lesions on primary and permanent dentition. Digit Health 2023; 9:20552076231216681. [PMID: 38047163 PMCID: PMC10693222 DOI: 10.1177/20552076231216681] [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: 03/20/2023] [Accepted: 11/08/2023] [Indexed: 12/05/2023] Open
Abstract
Objective The objective of this study was to evaluate the effectiveness of deep learning methods in detecting dental caries from radiographic images. Methods A total of 771 bitewing radiographs were divided into two groups: adult (n = 554) and pediatric (n = 217). Two distinct semantic segmentation models were constructed for each group. They were manually labeled by general dentists for semantic segmentation. The inter-examiner reliability of the two examiners was also measured. Finally, the models were trained using transfer learning methodology along with computer science advanced tools, such as ensemble U-Nets with ResNet50, ResNext101, and Vgg19 as the encoders, which were all pretrained on ImageNet weights using a training dataset. Results Intersection over union (IoU) score was used to evaluate the outcomes of the deep learning model. For the adult dataset, the IoU averaged 98%, 23%, 19%, and 51% for zero, primary, moderate, and advanced carious lesions, respectively. For pediatric bitewings, the IoU averaged 97%, 8%, 17%, and 25% for zero, primary, moderate, and advanced caries, respectively. Advanced caries was more accurately detected than primary caries on adults and pediatric bitewings P < 0.05. Conclusions The proposed deep learning models can accurately detect advanced caries in permanent or primary bitewing radiographs. Misclassification mostly occurs between primary and moderate caries. Although the model performed well in correctly classifying the lesions, it can misclassify one as the other or does not accurately capture the depth of the lesion at this early stage.
Collapse
Affiliation(s)
- Amr Ahmed Azhari
- Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Narmin Helal
- Department of Pediatric Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Leena M Sabri
- Internship Training Program, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Abeer Abduljawad
- Internship Training Program, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| |
Collapse
|
29
|
Sukegawa S, Tanaka F, Hara T, Yoshii K, Yamashita K, Nakano K, Takabatake K, Kawai H, Nagatsuka H, Furuki Y. Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography. Sci Rep 2022; 12:16925. [PMID: 36209283 PMCID: PMC9547920 DOI: 10.1038/s41598-022-21408-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Accepted: 09/27/2022] [Indexed: 12/29/2022] Open
Abstract
In this study, the accuracy of the positional relationship of the contact between the inferior alveolar canal and mandibular third molar was evaluated using deep learning. In contact analysis, we investigated the diagnostic performance of the presence or absence of contact between the mandibular third molar and inferior alveolar canal. We also evaluated the diagnostic performance of bone continuity diagnosed based on computed tomography as a continuity analysis. A dataset of 1279 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014-2021) was used for the validation. The deep learning models were ResNet50 and ResNet50v2, with stochastic gradient descent and sharpness-aware minimization (SAM) as optimizers. The performance metrics were accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). The results indicated that ResNet50v2 using SAM performed excellently in the contact and continuity analyses. The accuracy and AUC were 0.860 and 0.890 for the contact analyses and 0.766 and 0.843 for the continuity analyses. In the contact analysis, SAM and the deep learning model performed effectively. However, in the continuity analysis, none of the deep learning models demonstrated significant classification performance.
Collapse
Affiliation(s)
- Shintaro Sukegawa
- grid.414811.90000 0004 1763 8123Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa 760-8557 Japan ,grid.258331.e0000 0000 8662 309XDepartment of Oral and Maxillofacial Surgery, Kagawa University School of Medicine, 1750-1 Ikenobe, Miki, Kagawa 761-0793 Japan ,grid.261356.50000 0001 1302 4472Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558 Japan
| | - Futa Tanaka
- grid.256342.40000 0004 0370 4927Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu 501-1193 Japan
| | - Takeshi Hara
- grid.256342.40000 0004 0370 4927Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu 501-1193 Japan ,Center for Healthcare Information Technology (C-HiT), Tokai National Higher Education and Research System, 1-1 Yanagido, Gifu, Gifu 501-1193 Japan
| | - Kazumasa Yoshii
- grid.256342.40000 0004 0370 4927Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu 501-1193 Japan
| | - Katsusuke Yamashita
- Polytechnic Center Kagawa, 2-4-3, Hananomiya-cho, Takamatsu, Kagawa 761-8063 Japan
| | - Keisuke Nakano
- grid.261356.50000 0001 1302 4472Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558 Japan
| | - Kiyofumi Takabatake
- grid.261356.50000 0001 1302 4472Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558 Japan
| | - Hotaka Kawai
- grid.261356.50000 0001 1302 4472Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558 Japan
| | - Hitoshi Nagatsuka
- grid.261356.50000 0001 1302 4472Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558 Japan
| | - Yoshihiko Furuki
- grid.414811.90000 0004 1763 8123Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa 760-8557 Japan
| |
Collapse
|
30
|
Ramana Kumari A, Nagaraja Rao S, Ramana Reddy P. Design of hybrid dental caries segmentation and caries detection with meta-heuristic-based ResneXt-RNN. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103961] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
|
31
|
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.
Collapse
|
32
|
Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review. Diagnostics (Basel) 2022; 12:diagnostics12051083. [PMID: 35626239 PMCID: PMC9139989 DOI: 10.3390/diagnostics12051083] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 04/12/2022] [Accepted: 04/25/2022] [Indexed: 01/27/2023] Open
Abstract
Evolution in the fields of science and technology has led to the development of newer applications based on Artificial Intelligence (AI) technology that have been widely used in medical sciences. AI-technology has been employed in a wide range of applications related to the diagnosis of oral diseases that have demonstrated phenomenal precision and accuracy in their performance. The aim of this systematic review is to report on the diagnostic accuracy and performance of AI-based models designed for detection, diagnosis, and prediction of dental caries (DC). Eminent electronic databases (PubMed, Google scholar, Scopus, Web of science, Embase, Cochrane, Saudi Digital Library) were searched for relevant articles that were published from January 2000 until February 2022. A total of 34 articles that met the selection criteria were critically analyzed based on QUADAS-2 guidelines. The certainty of the evidence of the included studies was assessed using the GRADE approach. AI has been widely applied for prediction of DC, for detection and diagnosis of DC and for classification of DC. These models have demonstrated excellent performance and can be used in clinical practice for enhancing the diagnostic performance, treatment quality and patient outcome and can also be applied to identify patients with a higher risk of developing DC.
Collapse
|
33
|
Estai M, Tennant M, Gebauer D, Brostek A, Vignarajan J, Mehdizadeh M, Saha S. Evaluation of a deep learning system for automatic detection of proximal surface dental caries on bitewing radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol 2022; 134:262-270. [DOI: 10.1016/j.oooo.2022.03.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 02/23/2022] [Accepted: 03/12/2022] [Indexed: 01/11/2023]
|