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
|
Turosz N, Chęcińska K, Chęciński M, Lubecka K, Bliźniak F, Sikora M. Artificial Intelligence (AI) Assessment of Pediatric Dental Panoramic Radiographs (DPRs): A Clinical Study. Pediatr Rep 2024; 16:794-805. [PMID: 39311330 PMCID: PMC11417896 DOI: 10.3390/pediatric16030067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 08/26/2024] [Accepted: 09/04/2024] [Indexed: 09/26/2024] Open
Abstract
This clinical study aimed to evaluate the sensitivity, specificity, accuracy, and precision of artificial intelligence (AI) in assessing permanent teeth in pediatric patients. Over one thousand consecutive DPRs taken in Kielce, Poland, with the Carestream CS9600 device were screened. In the study material, 35 dental panoramic radiographs (DPRs) of patients of developmental age were identified and included. They were automatically evaluated with an AI algorithm. The DPRs were then analyzed by researchers. The status of the following dichotomous variables was assessed: (1) decay, (2) missing tooth, (3) filled tooth, (4) root canal filling, and (5) endodontic lesion. The results showed high specificity and accuracy (all above 85%) in detecting caries, dental fillings, and missing teeth but low precision. This study provided a detailed assessment of AI performance in a previously neglected age group. In conclusion, the overall accuracy of AI algorithms for evaluating permanent dentition in dental panoramic radiographs is lower for pediatric patients than adults or the entire population. Hence, identifying primary teeth should be implemented in AI-driven software, at least so as to ignore them when assessing mixed dentition (ClinicalTrials.gov registration number: NCT06258798).
Collapse
Affiliation(s)
- Natalia Turosz
- Department of Maxillofacial Surgery, Hospital of the Ministry of Interior, Wojska Polskiego 51, 25-375 Kielce, Poland
| | - Kamila Chęcińska
- Department of Glass Technology and Amorphous Coatings, Faculty of Materials Science and Ceramics, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków, Poland;
| | - Maciej Chęciński
- Department of Oral Surgery, Preventive Medicine Center, Komorowskiego 12, 30-106 Kraków, Poland; (M.C.); (K.L.); (F.B.)
| | - Karolina Lubecka
- Department of Oral Surgery, Preventive Medicine Center, Komorowskiego 12, 30-106 Kraków, Poland; (M.C.); (K.L.); (F.B.)
| | - Filip Bliźniak
- Department of Oral Surgery, Preventive Medicine Center, Komorowskiego 12, 30-106 Kraków, Poland; (M.C.); (K.L.); (F.B.)
| | - Maciej Sikora
- Department of Maxillofacial Surgery, Hospital of the Ministry of Interior, Wojska Polskiego 51, 25-375 Kielce, Poland
- Department of Biochemistry and Medical Chemistry, Pomeranian Medical University, Powstańców Wielkopolskich 72, 70-111 Szczecin, Poland
| |
Collapse
|
3
|
Modesto-Mata M, de la Fuente Valentín L, Hlusko LJ, Martínez de Pinillos M, Towle I, García-Campos C, Martinón-Torres M, Bermúdez de Castro JM. Artificial neural networks reconstruct missing perikymata in worn teeth. Anat Rec (Hoboken) 2024; 307:3120-3138. [PMID: 38468123 DOI: 10.1002/ar.25416] [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: 08/01/2023] [Revised: 02/08/2024] [Accepted: 02/11/2024] [Indexed: 03/13/2024]
Abstract
Dental evolutionary studies in hominins are key to understanding how our ancestors and close fossil relatives grew from the early stages of embryogenesis into adults. In a sense, teeth are like an airplane's 'black box' as they record important variables for assessing developmental timing, enabling comparisons within and between populations, species, and genera. The ability to discern this type of nuanced information is embedded in the nature of how tooth enamel and dentin form: incrementally and over years. This incremental growth leaves chronological indicators in the histological structure of enamel, visible on the crown surface as perikymata. These structures are used in the process of reconstructing the rate and timing of tooth formation. Unfortunately, the developmentally earliest growth lines in lateral enamel are quickly lost to wear once the tooth crown erupts. We developed a method to reconstruct these earliest, missing perilymata from worn teeth through knowledge of the later-developed, visible perikymata for all tooth types (incisors, canines, premolars, and molars) using a modern human dataset. Building on our previous research using polynomial regressions, here we describe an artificial neural networks (ANN) method. This new ANN method mostly predicts within 2 counts the number of perikymata present in each of the first three deciles of the crown height for all tooth types. Our ANN method for estimating perikymata lost through wear has two immediate benefits: more accurate values can be produced and worn teeth can be included in dental research. This tool is available on the open-source platform R within the package teethR released under GPL v3.0 license, enabling other researchers the opportunity to expand their datasets for studies of periodicity in histological growth, dental development, and evolution.
Collapse
Affiliation(s)
- Mario Modesto-Mata
- Centro Nacional de Investigación sobre la Evolución Humana (CENIEH), Burgos, Spain
- Universidad Internacional de La Rioja (UNIR), Logroño (La Rioja), Spain
| | | | - Leslea J Hlusko
- Centro Nacional de Investigación sobre la Evolución Humana (CENIEH), Burgos, Spain
| | - Marina Martínez de Pinillos
- Centro Nacional de Investigación sobre la Evolución Humana (CENIEH), Burgos, Spain
- Laboratorio de Evolución Humana (LEH), Universidad de Burgos, Burgos, Spain
| | - Ian Towle
- Centro Nacional de Investigación sobre la Evolución Humana (CENIEH), Burgos, Spain
| | - Cecilia García-Campos
- Centro Nacional de Investigación sobre la Evolución Humana (CENIEH), Burgos, Spain
- Facultad de Ciencias, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, Madrid, Spain
| | - María Martinón-Torres
- Centro Nacional de Investigación sobre la Evolución Humana (CENIEH), Burgos, Spain
- Department of Anthropology, University College London, London, UK
| | - José María Bermúdez de Castro
- Centro Nacional de Investigación sobre la Evolución Humana (CENIEH), Burgos, Spain
- Department of Anthropology, University College London, London, UK
| |
Collapse
|
4
|
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
|
5
|
Negi S, Mathur A, Tripathy S, Mehta V, Snigdha NT, Adil AH, Karobari MI. Artificial Intelligence in Dental Caries Diagnosis and Detection: An Umbrella Review. Clin Exp Dent Res 2024; 10:e70004. [PMID: 39206581 PMCID: PMC11358700 DOI: 10.1002/cre2.70004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 04/29/2024] [Accepted: 08/16/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND AND AIM Dental caries is largely preventable, yet an important global health issue. Numerous systematic reviews have summarized the efficacy of artificial intelligence (AI) models for the diagnosis and detection of dental caries. Therefore, this umbrella review aimed to synthesize the results of systematic reviews on the application and effectiveness of AI models in diagnosing and detecting dental caries. METHODS MEDLINE/PubMed, IEEE Explore, Embase, and Cochrane Database of Systematic Reviews were searched to retrieve studies. Two authors independently screened the articles based on eligibility criteria and then, appraised the included articles. The findings are summarized in tabulation form and discussed using the narrative method. RESULT A total of 1249 entries were identified out of which 7 were finally included. The most often employed AI algorithms were the multilayer perceptron, support vector machine (SVM), and neural networks. The algorithms were built to perform the segmentation, classification, caries detection, diagnosis, and caries prediction from several sources, including periapical radiographs, panoramic radiographs, smartphone images, bitewing radiographs, near-infrared light transillumination images, and so forth. Convoluted neural networks (CNN) demonstrated high sensitivity, specificity, and area under the curve in the caries detection, segmentation, and classification tests. Notably, AI in conjunction with periapical and panoramic radiography images yielded better accuracy in detecting and diagnosing dental caries. CONCLUSION AI models, especially convolutional neural network (CNN)-based models, have an enormous amount of potential for accurate, objective dental caries diagnosis and detection. However, ethical considerations and cautious adoption remain critical to its successful integration into routine practice.
Collapse
Affiliation(s)
- Sapna Negi
- Department of Dental Research Cell, Dr. D. Y. Patil Dental College and HospitalDr. D. Y. Patil VidyapeethPuneMaharashtraIndia
| | - Ankita Mathur
- Department of Dental Research Cell, Dr. D. Y. Patil Dental College and HospitalDr. D. Y. Patil VidyapeethPuneMaharashtraIndia
| | - Snehasish Tripathy
- Department of Dental Research Cell, Dr. D. Y. Patil Dental College and HospitalDr. D. Y. Patil VidyapeethPuneMaharashtraIndia
| | - Vini Mehta
- Department of Dental Research Cell, Dr. D. Y. Patil Dental College and HospitalDr. D. Y. Patil VidyapeethPuneMaharashtraIndia
| | - Niher Tabassum Snigdha
- Department of Dental Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical SciencesSaveetha UniversityChennaiTamil NaduIndia
| | - Abdul Habeeb Adil
- Department of Dental Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical SciencesSaveetha UniversityChennaiTamil NaduIndia
| | - Mohmed Isaqali Karobari
- Department of Dental Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical SciencesSaveetha UniversityChennaiTamil NaduIndia
- Department of Restorative Dentistry & Endodontics, Faculty of DentistryUniversity of PuthisastraPhnom PenhCambodia
| |
Collapse
|
6
|
Wu Z, Zhang C, Ye X, Dai Y, Zhao J, Zhao W, Zheng Y. Comparison of the Efficacy of Artificial Intelligence-Powered Software in Crown Design: An In Vitro Study. Int Dent J 2024:S0020-6539(24)00196-5. [PMID: 39069456 DOI: 10.1016/j.identj.2024.06.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 06/20/2024] [Accepted: 06/24/2024] [Indexed: 07/30/2024] Open
Abstract
INTRODUCTION AND AIMS Artificial intelligence (AI) has been adopted in the field of dental restoration. This study aimed to evaluate the time efficiency and morphological accuracy of crowns designed by two AI-powered software programs in comparison with conventional computer-aided design software. METHODS A total of 33 clinically adapted posterior crowns were involved in the standard group. AI Automate (AA) and AI Dentbird Crown (AD) used two AI-powered design software programs, while the computer-aided experienced and computer-aided novice employed the Exocad DentalCAD software. Time efficiency between the AI-powered groups and computer-aided groups was evaluated by assessing the elapsed time. Morphological accuracy was assessed by means of three-dimensional geometric calculations, with the root-mean-square error compared against the standard group. Statistical analysis was conducted via the Kruskal-Wallis test (α = 0.05). RESULTS The time efficiency of the AI-powered groups was significantly higher than that of the computer-aided groups (P < .01). Moreover, the working time for both AA and AD groups was only one-quarter of that for the computer-aided novice group. Four groups significantly differed in morphological accuracy for occlusal and distal surfaces (P < .05). The AD group performed lower accuracy than the other three groups on the occlusal surfaces (P < .001) and the computer-aided experienced group was superior to the AA group in terms of accuracy on the distal surfaces (P = .029). However, morphological accuracy showed no significant difference among the four groups for mesial surfaces and margin lines (P > .05). CONCLUSION AI-powered software enhanced the efficiency of crown design but failed to excel at morphological accuracy compared with experienced technicians using computer-aided software. AI-powered software requires further research and extensive deep learning to improve the morphological accuracy and stability of the crown design.
Collapse
Affiliation(s)
- Ziqiong Wu
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, China
| | - Chengqi Zhang
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xinjian Ye
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Centre for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Centre of Zhejiang University, Hangzhou, China
| | - Yuwei Dai
- Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Centre for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Centre of Zhejiang University, Hangzhou, China; Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jing Zhao
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, China
| | - Wuyuan Zhao
- Hangzhou Erran Technology Co., Ltd., Hangzhou, China
| | - Yuanna Zheng
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, China; Ningbo Dental Hospital/Ningbo Oral Health Research Institute, Ningbo, China.
| |
Collapse
|
7
|
Lee HS, Yang S, Han JY, Kang JH, Kim JE, Huh KH, Yi WJ, Heo MS, Lee SS. Automatic detection and classification of nasopalatine duct cyst and periapical cyst on panoramic radiographs using deep convolutional neural networks. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 138:184-195. [PMID: 38158267 DOI: 10.1016/j.oooo.2023.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/01/2023] [Accepted: 09/15/2023] [Indexed: 01/03/2024]
Abstract
OBJECTIVE The aim of this study was to evaluate a deep convolutional neural network (DCNN) method for the detection and classification of nasopalatine duct cysts (NPDC) and periapical cysts (PAC) on panoramic radiographs. STUDY DESIGN A total of 1,209 panoramic radiographs with 606 NPDC and 603 PAC were labeled with a bounding box and divided into training, validation, and test sets with an 8:1:1 ratio. The networks used were EfficientDet-D3, Faster R-CNN, YOLO v5, RetinaNet, and SSD. Mean average precision (mAP) was used to assess performance. Sixty images with no lesion in the anterior maxilla were added to the previous test set and were tested on 2 dentists with no training in radiology (GP) and on EfficientDet-D3. The performances were comparatively examined. RESULTS The mAP for each DCNN was EfficientDet-D3 93.8%, Faster R-CNN 90.8%, YOLO v5 89.5%, RetinaNet 79.4%, and SSD 60.9%. The classification performance of EfficientDet-D3 was higher than that of the GPs' with accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 94.4%, 94.4%, 97.2%, 94.6%, and 97.2%, respectively. CONCLUSIONS The proposed method achieved high performance for the detection and classification of NPDC and PAC compared with the GPs and presented promising prospects for clinical application.
Collapse
Affiliation(s)
- Han-Sol Lee
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Su Yang
- Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - Ji-Yong Han
- Interdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, South Korea
| | - Ju-Hee Kang
- Department of Oral and Maxillofacial Radiology, Seoul National University Dental Hospital, Seoul, South Korea
| | - Jo-Eun Kim
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Kyung-Hoe Huh
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| | - Won-Jin Yi
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea; Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea; Interdisciplinary Program in Bioengineering, College of Engineering, Seoul National University, Seoul, South Korea.
| | - Min-Suk Heo
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea.
| | - Sam-Sun Lee
- Department of Oral and Maxillofacial Radiology and Dental Research Institute, School of Dentistry, Seoul National University, Seoul, South Korea
| |
Collapse
|
8
|
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
|
9
|
Esmaeilyfard R, Bonyadifard H, Paknahad M. Dental Caries Detection and Classification in CBCT Images Using Deep Learning. Int Dent J 2024; 74:328-334. [PMID: 37940474 PMCID: PMC10988262 DOI: 10.1016/j.identj.2023.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 09/24/2023] [Accepted: 10/09/2023] [Indexed: 11/10/2023] Open
Abstract
OBJECTIVES This study aimed to investigate the accuracy of deep learning algorithms to diagnose tooth caries and classify the extension and location of dental caries in cone beam computed tomography (CBCT) images. To the best of our knowledge, this is the first study to evaluate the application of deep learning for dental caries in CBCT images. METHODS The CBCT image dataset comprised 382 molar teeth with caries and 403 noncarious molar cases. The dataset was divided into a development set for training and validation and test set. Three images were obtained for each case, including axial, sagittal, and coronal. The test dataset was provided to a multiple-input convolutional neural network (CNN). The network made predictions regarding the presence or absence of dental decay and classified the lesions according to their depths and types for the provided samples. Accuracy, sensitivity, specificity, and F1 score values were measured for dental caries detection and classification. RESULTS The diagnostic accuracy, sensitivity, specificity, and F1 score for caries detection in carious molar teeth were 95.3%, 92.1%, 96.3%, and 93.2%, respectively, and for noncarious molar teeth were 94.8%, 94.3%, 95.8%, and 94.6%. The CNN network showed high sensitivity, specificity, and accuracy in classifying caries extensions and locations. CONCLUSIONS This research demonstrates that deep learning models can accurately identify dental caries and classify their depths and types with high accuracy, sensitivity, and specificity. The successful application of deep learning in this field will undoubtedly assist dental practitioners and patients in improving diagnostic and treatment planning in dentistry. CLINICAL SIGNIFICANCE This study showed that deep learning can accurately detect and classify dental caries. Deep learning can provide dental caries detection accurately. Considering the shortage of dentists in certain areas, using CNNs can lead to broader geographic coverage in detecting dental caries.
Collapse
Affiliation(s)
- Rasool Esmaeilyfard
- Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran
| | - Haniyeh Bonyadifard
- Department of Computer Engineering and Information Technology, Shiraz University of Technology, Shiraz, Iran
| | - Maryam Paknahad
- Oral, and Dental Disease Research Center, Oral and Maxillofacial Radiology, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran.
| |
Collapse
|
10
|
Kühnisch J, Aps JK, Splieth C, Lussi A, Jablonski-Momeni A, Mendes FM, Schmalz G, Fontana M, Banerjee A, Ricketts D, Schwendicke F, Douglas G, Campus G, van der Veen M, Opdam N, Doméjean S, Martignon S, Neuhaus KW, Horner K, Huysmans MCD. ORCA-EFCD consensus report on clinical recommendation for caries diagnosis. Paper I: caries lesion detection and depth assessment. Clin Oral Investig 2024; 28:227. [PMID: 38514502 PMCID: PMC10957694 DOI: 10.1007/s00784-024-05597-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 02/29/2024] [Indexed: 03/23/2024]
Abstract
OBJECTIVES The aim of the present consensus paper was to provide recommendations for clinical practice considering the use of visual examination, dental radiography and adjunct methods for primary caries detection. MATERIALS AND METHODS The executive councils of the European Organisation for Caries Research (ORCA) and the European Federation of Conservative Dentistry (EFCD) nominated ten experts each to join the expert panel. The steering committee formed three work groups that were asked to provide recommendations on (1) caries detection and diagnostic methods, (2) caries activity assessment and (3) forming individualised caries diagnoses. The experts responsible for "caries detection and diagnostic methods" searched and evaluated the relevant literature, drafted this manuscript and made provisional consensus recommendations. These recommendations were discussed and refined during the structured process in the whole work group. Finally, the agreement for each recommendation was determined using an anonymous Delphi survey. RESULTS Recommendations (N = 8) were approved and agreed upon by the whole expert panel: visual examination (N = 3), dental radiography (N = 3) and additional diagnostic methods (N = 2). While the quality of evidence was found to be heterogeneous, all recommendations were agreed upon by the expert panel. CONCLUSION Visual examination is recommended as the first-choice method for the detection and assessment of caries lesions on accessible surfaces. Intraoral radiography, preferably bitewing, is recommended as an additional method. Adjunct, non-ionising radiation methods might also be useful in certain clinical situations. CLINICAL RELEVANCE The expert panel merged evidence from the scientific literature with practical considerations and provided recommendations for their use in daily dental practice.
Collapse
Affiliation(s)
- Jan Kühnisch
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians Universität München, Poliklinik für Zahnerhaltung und Parodontologie, Goethestraße 70, 80336, München, Germany.
| | | | - Christian Splieth
- Preventive and Pediatric Dentistry, Center for Oral Health, Universitätsmedizin Greifswald, Greifswald, Germany
| | - Adrian Lussi
- University Hospital for Conservative Dentistry and Periodontology, Medical University of Innsbruck, Innsbruck, Austria
- Department of Restorative, Preventive and Pediatric Dentistry, School of Dental Medicine, University of Bern, Bern, Switzerland
| | | | - Fausto M Mendes
- Department of Pediatric Dentistry, School of Dentistry, University of São Paulo, São Paulo, Brazil
| | - Gottfried Schmalz
- Department of Conservative Dentistry and Periodontology, University Hospital Regensburg, Regensburg, Germany
- Department of Periodontology, University of Bern, Bern, Switzerland
| | - Margherita Fontana
- Department of Cariology, Restorative Sciences and Endodontics, University of Michigan School of Dentistry, Ann Arbor, USA
| | - Avijit Banerjee
- Conservative & MI Dentistry, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK
| | - David Ricketts
- Unit of Restorative Dentistry, University of Dundee, Dundee, UK
| | - Falk Schwendicke
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians Universität München, Poliklinik für Zahnerhaltung und Parodontologie, Goethestraße 70, 80336, München, Germany
| | - Gail Douglas
- Department of Dental Public Health, University of Leeds Dental School, Leeds, UK
| | - Guglielmo Campus
- Department of Restorative, Preventive and Pediatric Dentistry, School of Dental Medicine, University of Bern, Bern, Switzerland
- Department of Surgery, Microsurgery and Medicine Sciences, School of Dentistry, University of Sassari, Sassari, Italy
| | - Monique van der Veen
- Departments of Preventive Dentistry and Paediatric Dentistry, Academic Centre for Dentistry Amsterdam, University of Amsterdam and VU University, Amsterdam, The Netherlands
- Oral Hygiene School, Inholland University of applied sciences, Amsterdam, The Netherlands
| | - Niek Opdam
- Department of Dentistry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Sophie Doméjean
- Centre de Recherche en Odontologie Clinique EA 4847, UFR d'Odontologie, Département d'Odontologie Conservatrice, Université Clermont Auvergne, Clermont-Ferrand, France
- Service d'Odontologie, CHU Estaing Clermont-Ferrand, Clermont-Ferrand, France
| | - Stefania Martignon
- UNICA - Caries Research Unit, Research Department, Universidad El Bosque, Bogotá, Colombia
| | - Klaus W Neuhaus
- Department of Pediatric Oral Health, University Center for Dental Medicine Basel (UZB), University of Basel, Basel, Switzerland
- Department of Dermatology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Keith Horner
- Division of Dentistry, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | | |
Collapse
|
11
|
Pérez de Frutos J, Holden Helland R, Desai S, Nymoen LC, Langø T, Remman T, Sen A. AI-Dentify: deep learning for proximal caries detection on bitewing x-ray - HUNT4 Oral Health Study. BMC Oral Health 2024; 24:344. [PMID: 38494481 PMCID: PMC10946166 DOI: 10.1186/s12903-024-04120-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 03/07/2024] [Indexed: 03/19/2024] Open
Abstract
BACKGROUND Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the use of artificial intelligence, and in particular deep-learning, has the potential to aid in the diagnosis by providing a quick and informative analysis of the bitewing images. METHODS A dataset of 13,887 bitewings from the HUNT4 Oral Health Study were annotated individually by six different experts, and used to train three different object detection deep-learning architectures: RetinaNet (ResNet50), YOLOv5 (M size), and EfficientDet (D0 and D1 sizes). A consensus dataset of 197 images, annotated jointly by the same six dental clinicians, was used for evaluation. A five-fold cross validation scheme was used to evaluate the performance of the AI models. RESULTS The trained models show an increase in average precision and F1-score, and decrease of false negative rate, with respect to the dental clinicians. When compared against the dental clinicians, the YOLOv5 model shows the largest improvement, reporting 0.647 mean average precision, 0.548 mean F1-score, and 0.149 mean false negative rate. Whereas the best annotators on each of these metrics reported 0.299, 0.495, and 0.164 respectively. CONCLUSION Deep-learning models have shown the potential to assist dental professionals in the diagnosis of caries. Yet, the task remains challenging due to the artifacts natural to the bitewing images.
Collapse
Affiliation(s)
- Javier Pérez de Frutos
- Department of Health Research, SINTEF Digital, Professor Brochs gate 2, Trondheim, 7030, Norway.
| | - Ragnhild Holden Helland
- Department of Health Research, SINTEF Digital, Professor Brochs gate 2, Trondheim, 7030, Norway
| | | | - Line Cathrine Nymoen
- Department of public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Kompetansesenteret Tannhelse Midt (TkMidt), Trondheim, Norway
| | - Thomas Langø
- Department of Health Research, SINTEF Digital, Professor Brochs gate 2, Trondheim, 7030, Norway
| | | | - Abhijit Sen
- Department of public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Kompetansesenteret Tannhelse Midt (TkMidt), Trondheim, Norway
| |
Collapse
|
12
|
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
|
13
|
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
|
14
|
Yoon K, Jeong HM, Kim JW, Park JH, Choi J. AI-based dental caries and tooth number detection in intraoral photos: Model development and performance evaluation. J Dent 2024; 141:104821. [PMID: 38145804 DOI: 10.1016/j.jdent.2023.104821] [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: 10/09/2023] [Revised: 12/17/2023] [Accepted: 12/18/2023] [Indexed: 12/27/2023] Open
Abstract
OBJECTIVES In this study, we aimed to integrate tooth number recognition and caries detection in full intraoral photographic images using a cascade region-based deep convolutional neural network (R-CNN) model to facilitate the practical application of artificial intelligence (AI)-driven automatic caries detection in clinical practice. METHODS Our dataset comprised 24,578 images, encompassing 4787 upper occlusal, 4347 lower occlusal, 5230 right lateral, 5010 left lateral, and 5204 frontal views. In each intraoral image, tooth numbers and, when present, dental caries, including their location and stage, were annotated using bounding boxes. A cascade R-CNN model was used for dental caries detection and tooth number recognition within intraoral images. RESULTS For tooth number recognition, the model achieved an average mean average precision (mAP) score of 0.880. In the task of dental caries detection, the model's average mAP score was 0.769, with individual scores spanning from 0.695 to 0.893. CONCLUSIONS The primary objective of integrating tooth number recognition and caries detection within full intraoral photographic images has been achieved by our deep learning model. The model's training on comprehensive intraoral datasets has demonstrated its potential for seamless clinical application. CLINICAL SIGNIFICANCE This research holds clinical significance by achieving AI-driven automatic integration of tooth number recognition and caries detection in full intraoral images where multiple teeth are visible. It has the potential to promote the practical application of AI in real-life and clinical settings.
Collapse
Affiliation(s)
- Kyubaek Yoon
- School of Mechanical Engineering, Yonsei University, Seoul, South Korea
| | - Hye-Min Jeong
- Department of Artificial Intelligence Convergence, Ewha Womans University, Seoul, South Korea
| | - Jin-Woo Kim
- Department of Oral and Maxillofacial Surgery, College of Medicine, Ewha Womans University, Seoul, South Korea
| | - Jung-Hyun Park
- Department of Oral and Maxillofacial Surgery, College of Medicine, Ewha Womans University, Seoul, South Korea.
| | - Jongeun Choi
- School of Mechanical Engineering, Yonsei University, Seoul, South Korea.
| |
Collapse
|
15
|
Kazimierczak N, Kazimierczak W, Serafin Z, Nowicki P, Nożewski J, Janiszewska-Olszowska J. AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review. J Clin Med 2024; 13:344. [PMID: 38256478 PMCID: PMC10816993 DOI: 10.3390/jcm13020344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 12/29/2023] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
The advent of artificial intelligence (AI) in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. Its usage in orthodontic practices worldwide has increased with the availability of various AI applications and tools. This review explores the principles of AI, its applications in orthodontics, and its implementation in clinical practice. A comprehensive literature review was conducted, focusing on AI applications in dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision making, and patient telemonitoring. Due to study heterogeneity, no meta-analysis was possible. AI has demonstrated high efficacy in all these areas, but variations in performance and the need for manual supervision suggest caution in clinical settings. The complexity and unpredictability of AI algorithms call for cautious implementation and regular manual validation. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for successful integration into orthodontic practice.
Collapse
Affiliation(s)
- Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Wojciech Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Paweł Nowicki
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Jakub Nożewski
- Department of Emeregncy Medicine, University Hospital No 2 in Bydgoszcz, Ujejskiego 75, 85-168 Bydgoszcz, Poland
| | | |
Collapse
|
16
|
Tiwari A, Ghosh A, Agrawal PK, Reddy A, Singla D, Mehta DN, Girdhar G, Paiwal K. Artificial intelligence in oral health surveillance among under-served communities. Bioinformation 2023; 19:1329-1335. [PMID: 38415032 PMCID: PMC10895529 DOI: 10.6026/973206300191329] [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: 12/01/2023] [Revised: 12/31/2023] [Accepted: 12/31/2023] [Indexed: 02/29/2024] Open
Abstract
A sizable percentage of the population in India still does not have easy access to dental facilities. Therefore, it is of interest to document the role of artificial intelligence (AI) in oral surveillance of underserved communities. Available data shows that AI makes it possible to screen, diagnose, track, prioritize, and monitor dental patients remotely via smart devices. As a result, dentists won't have to deal with simple situations that only require standard treatments; freeing them up to focus on more complicated cases. Additionally, this would allow dentists to reach a broader, more underprivileged population in difficult-to-reach places. AI fracture recognition and categorization performance has shown promise in preliminary testing. Methods for detecting aberrations are frequently employed in public health practise and research continues to be focused on them.
Collapse
Affiliation(s)
- Anushree Tiwari
- Clinical Quality and Value, American Academy of Orthopaedic Surgeons, Rosemont, USA
| | - Anirbhan Ghosh
- Department of Orthodontics and Dentofacial Orthopedics, Bhabha College of Dental Sciences, Bhopal, M.P., India
| | - Pankaj Kumar Agrawal
- Department of Oral Pathology and Microbiology, Maitri College of Dentistry and Research Centre, Anjora, Durg, Chhattisgarh, India
| | - Arjun Reddy
- Manipal College of Dental Sciences, Manipal, India
| | - Deepika Singla
- Department of Conservative Dentistry and Endodontics, Desh Bhagat Dental College and Hospital, Malout, India
| | - Dhaval Niranjan Mehta
- Department of Oral Medicine and Radiology, Narsinbhai Patel Dental College and Hospital, Sankalchand Patel University, Visnagar, Gujarat, India
| | - Gaurav Girdhar
- Department of Periodontology, Karnavati School of Dentistry Karnavati University, Gandhinagar, Gujarat, India
| | - Kapil Paiwal
- Department of Oral and Maxillofacial Pathology, Daswani Dental College and Research Center, Kota, Rajasthan, India
| |
Collapse
|
17
|
Giannakopoulos K, Kavadella A, Aaqel Salim A, Stamatopoulos V, Kaklamanos EG. Evaluation of the Performance of Generative AI Large Language Models ChatGPT, Google Bard, and Microsoft Bing Chat in Supporting Evidence-Based Dentistry: Comparative Mixed Methods Study. J Med Internet Res 2023; 25:e51580. [PMID: 38009003 PMCID: PMC10784979 DOI: 10.2196/51580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/15/2023] [Accepted: 11/20/2023] [Indexed: 11/28/2023] Open
Abstract
BACKGROUND The increasing application of generative artificial intelligence large language models (LLMs) in various fields, including dentistry, raises questions about their accuracy. OBJECTIVE This study aims to comparatively evaluate the answers provided by 4 LLMs, namely Bard (Google LLC), ChatGPT-3.5 and ChatGPT-4 (OpenAI), and Bing Chat (Microsoft Corp), to clinically relevant questions from the field of dentistry. METHODS The LLMs were queried with 20 open-type, clinical dentistry-related questions from different disciplines, developed by the respective faculty of the School of Dentistry, European University Cyprus. The LLMs' answers were graded 0 (minimum) to 10 (maximum) points against strong, traditionally collected scientific evidence, such as guidelines and consensus statements, using a rubric, as if they were examination questions posed to students, by 2 experienced faculty members. The scores were statistically compared to identify the best-performing model using the Friedman and Wilcoxon tests. Moreover, the evaluators were asked to provide a qualitative evaluation of the comprehensiveness, scientific accuracy, clarity, and relevance of the LLMs' answers. RESULTS Overall, no statistically significant difference was detected between the scores given by the 2 evaluators; therefore, an average score was computed for every LLM. Although ChatGPT-4 statistically outperformed ChatGPT-3.5 (P=.008), Bing Chat (P=.049), and Bard (P=.045), all models occasionally exhibited inaccuracies, generality, outdated content, and a lack of source references. The evaluators noted instances where the LLMs delivered irrelevant information, vague answers, or information that was not fully accurate. CONCLUSIONS This study demonstrates that although LLMs hold promising potential as an aid in the implementation of evidence-based dentistry, their current limitations can lead to potentially harmful health care decisions if not used judiciously. Therefore, these tools should not replace the dentist's critical thinking and in-depth understanding of the subject matter. Further research, clinical validation, and model improvements are necessary for these tools to be fully integrated into dental practice. Dental practitioners must be aware of the limitations of LLMs, as their imprudent use could potentially impact patient care. Regulatory measures should be established to oversee the use of these evolving technologies.
Collapse
Affiliation(s)
| | - Argyro Kavadella
- School of Dentistry, European University Cyprus, Nicosia, Cyprus
| | - Anas Aaqel Salim
- School of Dentistry, European University Cyprus, Nicosia, Cyprus
| | - Vassilis Stamatopoulos
- Information Management Systems Institute, ATHENA Research and Innovation Center, Athens, Greece
| | - Eleftherios G Kaklamanos
- School of Dentistry, European University Cyprus, Nicosia, Cyprus
- School of Dentistry, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| |
Collapse
|
18
|
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
|
19
|
Ghods K, Azizi A, Jafari A, Ghods K. Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature. JOURNAL OF DENTISTRY (SHIRAZ, IRAN) 2023; 24:356-371. [PMID: 38149231 PMCID: PMC10749440 DOI: 10.30476/dentjods.2023.96835.1969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 01/04/2023] [Accepted: 03/05/2023] [Indexed: 12/28/2023]
Abstract
Statement of the Problem In recent years, the use of artificial intelligence (AI) has become increasingly popular in dentistry because it facilitates the process of diagnosis and clinical decision-making. However, AI holds multiple prominent drawbacks, which restrict its wide application today. It is necessary for dentists to be aware of AI's pros and cons before its implementation. Purpose Therefore, the present study was conducted to comprehensively review various applications of AI in all dental branches along with its advantages and disadvantages. Materials and Method For this review article, a complete query was carried out on PubMed and Google Scholar databases and the studies published during 2010-2022 were collected using the keywords "Artificial Intelligence", "Dentistry," "Machine learning," "Deep learning," and "Diagnostic System." Ultimately, 116 relevant articles focused on artificial intelligence in dentistry were selected and evaluated. Results In new research AI applications in detecting dental abnormalities and oral malignancies based on radiographic view and histopathological features, designing dental implants and crowns, determining tooth preparation finishing line, analyzing growth patterns, estimating biological age, predicting the viability of dental pulp stem cells, analyzing the gene expression of periapical lesions, forensic dentistry, and predicting the success rate of treatments, have been mentioned. Despite AI's benefits in clinical dentistry, three controversial challenges including ease of use, financial return on investment, and evidence of performance exist and need to be managed. Conclusion As evidenced by the obtained results, the most crucial progression of AI is in oral malignancies' diagnostic systems. However, AI's newest advancements in various branches of dentistry require further scientific work before being applied to clinical practice. Moreover, the immense use of AI in clinical dentistry is only achievable when its challenges are appropriately managed.
Collapse
Affiliation(s)
- Kimia Ghods
- Student of Dentistry, Membership of Dental Material Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Arash Azizi
- Dept. Oral Medicine, Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran
| | - Aryan Jafari
- Student of Dentistry, Membership of Dental Material Research Center, Tehran
| | - Kian Ghods
- Dept. of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, Canada
| |
Collapse
|
20
|
Turosz N, Chęcińska K, Chęciński M, Brzozowska A, Nowak Z, Sikora M. Applications of artificial intelligence in the analysis of dental panoramic radiographs: an overview of systematic reviews. Dentomaxillofac Radiol 2023; 52:20230284. [PMID: 37665008 PMCID: PMC10552133 DOI: 10.1259/dmfr.20230284] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 09/05/2023] Open
Abstract
OBJECTIVES This overview of systematic reviews aimed to establish the current state of knowledge on the suitability of artificial intelligence (AI) in dental panoramic radiograph analysis and illustrate its changes over time. METHODS Medical databases covered by the Association for Computing Machinery, Bielefeld Academic Search Engine, Google Scholar, and PubMed engines were searched. The risk of bias was assessed using ROBIS tool. Ultimately, 12 articles were qualified for the qualitative synthesis. The results were visualized with timelines, tables, and charts. RESULTS In the years 1988-2023, a significant development of information technologies for the analysis of DPRs was observed. The latest analyzed AI models achieve high accuracy in detecting caries (91.5%), osteoporosis (89.29%), maxillary sinusitis (87.5%), periodontal bone loss (93.09%), and teeth identification and numbering (93.67%). The detection of periapical lesions is also characterized by high sensitivity (99.95%) and specificity (92%). However, due to the small number of heterogeneous source studies synthesized in systematic reviews, the results of this overview should be interpreted with caution. CONCLUSION Currently, AI applications can significantly support dentists in dental panoramic radiograph analysis. As systematic reviews on AI become outdated quickly, their regular updating is recommended. PROSPERO registration number: CRD42023416048.
Collapse
Affiliation(s)
- Natalia Turosz
- Institute of Public Health, Jagiellonian University Medical College, Skawińska, Poland
| | - Kamila Chęcińska
- Department of Glass Technology and Amorphous Coatings, Faculty of Materials Science and Ceramics, AGH University of Science and Technology, Mickiewicza, Poland
| | - Maciej Chęciński
- Department of Oral Surgery, Preventive Medicine Center, Komorowskiego, Poland
| | | | - Zuzanna Nowak
- Department of Temporomandibular Disorders, Medical University of Silesia in Katowice, Katowice, Poland
| | | |
Collapse
|
21
|
Orhan K, Aktuna Belgin C, Manulis D, Golitsyna M, Bayrak S, Aksoy S, Sanders A, Önder M, Ezhov M, Shamshiev M, Gusarev M, Shlenskii V. Determining the reliability of diagnosis and treatment using artificial intelligence software with panoramic radiographs. Imaging Sci Dent 2023; 53:199-208. [PMID: 37799743 PMCID: PMC10548159 DOI: 10.5624/isd.20230109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/07/2023] [Accepted: 07/10/2023] [Indexed: 10/07/2023] Open
Abstract
Purpose The objective of this study was to evaluate the accuracy and effectiveness of an artificial intelligence (AI) program in identifying dental conditions using panoramic radiographs (PRs), as well as to assess the appropriateness of its treatment recommendations. Material and Methods PRs from 100 patients (representing 4497 teeth) with known clinical examination findings were randomly selected from a university database. Three dentomaxillofacial radiologists and the Diagnocat AI software evaluated these PRs. The evaluations were focused on various dental conditions and treatments, including canal filling, caries, cast post and core, dental calculus, fillings, furcation lesions, implants, lack of interproximal tooth contact, open margins, overhangs, periapical lesions, periodontal bone loss, short fillings, voids in root fillings, overfillings, pontics, root fragments, impacted teeth, artificial crowns, missing teeth, and healthy teeth. Results The AI demonstrated almost perfect agreement (exceeding 0.81) in most of the assessments when compared to the ground truth. The sensitivity was very high (above 0.8) for the evaluation of healthy teeth, artificial crowns, dental calculus, missing teeth, fillings, lack of interproximal contact, periodontal bone loss, and implants. However, the sensitivity was low for the assessment of caries, periapical lesions, pontic voids in the root canal, and overhangs. Conclusion Despite the limitations of this study, the synthesized data suggest that AI-based decision support systems can serve as a valuable tool in detecting dental conditions, when used with PR for clinical dental applications.
Collapse
Affiliation(s)
- Kaan Orhan
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
| | - Ceren Aktuna Belgin
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Hatay Mustafa Kemal University, Hatay, Turkey
| | | | | | - Seval Bayrak
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Abant İzzet Baysal University, Bolu, Turkey
| | - Secil Aksoy
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, Nicosia, Cyprus
| | | | - Merve Önder
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
| | | | | | | | | |
Collapse
|
22
|
Shafi I, Sajad M, Fatima A, Aray DG, Lipari V, Diez IDLT, Ashraf I. Teeth Lesion Detection Using Deep Learning and the Internet of Things Post-COVID-19. SENSORS (BASEL, SWITZERLAND) 2023; 23:6837. [PMID: 37571620 PMCID: PMC10422255 DOI: 10.3390/s23156837] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 07/26/2023] [Accepted: 07/29/2023] [Indexed: 08/13/2023]
Abstract
With a view of the post-COVID-19 world and probable future pandemics, this paper presents an Internet of Things (IoT)-based automated healthcare diagnosis model that employs a mixed approach using data augmentation, transfer learning, and deep learning techniques and does not require physical interaction between the patient and physician. Through a user-friendly graphic user interface and availability of suitable computing power on smart devices, the embedded artificial intelligence allows the proposed model to be effectively used by a layperson without the need for a dental expert by indicating any issues with the teeth and subsequent treatment options. The proposed method involves multiple processes, including data acquisition using IoT devices, data preprocessing, deep learning-based feature extraction, and classification through an unsupervised neural network. The dataset contains multiple periapical X-rays of five different types of lesions obtained through an IoT device mounted within the mouth guard. A pretrained AlexNet, a fast GPU implementation of a convolutional neural network (CNN), is fine-tuned using data augmentation and transfer learning and employed to extract the suitable feature set. The data augmentation avoids overtraining, whereas accuracy is improved by transfer learning. Later, support vector machine (SVM) and the K-nearest neighbors (KNN) classifiers are trained for lesion classification. It was found that the proposed automated model based on the AlexNet extraction mechanism followed by the SVM classifier achieved an accuracy of 98%, showing the effectiveness of the presented approach.
Collapse
Affiliation(s)
- Imran Shafi
- College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (I.S.); (A.F.)
| | - Muhammad Sajad
- Abasyn University Islamabad Campus, Islamabad 44000, Pakistan;
| | - Anum Fatima
- College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (I.S.); (A.F.)
| | - Daniel Gavilanes Aray
- Higher Polytechnic School, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain; (D.G.A.); (V.L.)
- Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Fundación Universitaria Internacional de Colombia Bogotá, Bogotá 11131, Colombia
| | - Vivían Lipari
- Higher Polytechnic School, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain; (D.G.A.); (V.L.)
- Universidad Internacional Iberoamericana Arecibo, Puerto Rico, PR 00613, USA
- Universidade Internacional do Cuanza, Cuito EN250, Bié, Angola
| | - Isabel de la Torre Diez
- Department of Signal Theory, Communications and Telematics Engineering, Unviersity of Valladolid, Paseo de Belén, 15, 47011 Valladolid, Spain
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
| |
Collapse
|
23
|
Park WS, Huh JK, Lee JH. Automated deep learning for classification of dental implant radiographs using a large multi-center dataset. Sci Rep 2023; 13:4862. [PMID: 36964171 PMCID: PMC10039053 DOI: 10.1038/s41598-023-32118-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 03/22/2023] [Indexed: 03/26/2023] Open
Abstract
This study aimed to evaluate the accuracy of automated deep learning (DL) algorithm for identifying and classifying various types of dental implant systems (DIS) using a large-scale multicenter dataset. Dental implant radiographs of pos-implant surgery were collected from five college dental hospitals and 10 private dental clinics, and validated by the National Information Society Agency and the Korean Academy of Oral and Maxillofacial Implantology. The dataset contained a total of 156,965 panoramic and periapical radiographic images and comprised 10 manufacturers and 27 different types of DIS. The accuracy, precision, recall, F1 score, and confusion matrix were calculated to evaluate the classification performance of the automated DL algorithm. The performance metrics of the automated DL based on accuracy, precision, recall, and F1 score for 116,756 panoramic and 40,209 periapical radiographic images were 88.53%, 85.70%, 82.30%, and 84.00%, respectively. Using only panoramic images, the DL algorithm achieved 87.89% accuracy, 85.20% precision, 81.10% recall, and 83.10% F1 score, whereas the corresponding values using only periapical images achieved 86.87% accuracy, 84.40% precision, 81.70% recall, and 83.00% F1 score, respectively. Within the study limitations, automated DL shows a reliable classification accuracy based on large-scale and comprehensive datasets. Moreover, we observed no statistically significant difference in accuracy performance between the panoramic and periapical images. The clinical feasibility of the automated DL algorithm requires further confirmation using additional clinical datasets.
Collapse
Affiliation(s)
- Won-Se Park
- Korean Academy of Oral and Maxillofacial Implantology (KAOMI) Implant Research Institute, Seoul, Korea
- Department of Advanced General Dentistry, Yonsei University College of Dentistry, Seoul, Korea
| | - Jong-Ki Huh
- Korean Academy of Oral and Maxillofacial Implantology (KAOMI) Implant Research Institute, Seoul, Korea.
- Department of Oral and Maxillofacial Surgery, Gangnam Severance Hospital, Yonsei University College of Dentistry, 211 Eonju-ro, Gangnam-gu, Seoul, 06273, Korea.
| | - Jae-Hong Lee
- Korean Academy of Oral and Maxillofacial Implantology (KAOMI) Implant Research Institute, Seoul, Korea.
- Department of Periodontology, College of Dentistry and Institute of Oral Bioscience, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju, 54896, Korea.
| |
Collapse
|
24
|
Kim C, Jeong H, Park W, Kim D. Tooth-Related Disease Detection System Based on Panoramic Images and Optimization Through Automation: Development Study. JMIR Med Inform 2022; 10:e38640. [DOI: 10.2196/38640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/11/2022] [Accepted: 08/11/2022] [Indexed: 11/07/2022] Open
Abstract
Background
Early detection of tooth-related diseases in patients plays a key role in maintaining their dental health and preventing future complications. Since dentists are not overly attentive to tooth-related diseases that may be difficult to judge visually, many patients miss timely treatment. The 5 representative tooth-related diseases, that is, coronal caries or defect, proximal caries, cervical caries or abrasion, periapical radiolucency, and residual root can be detected on panoramic images. In this study, a web service was constructed for the detection of these diseases on panoramic images in real time, which helped shorten the treatment planning time and reduce the probability of misdiagnosis.
Objective
This study designed a model to assess tooth-related diseases in panoramic images by using artificial intelligence in real time. This model can perform an auxiliary role in the diagnosis of tooth-related diseases by dentists and reduce the treatment planning time spent through telemedicine.
Methods
For learning the 5 tooth-related diseases, 10,000 panoramic images were modeled: 4206 coronal caries or defects, 4478 proximal caries, 6920 cervical caries or abrasion, 8290 periapical radiolucencies, and 1446 residual roots. To learn the model, the fast region-based convolutional network (Fast R-CNN), residual neural network (ResNet), and inception models were used. Learning about the 5 tooth-related diseases completely did not provide accurate information on the diseases because of indistinct features present in the panoramic pictures. Therefore, 1 detection model was applied to each tooth-related disease, and the models for each of the diseases were integrated to increase accuracy.
Results
The Fast R-CNN model showed the highest accuracy, with an accuracy of over 90%, in diagnosing the 5 tooth-related diseases. Thus, Fast R-CNN was selected as the final judgment model as it facilitated the real-time diagnosis of dental diseases that are difficult to judge visually from radiographs and images, thereby assisting the dentists in their treatment plans.
Conclusions
The Fast R-CNN model showed the highest accuracy in the real-time diagnosis of dental diseases and can therefore play an auxiliary role in shortening the treatment planning time after the dentists diagnose the tooth-related disease. In addition, by updating the captured panoramic images of patients on the web service developed in this study, we are looking forward to increasing the accuracy of diagnosing these 5 tooth-related diseases. The dental diagnosis system in this study takes 2 minutes for diagnosing 5 diseases in 1 panoramic image. Therefore, this system plays an effective role in setting a dental treatment schedule.
Collapse
|
25
|
Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. Int J Med Inform 2022; 166:104855. [PMID: 35998421 PMCID: PMC9551134 DOI: 10.1016/j.ijmedinf.2022.104855] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/01/2022] [Accepted: 08/11/2022] [Indexed: 12/04/2022]
Abstract
BACKGROUND Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people's health. It is necessary to assess the current status on the application of AI towards the improvement of people's health in the domains defined by WHO's Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges. OBJECTIVE To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people's health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality. METHODS A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO's PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist. RESULTS The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews. CONCLUSION Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.
Collapse
Affiliation(s)
- Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Aida Saez-Saez
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark.
| |
Collapse
|
26
|
Bayrakdar IS, Orhan K, Akarsu S, Çelik Ö, Atasoy S, Pekince A, Yasa Y, Bilgir E, Sağlam H, Aslan AF, Odabaş A. Deep-learning approach for caries detection and segmentation on dental bitewing radiographs. Oral Radiol 2022; 38:468-479. [PMID: 34807344 DOI: 10.1007/s11282-021-00577-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/09/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVES The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer. METHODS A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University. VGG-16 and U-Net implemented with PyTorch models were used for the detection and segmentation of caries lesions, respectively. RESULTS The sensitivity, precision, and F-measure rates for caries detection and caries segmentation were 0.84, 0.81; 0.84, 0.86; and 0.84, 0.84, respectively. Comparing to 5 different experienced observers and AI models on external radiographic dataset, AI models showed superiority to assistant specialists. CONCLUSION CNN-based AI algorithms can have the potential to detect and segmentation of dental caries accurately and effectively in bitewing radiographs. AI algorithms based on the deep-learning method have the potential to assist clinicians in routine clinical practice for quickly and reliably detecting the tooth caries. The use of these algorithms in clinical practice can provide to important benefit to physicians as a clinical decision support system in dentistry.
Collapse
Affiliation(s)
- Ibrahim Sevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey.
- Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir, Turkey.
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
- Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey
| | - Serdar Akarsu
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Özer Çelik
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
- Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey
| | - Samet Atasoy
- Department of Restorative Dentistry, Faculty of Dentistry, Ordu University, Ordu, Turkey
| | - Adem Pekince
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Karabuk University, Karabuk, Turkey
| | - Yasin Yasa
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ordu University, Ordu, Turkey
| | - Elif Bilgir
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey
| | - Hande Sağlam
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey
| | - Ahmet Faruk Aslan
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Alper Odabaş
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| |
Collapse
|
27
|
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
|
28
|
Islam NM, Laughter L, Sadid-Zadeh R, Smith C, Dolan TA, Crain G, Squarize CH. Adopting artificial intelligence in dental education: A model for academic leadership and innovation. J Dent Educ 2022; 86:1545-1551. [PMID: 35781809 DOI: 10.1002/jdd.13010] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 03/25/2022] [Accepted: 05/28/2022] [Indexed: 11/06/2022]
Abstract
INTRODUCTION The continual evolution of dental education, dental practice and the delivery of optimal oral health care is rooted in the practice of leadership. This paper explores opportunities and challenges facing dental education with a specific focus on incorporating the use of artificial intelligence (AI). METHODS Using the model in Bolman and Deal's Reframing Organizations, the Four Frames model serves as a road map for building infrastructure within dental schools for the adoption of AI. CONCLUSION AI can complement and boost human tasks and have a far-reaching impact in academia and health care. Its adoption could enhance educational experiences and the delivery of care, and support current functions and future innovation. The framework suggested in this paper, while specific to AI, could be adapted and applied to a myriad of innovations and new organizational ideals and goals within institutions of dental education.
Collapse
Affiliation(s)
- Nadim M Islam
- Department of Oral and Maxillofacial Diagnostic Sciences, University of Florida College of Dentistry, Gainesville, Florida, USA
| | - Lory Laughter
- Department of Periodontics, University of the Pacific, San Francisco, California, USA
| | - Ramtin Sadid-Zadeh
- Department of Restorative Dentistry and Digital Technologies, University at Buffalo School of Dental Medicine, Buffalo, New York, USA
| | - Carlos Smith
- Dental Public Health and Policy, Virginia Commonwealth University School of Dentistry, Richmond, Virginia, USA
| | - Teresa A Dolan
- Chief Dental Officer, Overjet AI, Boston, Massachusetts, USA
| | - Geralyn Crain
- College of Dental Medicine, Roseman University of Health Sciences, South Jordan, Utah, USA
| | - Cristiane H Squarize
- Laboratory of Epithelial Biology, Department of Periodontics and Oral Medicine, University of Michigan School of Dentistry, Ann Arbor, Michigan, USA
| |
Collapse
|
29
|
Deep Learning Application in Dental Caries Detection Using Intraoral Photos Taken by Smartphones. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115504] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
A mobile-phone-based diagnostic tool, which most of the population can easily access, could be a game changer in increasing the number of examinations of people with dental caries. This study aimed to apply a deep learning algorithm in diagnosing the stages of smooth surface caries via smartphone images. Materials and methods: A training dataset consisting of 1902 photos of the smooth surface of teeth taken with an iPhone 7 from 695 people was used. Four deep learning models, consisting of Faster Region-Based Convolutional Neural Networks (Faster R-CNNs), You Only Look Once version 3 (YOLOv3), RetinaNet, and Single-Shot Multi-Box Detector (SSD), were tested to detect initial caries lesions and cavities. The reference standard was the diagnosis of a dentist based on image examination according to the International Caries Classification and Management System (ICCMS) classification. Results: For cavitated caries, YOLOv3 and Faster R-CNN showed the highest sensitivity among the four tested models, at 87.4% and 71.4%, respectively. The sensitivity levels of these two models were only 36.9 % and 26% for visually non-cavitated (VNC). The specificity of the four models reached above 86% for cavitated caries and above 71% for VNC. Conclusion: The clinical application of YOLOv3 and Faster R-CNN models for diagnosing dental caries via smartphone images was promising. The current study provides a preliminary insight into the potential translation of AI from the laboratory to clinical practice.
Collapse
|
30
|
Momeni-Moghaddam M, Hashemi C, Fathi A, Khamesipour F. Diagnostic accuracy, available treatment, and diagnostic methods of dental caries in practice: a meta-analysis. BENI-SUEF UNIVERSITY JOURNAL OF BASIC AND APPLIED SCIENCES 2022. [DOI: 10.1186/s43088-022-00243-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Abstract
Background
Diagnosis of dental caries and identification of patients with dental caries is the biggest challenge in dentistry. For this diagnostic accuracy, several methods are studied. The present study attempts to re-study the published data in the last 50 years, between 1960 and 2020.
Main body
Based on designed keywords, we made a thorough search of 4 different databases and found 3887 articles after removing the duplicate. The included database was PubMed, Ovid, Web of Science, and Cochrane library. On keen screening of the articles, we included 19 articles in the review. All the articles were analyzed based on the Cochrane risk assessment method. Maximum studies of up to 80% of caries management are based on children from 1 to 10 years of age. About 47% of articles were found based on reported use of drugs against dental caries, whereas 52.6% of articles were based on the behavioral and socio-demographic study of the mother and caretakers. We found that attentive parents and caretakers of the children can help in reducing the prevention of caries. Frese et al. (Sci Rep. 8(1):16991, 2018. 10.1038/s41598-018-34777-x), Liu et al. (PLoS ONE 8(11):e78723, 2013. 10.1371/journal.pone.0078723), and Innes et al. J Dent Res 99(1):36–43, 2020. 10.1177/0022034519888882) were the studied articles with high quality and low bias risk. These methods were based on the use of stannous fluoride for dental caries, the study of the effect of smoking on older adults, by checking the anxiety level of the participants.
Short conclusions
Tooth decay is a common condition in the general population and affects mostly children. The method with high accuracy and low risk can be recommended for routine treatment.
Collapse
|
31
|
Lee S, Kim D, Jeong HG. Detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence. Sci Rep 2022; 12:5172. [PMID: 35338198 PMCID: PMC8956729 DOI: 10.1038/s41598-022-09083-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 02/23/2022] [Indexed: 12/27/2022] Open
Abstract
Panoramic dental radiography is one of the most common examinations performed in dental clinics. Compared with other dental images, it covers a wide area from individual teeth to the maxilla and mandibular area. Dental clinicians can get much information about patients’ health. However, it is time-consuming and laborious to detect all signs of anomalies because these regions are very complicated. So it is needed to filter out healthy images to save clinicians’ time to examine. For this, we applied modern artificial intelligence-based computer vision techniques. In this study, we built a model to detect 17 fine-grained dental anomalies which are critical to patients’ dental health and quality of life. We used about 23,000 anonymized panoramic dental images taken from local dental clinics from July 2020 to July 2021. Our model can detect these abnormal signs and filter out normal images with high sensitivity of about 0.99. The result indicates that our model can be used in real clinical practice to alleviate the burden of clinicians.
Collapse
Affiliation(s)
- Sangyeon Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea.
| | - Donghyun Kim
- InVisionLab, Seoul, 05854, Republic of Korea.,Department of Advanced General Dentistry, Yeonsei University, Seoul, 03722, Republic of Korea
| | | |
Collapse
|
32
|
Ossowska A, Kusiak A, Świetlik D. Artificial Intelligence in Dentistry-Narrative Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19063449. [PMID: 35329136 PMCID: PMC8950565 DOI: 10.3390/ijerph19063449] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/03/2022] [Accepted: 03/11/2022] [Indexed: 12/21/2022]
Abstract
Nowadays, artificial intelligence (AI) is becoming more important in medicine and in dentistry. It can be helpful in many fields where the human may be assisted and helped by new technologies. Neural networks are a part of artificial intelligence, and are similar to the human brain in their work and can solve given problems and make fast decisions. This review shows that artificial intelligence and the use of neural networks has developed very rapidly in recent years, and it may be an ordinary tool in modern dentistry in the near future. The advantages of this process are better efficiency, accuracy, and time saving during the diagnosis and treatment planning. More research and improvements are needed in the use of neural networks in dentistry to put them into daily practice and to facilitate the work of the dentist.
Collapse
Affiliation(s)
- Agata Ossowska
- Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdańsk, 80-204 Gdańsk, Poland;
| | - Aida Kusiak
- Department of Biostatistics and Neural Networks, Medical University of Gdańsk, 80-211 Gdańsk, Poland;
| | - Dariusz Świetlik
- Department of Biostatistics and Neural Networks, Medical University of Gdańsk, 80-211 Gdańsk, Poland;
- Correspondence:
| |
Collapse
|
33
|
Zaborowicz K, Garbowski T, Biedziak B, Zaborowicz M. Robust Estimation of the Chronological Age of Children and Adolescents Using Tooth Geometry Indicators and POD-GP. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19052952. [PMID: 35270645 PMCID: PMC8910714 DOI: 10.3390/ijerph19052952] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/17/2022] [Accepted: 03/02/2022] [Indexed: 01/27/2023]
Abstract
Determining the chronological age of children or adolescents is becoming an extremely necessary and important issue. Correct age-assessment methods are especially important in the process of international adoption and in the case of immigrants without valid documents confirming their identity. It is well known that traditional, analog methods widely used in clinical evaluation are burdened with a high error rate and are characterized by low accuracy. On the other hand, new digital approaches appear in medicine more and more often, which allow the increase of the accuracy of these estimates, and thus equip doctors with a tool for reliable estimation of the chronological age of children and adolescents. In this study, the work on a fast and effective metamodel is continued. Metamodels have one great advantage over all other analog and quasidigital methods—if they are well trained, a priori, on a representative set of samples, then in the age-assessment phase, results are obtained in a fraction of a second and with little error (reduced to ±7.5 months). In the here-proposed method, the standard deviation for each estimate is additionally obtained, which allows the assessment of the certainty of each result. In this study, 619 pantomographic photos of 619 patients (296 girls and 323 boys) of different ages were used. In the numerical procedure, on the other hand, a metamodel based on the Proper Orthogonal Decomposition (POD) and Gaussian processes (GP) were utilized. The accuracy of the trained model was up to 95%.
Collapse
Affiliation(s)
- Katarzyna Zaborowicz
- Department of Orthodontics and Craniofacial Anomalies, Poznań University of Medical Sciences, Collegium Maius, Fredry 10, 61-701 Poznan, Poland;
- Correspondence:
| | - Tomasz Garbowski
- Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-627 Poznan, Poland; (T.G.); (M.Z.)
| | - Barbara Biedziak
- Department of Orthodontics and Craniofacial Anomalies, Poznań University of Medical Sciences, Collegium Maius, Fredry 10, 61-701 Poznan, Poland;
| | - Maciej Zaborowicz
- Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-627 Poznan, Poland; (T.G.); (M.Z.)
| |
Collapse
|
34
|
Deep Learning for Caries Detection: A Systematic Review. J Dent 2022; 122:104115. [DOI: 10.1016/j.jdent.2022.104115] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 03/24/2022] [Accepted: 03/28/2022] [Indexed: 12/21/2022] Open
|
35
|
Rashid U, Javid A, Khan AR, Liu L, Ahmed A, Khalid O, Saleem K, Meraj S, Iqbal U, Nawaz R. A hybrid mask RCNN-based tool to localize dental cavities from real-time mixed photographic images. PeerJ Comput Sci 2022; 8:e888. [PMID: 35494840 PMCID: PMC9044255 DOI: 10.7717/peerj-cs.888] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
Nearly 3.5 billion humans have oral health issues, including dental caries, which requires dentist-patient exposure in oral examinations. The automated approaches identify and locate carious regions from dental images by localizing and processing either colored photographs or X-ray images taken via specialized dental photography cameras. The dentists' interpretation of carious regions is difficult since the detected regions are masked using solid coloring and limited to a particular dental image type. The software-based automated tools to localize caries from dental images taken via ordinary cameras requires further investigation. This research provided a mixed dataset of dental photographic (colored or X-ray) images, instantiated a deep learning approach to enhance the existing dental image carious regions' localization procedure, and implemented a full-fledged tool to present carious regions via simple dental images automatically. The instantiation mainly exploits the mixed dataset of dental images (colored photographs or X-rays) collected from multiple sources and pre-trained hybrid Mask RCNN to localize dental carious regions. The evaluations performed by the dentists showed that the correctness of annotated datasets is up to 96%, and the accuracy of the proposed system is between 78% and 92%. Moreover, the system achieved the overall satisfaction level of dentists above 80%.
Collapse
Affiliation(s)
- Umer Rashid
- Department of Computer Science, Quaid-e-Azam University, Islamabad, Pakistan
| | - Aiman Javid
- Department of Computer Science, Quaid-e-Azam University, Islamabad, Pakistan
| | - Abdur Rehman Khan
- Department of Computer Science, Quaid-e-Azam University, Islamabad, Pakistan
| | - Leo Liu
- School of Business and Law, The Manchester Metropolitan University, Manchester, United Kingdom
| | - Adeel Ahmed
- Department of Computer Science, Quaid-e-Azam University, Islamabad, Pakistan
| | - Osman Khalid
- Department of Computer Science, COMSATS University, Islamabad, Pakistan
| | - Khalid Saleem
- Department of Computer Science, Quaid-e-Azam University, Islamabad, Pakistan
| | - Shaista Meraj
- Department of Radiology, Bolton NHS Foundation Trust, Bolton, United Kingdom
| | - Uzair Iqbal
- Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad Chiniot-Faisalabad, Pakistan
| | - Raheel Nawaz
- School of Business and Law, The Manchester Metropolitan University, Manchester, United Kingdom
| |
Collapse
|
36
|
Artificial Intelligence Application in Assessment of Panoramic Radiographs. Diagnostics (Basel) 2022; 12:diagnostics12010224. [PMID: 35054390 PMCID: PMC8774336 DOI: 10.3390/diagnostics12010224] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/12/2022] [Accepted: 01/14/2022] [Indexed: 12/04/2022] Open
Abstract
The aim of this study was to assess the reliability of the artificial intelligence (AI) automatic evaluation of panoramic radiographs (PRs). Thirty PRs, covering at least six teeth with the possibility of assessing the marginal and apical periodontium, were uploaded to the Diagnocat (LLC Diagnocat, Moscow, Russia) account, and the radiologic report of each was generated as the basis of automatic evaluation. The same PRs were manually evaluated by three independent evaluators with 12, 15, and 28 years of experience in dentistry, respectively. The data were collected in such a way as to allow statistical analysis with SPSS Statistics software (IBM, Armonk, NY, USA). A total of 90 reports were created for 30 PRs. The AI protocol showed very high specificity (above 0.9) in all assessments compared to ground truth except from periodontal bone loss. Statistical analysis showed a high interclass correlation coefficient (ICC > 0.75) for all interevaluator assessments, proving the good credibility of the ground truth and the reproducibility of the reports. Unacceptable reliability was obtained for caries assessment (ICC = 0.681) and periapical lesions assessment (ICC = 0.619). The tested AI system can be helpful as an initial evaluation of screening PRs, giving appropriate credibility reports and suggesting additional diagnostic methods for more accurate evaluation if needed.
Collapse
|
37
|
Carrillo-Perez F, Pecho OE, Morales JC, Paravina RD, Della Bona A, Ghinea R, Pulgar R, Pérez MDM, Herrera LJ. Applications of artificial intelligence in dentistry: A comprehensive review. J ESTHET RESTOR DENT 2021; 34:259-280. [PMID: 34842324 DOI: 10.1111/jerd.12844] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 09/30/2021] [Accepted: 11/09/2021] [Indexed: 12/25/2022]
Abstract
OBJECTIVE To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research. MATERIALS AND METHODS The comprehensive review was conducted in MEDLINE/PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years. RESULTS Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling. CONCLUSIONS The insight provided by the present work has reported outstanding results in the design of high-performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations. CLINICAL SIGNIFICANCE The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.
Collapse
Affiliation(s)
- Francisco Carrillo-Perez
- Department of Computer Architecture and Technology, E.T.S.I.I.T.-C.I.T.I.C. University of Granada, Granada, Spain
| | - Oscar E Pecho
- Post-Graduate Program in Dentistry, Dental School, University of Passo Fundo, Passo Fundo, Brazil
| | - Juan Carlos Morales
- Department of Computer Architecture and Technology, E.T.S.I.I.T.-C.I.T.I.C. University of Granada, Granada, Spain
| | - Rade D Paravina
- Department of Restorative Dentistry and Prosthodontics, School of Dentistry, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Alvaro Della Bona
- Post-Graduate Program in Dentistry, Dental School, University of Passo Fundo, Passo Fundo, Brazil
| | - Razvan Ghinea
- Department of Optics, Faculty of Science, University of Granada, Granada, Spain
| | - Rosa Pulgar
- Department of Stomatology, Campus Cartuja, University of Granada, Granada, Spain
| | - María Del Mar Pérez
- Department of Optics, Faculty of Science, University of Granada, Granada, Spain
| | - Luis Javier Herrera
- Department of Computer Architecture and Technology, E.T.S.I.I.T.-C.I.T.I.C. University of Granada, Granada, Spain
| |
Collapse
|
38
|
Abstract
Dental Caries are one of the most prevalent chronic diseases around the globe. Detecting carious lesions is a challenging task. Conventional computer aided diagnosis and detection methods in the past have heavily relied on the visual inspection of teeth. These methods are only effective on large and clearly visible caries on affected teeth. Conventional methods have been limited in performance due to the complex visual characteristics of dental caries images, which consist of hidden or inaccessible lesions. The early detection of dental caries is an important determinant for treatment and benefits much from the introduction of new tools, such as dental radiography. In this paper, we propose a deep learning-based technique for dental caries detection namely: blob detection. The proposed technique automatically detects hidden and inaccessible dental caries lesions in bitewing radio-graphs. The approach employs data augmentation to increase the number of images in the data set to have a total of 11,114 dental images. Image pre-processing on the data set was through the use of Gaussian blur filters. Image segmentation was handled through thresholding, erosion and dilation morphology, while image boundary detection was achieved through active contours method. Furthermore, the deep learning based network through the sequential model in Keras extracts features from the images through blob detection. Finally, a convexity threshold value of 0.9 is introduced to aid in the classification of caries as either present or not present. The process of detection and classifying dental caries achieved the results of 97% and 96% for the precision and recall values, respectively.
Collapse
|
39
|
Lian L, Zhu T, Zhu F, Zhu H. Deep Learning for Caries Detection and Classification. Diagnostics (Basel) 2021; 11:1672. [PMID: 34574013 PMCID: PMC8469830 DOI: 10.3390/diagnostics11091672] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Revised: 09/08/2021] [Accepted: 09/09/2021] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVES Deep learning methods have achieved impressive diagnostic performance in the field of radiology. The current study aimed to use deep learning methods to detect caries lesions, classify different radiographic extensions on panoramic films, and compare the classification results with those of expert dentists. METHODS A total of 1160 dental panoramic films were evaluated by three expert dentists. All caries lesions in the films were marked with circles, whose combination was defined as the reference dataset. A training and validation dataset (1071) and a test dataset (89) were then established from the reference dataset. A convolutional neural network, called nnU-Net, was applied to detect caries lesions, and DenseNet121 was applied to classify the lesions according to their depths (dentin lesions in the outer, middle, or inner third D1/2/3 of dentin). The performance of the test dataset in the trained nnU-Net and DenseNet121 models was compared with the results of six expert dentists in terms of the intersection over union (IoU), Dice coefficient, accuracy, precision, recall, negative predictive value (NPV), and F1-score metrics. RESULTS nnU-Net yielded caries lesion segmentation IoU and Dice coefficient values of 0.785 and 0.663, respectively, and the accuracy and recall rate of nnU-Net were 0.986 and 0.821, respectively. The results of the expert dentists and the neural network were shown to be no different in terms of accuracy, precision, recall, NPV, and F1-score. For caries depth classification, DenseNet121 showed an overall accuracy of 0.957 for D1 lesions, 0.832 for D2 lesions, and 0.863 for D3 lesions. The recall results of the D1/D2/D3 lesions were 0.765, 0.652, and 0.918, respectively. All metric values, including accuracy, precision, recall, NPV, and F1-score values, were proven to be no different from those of the experienced dentists. CONCLUSION In detecting and classifying caries lesions on dental panoramic radiographs, the performance of deep learning methods was similar to that of expert dentists. The impact of applying these well-trained neural networks for disease diagnosis and treatment decision making should be explored.
Collapse
Affiliation(s)
| | | | - Fudong Zhu
- 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; (L.L.); (T.Z.)
| | - Haihua Zhu
- 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; (L.L.); (T.Z.)
| |
Collapse
|
40
|
Zaborowicz K, Biedziak B, Olszewska A, Zaborowicz M. Tooth and Bone Parameters in the Assessment of the Chronological Age of Children and Adolescents Using Neural Modelling Methods. SENSORS (BASEL, SWITZERLAND) 2021; 21:6008. [PMID: 34577221 PMCID: PMC8473021 DOI: 10.3390/s21186008] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/03/2021] [Accepted: 09/06/2021] [Indexed: 12/13/2022]
Abstract
The analog methods used in the clinical assessment of the patient's chronological age are subjective and characterized by low accuracy. When using those methods, there is a noticeable discrepancy between the chronological age and the age estimated based on relevant scientific studies. Innovations in the field of information technology are increasingly used in medicine, with particular emphasis on artificial intelligence methods. The paper presents research aimed at developing a new, effective methodology for the assessment of the chronological age using modern IT methods. In this paper, a study was conducted to determine the features of pantomographic images that support the determination of metric age, and neural models were produced to support the process of identifying the age of children and adolescents. The whole conducted work was a new methodology of metric age assessment. The result of the conducted study is a set of 21 original indicators necessary for the assessment of the chronological age with the use of computer image analysis and neural modelling, as well as three non-linear models of radial basis function networks (RBF), whose accuracy ranges from 96 to 99%. The result of the research are three neural models that determine the chronological age.
Collapse
Affiliation(s)
- Katarzyna Zaborowicz
- Department of Craniofacial Anomalies, Poznań University of Medical Sciences, Collegium Maius, Fredry 10, 61-701 Poznań, Poland
| | - Barbara Biedziak
- Department of Craniofacial Anomalies, Poznań University of Medical Sciences, Collegium Maius, Fredry 10, 61-701 Poznań, Poland
| | - Aneta Olszewska
- Department of Craniofacial Anomalies, Poznań University of Medical Sciences, Collegium Maius, Fredry 10, 61-701 Poznań, Poland
| | - Maciej Zaborowicz
- Department of Biosystems Engineering, Poznan University of Life Sciences, Wojska Polskiego 50, 60-637 Poznań, Poland
| |
Collapse
|
41
|
Moran M, Faria M, Giraldi G, Bastos L, Oliveira L, Conci A. Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks. SENSORS (BASEL, SWITZERLAND) 2021; 21:5192. [PMID: 34372429 PMCID: PMC8347840 DOI: 10.3390/s21155192] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 07/21/2021] [Accepted: 07/22/2021] [Indexed: 11/30/2022]
Abstract
Dental caries is an extremely common problem in dentistry that affects a significant part of the population. Approximal caries are especially difficult to identify because their position makes clinical analysis difficult. Radiographic evaluation-more specifically, bitewing images-are mostly used in such cases. However, incorrect interpretations may interfere with the diagnostic process. To aid dentists in caries evaluation, computational methods and tools can be used. In this work, we propose a new method that combines image processing techniques and convolutional neural networks to identify approximal dental caries in bitewing radiographic images and classify them according to lesion severity. For this study, we acquired 112 bitewing radiographs. From these exams, we extracted individual tooth images from each exam, applied a data augmentation process, and used the resulting images to train CNN classification models. The tooth images were previously labeled by experts to denote the defined classes. We evaluated classification models based on the Inception and ResNet architectures using three different learning rates: 0.1, 0.01, and 0.001. The training process included 2000 iterations, and the best results were achieved by the Inception model with a 0.001 learning rate, whose accuracy on the test set was 73.3%. The results can be considered promising and suggest that the proposed method could be used to assist dentists in the evaluation of bitewing images, and the definition of lesion severity and appropriate treatments.
Collapse
Affiliation(s)
- Maira Moran
- Policlínica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil; (M.F.); (L.B.); (L.O.)
- Instituto de Computação, Universidade Federal Fluminense, Niterói 24210-310, Brazil
| | - Marcelo Faria
- Policlínica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil; (M.F.); (L.B.); (L.O.)
- Faculdade de Odontologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21941-617, Brazil
| | - Gilson Giraldi
- Laboratório Nacional de Computação Científica, Petrópolis 25651-076, Brazil;
| | - Luciana Bastos
- Policlínica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil; (M.F.); (L.B.); (L.O.)
| | - Larissa Oliveira
- Policlínica Piquet Carneiro, Universidade do Estado do Rio de Janeiro, Rio de Janeiro 20950-003, Brazil; (M.F.); (L.B.); (L.O.)
| | - Aura Conci
- Instituto de Computação, Universidade Federal Fluminense, Niterói 24210-310, Brazil
| |
Collapse
|
42
|
Aminoshariae A, Kulild J, Nagendrababu V. Artificial Intelligence in Endodontics: Current Applications and Future Directions. J Endod 2021; 47:1352-1357. [PMID: 34119562 DOI: 10.1016/j.joen.2021.06.003] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 06/03/2021] [Accepted: 06/03/2021] [Indexed: 01/04/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) has the potential to replicate human intelligence to perform prediction and complex decision making in health care and has significantly increased its presence and relevance in various tasks and applications in dentistry, especially endodontics. The aim of this review was to discuss the current endodontic applications of AI and potential future directions. METHODS Articles that have addressed the applications of AI in endodontics were evaluated for information pertinent to include in this narrative review. RESULTS AI models (eg, convolutional neural networks and/or artificial neural networks) have demonstrated various applications in endodontics such as studying root canal system anatomy, detecting periapical lesions and root fractures, determining working length measurements, predicting the viability of dental pulp stem cells, and predicting the success of retreatment procedures. The future of this technology was discussed in light of helping with scheduling, treating patients, drug-drug interactions, diagnosis with prognostic values, and robotic-assisted endodontic surgery. CONCLUSIONS AI demonstrated accuracy and precision in terms of detection, determination, and disease prediction in endodontics. AI can contribute to the improvement of diagnosis and treatment that can lead to an increase in the success of endodontic treatment outcomes. However, it is still necessary to further verify the reliability, applicability, and cost-effectiveness of AI models before transferring these models into day-to-day clinical practice.
Collapse
Affiliation(s)
- Anita Aminoshariae
- Department of Endodontics, Case School of Dental Medicine, Cleveland, Ohio.
| | - Jim Kulild
- Department of Endodontics, University of Missouri-Kansas City School of Dentistry, Kansas City, Missouri
| | - Venkateshbabu Nagendrababu
- Department of Preventive and Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| |
Collapse
|
43
|
Alauddin MS, Baharuddin AS, Mohd Ghazali MI. The Modern and Digital Transformation of Oral Health Care: A Mini Review. Healthcare (Basel) 2021; 9:healthcare9020118. [PMID: 33503807 PMCID: PMC7912705 DOI: 10.3390/healthcare9020118] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 12/31/2020] [Accepted: 01/12/2021] [Indexed: 12/13/2022] Open
Abstract
Dentistry is a part of the field of medicine which is advocated in this digital revolution. The increasing trend in dentistry digitalization has led to the advancement in computer-derived data processing and manufacturing. This progress has been exponentially supported by the Internet of medical things (IoMT), big data and analytical algorithm, internet and communication technologies (ICT) including digital social media, augmented and virtual reality (AR and VR), and artificial intelligence (AI). The interplay between these sophisticated digital aspects has dramatically changed the healthcare and biomedical sectors, especially for dentistry. This myriad of applications of technologies will not only be able to streamline oral health care, facilitate workflow, increase oral health at a fraction of the current conventional cost, relieve dentist and dental auxiliary staff from routine and laborious tasks, but also ignite participatory in personalized oral health care. This narrative article review highlights recent dentistry digitalization encompassing technological advancement, limitations, challenges, and conceptual theoretical modern approaches in oral health prevention and care, particularly in ensuring the quality, efficiency, and strategic dental care in the modern era of dentistry.
Collapse
Affiliation(s)
- Muhammad Syafiq Alauddin
- Department of Conservative Dentistry and Prosthodontics, Faculty of Dentistry, Universiti Sains Islam Malaysia, Kuala Lumpur 56100, Malaysia
- Correspondence:
| | | | | |
Collapse
|