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Tastan Eroglu Z, Babayigit O, Ozkan Sen D, Ucan Yarkac F. Performance of ChatGPT in classifying periodontitis according to the 2018 classification of periodontal diseases. Clin Oral Investig 2024; 28:407. [PMID: 38951256 PMCID: PMC11217036 DOI: 10.1007/s00784-024-05799-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 06/20/2024] [Indexed: 07/03/2024]
Abstract
OBJECTIVES This study assessed the ability of ChatGPT, an artificial intelligence(AI) language model, to determine the stage, grade, and extent of periodontitis based on the 2018 classification. MATERIALS AND METHODS This study used baseline digital data of 200 untreated periodontitis patients to compare standardized reference diagnoses (RDs) with ChatGPT findings and determine the best criteria for assessing stage and grade. RDs were provided by four experts who examined each case. Standardized texts containing the relevant information for each situation were constructed to query ChatGPT. RDs were compared to ChatGPT's responses. Variables influencing the responses of ChatGPT were evaluated. RESULTS ChatGPT successfully identified the periodontitis stage, grade, and extent in 59.5%, 50.5%, and 84.0% of cases, respectively. Cohen's kappa values for stage, grade and extent were respectively 0.447, 0.284, and 0.652. A multiple correspondence analysis showed high variance between ChatGPT's staging and the variables affecting the stage (64.08%) and low variance between ChatGPT's grading and the variables affecting the grade (42.71%). CONCLUSIONS The present performance of ChatGPT in the classification of periodontitis exhibited a reasonable level. However, it is expected that additional improvements would increase its effectiveness and broaden its range of functionalities (NCT05926999). CLINICAL RELEVANCE Despite ChatGPT's current limitations in accurately classifying periodontitis, it is important to note that the model has not been specifically trained for this task. However, it is expected that with additional improvements, the effectiveness and capabilities of ChatGPT might be enhanced.
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Affiliation(s)
- Zeynep Tastan Eroglu
- Department of Periodontology, Necmettin Erbakan University Faculty of Dentistry, Beyşehir Caddesi, Bağlarbaşı Sk., 42090 Meram, Konya, Turkey.
| | - Osman Babayigit
- Department of Periodontology, Necmettin Erbakan University Faculty of Dentistry, Beyşehir Caddesi, Bağlarbaşı Sk., 42090 Meram, Konya, Turkey
| | - Dilek Ozkan Sen
- Department of Periodontology, Necmettin Erbakan University Faculty of Dentistry, Beyşehir Caddesi, Bağlarbaşı Sk., 42090 Meram, Konya, Turkey
| | - Fatma Ucan Yarkac
- Department of Periodontology, Necmettin Erbakan University Faculty of Dentistry, Beyşehir Caddesi, Bağlarbaşı Sk., 42090 Meram, Konya, Turkey
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Li W, Wang Y, Liu Y. DMAF-Net: deformable multi-scale adaptive fusion network for dental structure detection with panoramic radiographs. Dentomaxillofac Radiol 2024; 53:296-307. [PMID: 38518093 PMCID: PMC11211679 DOI: 10.1093/dmfr/twae014] [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: 12/27/2023] [Revised: 03/03/2024] [Accepted: 03/19/2024] [Indexed: 03/24/2024] Open
Abstract
OBJECTIVES Panoramic radiography is one of the most commonly used diagnostic modalities in dentistry. Automatic recognition of panoramic radiography helps dentists in decision support. In order to improve the accuracy of the detection of dental structural problems in panoramic radiographs, we have improved the You Only Look Once (YOLO) network and verified the feasibility of this new method in aiding the detection of dental problems. METHODS We propose a Deformable Multi-scale Adaptive Fusion Net (DMAF-Net) to detect 5 types of dental situations (impacted teeth, missing teeth, implants, crown restorations, and root canal-treated teeth) in panoramic radiography by improving the YOLO network. In DMAF-Net, we propose different modules to enhance the feature extraction capability of the network as well as to acquire high-level features at different scales, while using adaptively spatial feature fusion to solve the problem of scale mismatches of different feature layers, which effectively improves the detection performance. In order to evaluate the detection performance of the models, we compare the experimental results of different models in the test set and select the optimal results of the models by calculating the average of different metrics in each category as the evaluation criteria. RESULTS About 1474 panoramic radiographs were divided into training, validation, and test sets in the ratio of 7:2:1. In the test set, the average precision and recall of DMAF-Net are 92.7% and 87.6%, respectively; the mean Average Precision (mAP0.5 and mAP[0.5:0.95]) are 91.8% and 63.7%, respectively. CONCLUSIONS The proposed DMAF-Net model improves existing deep learning models and achieves automatic detection of tooth structure problems in panoramic radiographs. This new method has great potential for new computer-aided diagnostic, teaching, and clinical applications in the future.
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Affiliation(s)
- Wei Li
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yuanjun Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yu Liu
- Department of Radiology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
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Pitchika V, Büttner M, Schwendicke F. Artificial intelligence and personalized diagnostics in periodontology: A narrative review. Periodontol 2000 2024. [PMID: 38927004 DOI: 10.1111/prd.12586] [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: 02/06/2024] [Revised: 04/29/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024]
Abstract
Periodontal diseases pose a significant global health burden, requiring early detection and personalized treatment approaches. Traditional diagnostic approaches in periodontology often rely on a "one size fits all" approach, which may overlook the unique variations in disease progression and response to treatment among individuals. This narrative review explores the role of artificial intelligence (AI) and personalized diagnostics in periodontology, emphasizing the potential for tailored diagnostic strategies to enhance precision medicine in periodontal care. The review begins by elucidating the limitations of conventional diagnostic techniques. Subsequently, it delves into the application of AI models in analyzing diverse data sets, such as clinical records, imaging, and molecular information, and its role in periodontal training. Furthermore, the review also discusses the role of research community and policymakers in integrating personalized diagnostics in periodontal care. Challenges and ethical considerations associated with adopting AI-based personalized diagnostic tools are also explored, emphasizing the need for transparent algorithms, data safety and privacy, ongoing multidisciplinary collaboration, and patient involvement. In conclusion, this narrative review underscores the transformative potential of AI in advancing periodontal diagnostics toward a personalized paradigm, and their integration into clinical practice holds the promise of ushering in a new era of precision medicine for periodontal care.
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Affiliation(s)
- Vinay Pitchika
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, Munich, Germany
| | - Martha Büttner
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Falk Schwendicke
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, Munich, Germany
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Șalgău CA, Morar A, Zgarta AD, Ancuța DL, Rădulescu A, Mitrea IL, Tănase AO. Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review. Ann Biomed Eng 2024:10.1007/s10439-024-03559-0. [PMID: 38884831 DOI: 10.1007/s10439-024-03559-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 06/05/2024] [Indexed: 06/18/2024]
Abstract
Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes: periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.
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Affiliation(s)
- Cristiana Adina Șalgău
- University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
| | - Anca Morar
- National University of Science and Technology Politehnica Bucharest, Bucharest, Romania.
| | | | - Diana-Larisa Ancuța
- University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
- Cantacuzino National Medical-Military Institute for Research and Development, Bucharest, Romania
| | - Alexandros Rădulescu
- University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
| | - Ioan Liviu Mitrea
- University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
| | - Andrei Ovidiu Tănase
- University of Agronomic Sciences and Veterinary Medicine of Bucharest, Bucharest, Romania
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Tyndall DA, Price JB, Gaalaas L, Spin-Neto R. Surveying the landscape of diagnostic imaging in dentistry's future: Four emerging technologies with promise. J Am Dent Assoc 2024; 155:364-378. [PMID: 38520421 DOI: 10.1016/j.adaj.2024.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 01/04/2024] [Accepted: 01/07/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Advances in digital radiography for both intraoral and panoramic imaging and cone-beam computed tomography have led the way to an increase in diagnostic capabilities for the dental care profession. In this article, the authors provide information on 4 emerging technologies with promise. TYPES OF STUDIES REVIEWED The authors feature the following: artificial intelligence in the form of deep learning using convolutional neural networks, dental magnetic resonance imaging, stationary intraoral tomosynthesis, and second-generation cone-beam computed tomography sources based on carbon nanotube technology and multispectral imaging. The authors review and summarize articles featuring these technologies. RESULTS The history and background of these emerging technologies are previewed along with their development and potential impact on the practice of dental diagnostic imaging. The authors conclude that these emerging technologies have the potential to have a substantial influence on the practice of dentistry as these systems mature. The degree of influence most likely will vary, with artificial intelligence being the most influential of the 4. CONCLUSIONS AND PRACTICAL IMPLICATIONS The readers are informed about these emerging technologies and the potential effects on their practice going forward, giving them information on which to base decisions on adopting 1 or more of these technologies. The 4 technologies reviewed in this article have the potential to improve imaging diagnostics in dentistry thereby leading to better patient care and heightened professional satisfaction.
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Bonfanti-Gris M, Herrera A, Paraíso-Medina S, Alonso-Calvo R, Martínez-Rus F, Pradíes G. Performance evaluation of three versions of a convolutional neural network for object detection and segmentation using a multiclass and reduced panoramic radiograph dataset. J Dent 2024; 144:104891. [PMID: 38367827 DOI: 10.1016/j.jdent.2024.104891] [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: 08/20/2023] [Revised: 02/12/2024] [Accepted: 02/15/2024] [Indexed: 02/19/2024] Open
Abstract
OBJECTIVES To evaluate the diagnostic performance of three versions of a deep-learning convolutional neural network in terms of object detection and segmentation using a multiclass panoramic radiograph dataset. METHODS A total of 600 orthopantomographies were randomly selected for this study and manually annotated by a single operator using an image annotation tool (COCO Annotator v.11.0.1) to establish ground truth. The annotation classes included teeth, maxilla, mandible, inferior alveolar nerve, dento- and implant-supported crowns/pontics, endodontic treatment, resin-based restorations, metallic restorations, and implants. The dataset was then divided into training, validation, and testing subsets, which were used to train versions 5, 7, and 8 of You Only Look Once (YOLO) Neural Network. Results were stored, and a posterior performance analysis was carried out by calculating the precision (P), recall (R), F1 Score, Intersection over Union (IoU), and mean average precision (mAP) at 0.5 and 0.5-0.95 thresholds. The confusion matrix and recall precision graphs were also sketched. RESULTS YOLOv5s showed an improvement in object detection results with an average R = 0.634, P = 0.781, mAP0.5 = 0.631, and mAP0.5-0.95 = 0.392. YOLOv7m achieved the best object detection results with average R = 0.793, P = 0.779, mAP0.5 = 0.740, and mAP0.5-0.95 = 0,481. For object segmentation, YOLOv8m obtained the best average results (R = 0.589, P = 0.755, mAP0.5 = 0.591, and mAP0.5-0.95 = 0.272). CONCLUSIONS YOLOv7m was better suited for object detection, while YOLOv8m demonstrated superior performance in object segmentation. The most frequent error in object detection was related to background classification. Conversely, in object segmentation, there is a tendency to misclassify True Positives across different dental treatment categories. CLINICAL SIGNIFICANCE General diagnostic and treatment decisions based on panoramic radiographs can be enhanced using new artificial intelligence-based tools. Nevertheless, the reliability of these neural networks should be subjected to training and validation to ensure their generalizability.
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Affiliation(s)
- M Bonfanti-Gris
- Department of Conservative and Prosthetic Dentistry, Faculty of Dentistry, Complutense University of Madrid. Plaza Ramón y Cajal s/n - 28040 Madrid, España
| | - A Herrera
- Department of Conservative and Prosthetic Dentistry, Faculty of Dentistry, Complutense University of Madrid. Plaza Ramón y Cajal s/n - 28040 Madrid, España
| | - S Paraíso-Medina
- Department of Computer Languages and Systems and Software Engineering, Polytechnic University of Madrid. Campus Montegancedo s/n - 28660 Boadilla del Monte, Madrid. Spain
| | - R Alonso-Calvo
- Department of Computer Languages and Systems and Software Engineering, Polytechnic University of Madrid. Campus Montegancedo s/n - 28660 Boadilla del Monte, Madrid. Spain
| | - F Martínez-Rus
- Department of Conservative and Prosthetic Dentistry, Faculty of Dentistry, Complutense University of Madrid. Plaza Ramón y Cajal s/n - 28040 Madrid, España.
| | - G Pradíes
- Department of Conservative and Prosthetic Dentistry, Faculty of Dentistry, Complutense University of Madrid. Plaza Ramón y Cajal s/n - 28040 Madrid, España
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Kurt-Bayrakdar S, Bayrakdar İŞ, Yavuz MB, Sali N, Çelik Ö, Köse O, Uzun Saylan BC, Kuleli B, Jagtap R, Orhan K. Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study. BMC Oral Health 2024; 24:155. [PMID: 38297288 PMCID: PMC10832206 DOI: 10.1186/s12903-024-03896-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/15/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND This retrospective study aimed to develop a deep learning algorithm for the interpretation of panoramic radiographs and to examine the performance of this algorithm in the detection of periodontal bone losses and bone loss patterns. METHODS A total of 1121 panoramic radiographs were used in this study. Bone losses in the maxilla and mandibula (total alveolar bone loss) (n = 2251), interdental bone losses (n = 25303), and furcation defects (n = 2815) were labeled using the segmentation method. In addition, interdental bone losses were divided into horizontal (n = 21839) and vertical (n = 3464) bone losses according to the defect patterns. A Convolutional Neural Network (CNN)-based artificial intelligence (AI) system was developed using U-Net architecture. The performance of the deep learning algorithm was statistically evaluated by the confusion matrix and ROC curve analysis. RESULTS The system showed the highest diagnostic performance in the detection of total alveolar bone losses (AUC = 0.951) and the lowest in the detection of vertical bone losses (AUC = 0.733). The sensitivity, precision, F1 score, accuracy, and AUC values were found as 1, 0.995, 0.997, 0.994, 0.951 for total alveolar bone loss; found as 0.947, 0.939, 0.943, 0.892, 0.910 for horizontal bone losses; found as 0.558, 0.846, 0.673, 0.506, 0.733 for vertical bone losses and found as 0.892, 0.933, 0.912, 0.837, 0.868 for furcation defects (respectively). CONCLUSIONS AI systems offer promising results in determining periodontal bone loss patterns and furcation defects from dental radiographs. This suggests that CNN algorithms can also be used to provide more detailed information such as automatic determination of periodontal disease severity and treatment planning in various dental radiographs.
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Affiliation(s)
- Sevda Kurt-Bayrakdar
- Faculty of Dentistry, Department of Periodontology, Eskisehir Osmangazi University, Eskisehir, 26240, Turkey.
- Division of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS, USA.
| | - İbrahim Şevki Bayrakdar
- Division of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS, USA
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Muhammet Burak Yavuz
- Faculty of Dentistry, Department of Periodontology, Eskisehir Osmangazi University, Eskisehir, 26240, Turkey
| | - Nichal Sali
- Faculty of Dentistry, Department of Periodontology, Eskisehir Osmangazi University, Eskisehir, 26240, Turkey
| | - Özer Çelik
- Faculty of Science, Department of Mathematics and Computer Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Oğuz Köse
- Faculty of Dentistry, Department of Periodontology, Recep Tayyip Erdogan University, Rize, Turkey
| | | | - Batuhan Kuleli
- Faculty of Dentistry, Department of Orthodontics, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Rohan Jagtap
- Division of Oral and Maxillofacial Radiology, Department of Care Planning and Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS, USA
| | - Kaan Orhan
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Ankara University, Ankara, Turkey
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Cerda Mardini D, Cerda Mardini P, Vicuña Iturriaga DP, Ortuño Borroto DR. "Determining the efficacy of a machine learning model for measuring periodontal bone loss". BMC Oral Health 2024; 24:100. [PMID: 38233822 PMCID: PMC10792795 DOI: 10.1186/s12903-023-03819-w] [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: 09/11/2023] [Accepted: 12/21/2023] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND Considering the prevalence of Periodontitis, new tools to help improve its diagnostic workflow could be beneficial. Machine Learning (ML) models have already been used in dentistry to automate radiographic analysis. AIMS To determine the efficacy of an ML model for automatically measuring Periodontal Bone Loss (PBL) in panoramic radiographs by comparing it to dentists. METHODS A dataset of 2010 images with and without PBL was segmented using Label Studio. The dataset was split into n = 1970 images for building a training dataset and n = 40 images for building a testing dataset. We propose a model composed of three components. Firstly, statistical inference techniques find probability functions that best describe the segmented dataset. Secondly, Convolutional Neural Networks extract visual information from the training dataset. Thirdly, an algorithm calculates PBL as a percentage and classifies it in stages. Afterwards, a standardized test compared the model to two radiologists, two periodontists and one general dentist. The test was built using the testing dataset, 40 questions long, done in controlled conditions, with radiologists considered as ground truth. Presence or absence, percentage, and stage of PBL were asked, and time to answer the test was measured in seconds. Diagnostic indices, performance metrics and performance averages were calculated for each participant. RESULTS The model had an acceptable performance for diagnosing light to moderate PBL (weighted sensitivity 0.23, weighted F1-score 0.29) and was able to achieve real-time diagnosis. However, it proved incapable of diagnosing severe PBL (sensitivity, precision, and F1-score = 0). CONCLUSIONS We propose a Machine Learning model that automates the diagnosis of Periodontal Bone Loss in panoramic radiographs with acceptable performance.
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Affiliation(s)
| | - Patricio Cerda Mardini
- Universidad de los Andes, Chile, Facultad de Odontología, Santiago, Chile
- MindsDB, San Francisco, USA
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Guler Ayyildiz B, Karakis R, Terzioglu B, Ozdemir D. Comparison of deep learning methods for the radiographic detection of patients with different periodontitis stages. Dentomaxillofac Radiol 2024; 53:32-42. [PMID: 38214940 PMCID: PMC11003609 DOI: 10.1093/dmfr/twad003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 10/20/2023] [Accepted: 11/06/2023] [Indexed: 01/13/2024] Open
Abstract
OBJECTIVES The objective of this study is to assess the accuracy of computer-assisted periodontal classification bone loss staging using deep learning (DL) methods on panoramic radiographs and to compare the performance of various models and layers. METHODS Panoramic radiographs were diagnosed and classified into 3 groups, namely "healthy," "Stage1/2," and "Stage3/4," and stored in separate folders. The feature extraction stage involved transferring and retraining the feature extraction layers and weights from 3 models, namely ResNet50, DenseNet121, and InceptionV3, which were proposed for classifying the ImageNet dataset, to 3 DL models designed for classifying periodontal bone loss. The features obtained from global average pooling (GAP), global max pooling (GMP), or flatten layers (FL) of convolutional neural network (CNN) models were used as input to the 8 different machine learning (ML) models. In addition, the features obtained from the GAP, GMP, or FL of the DL models were reduced using the minimum redundancy maximum relevance (mRMR) method and then classified again with 8 ML models. RESULTS A total of 2533 panoramic radiographs, including 721 in the healthy group, 842 in the Stage1/2 group, and 970 in the Stage3/4 group, were included in the dataset. The average performance values of DenseNet121 + GAP-based and DenseNet121 + GAP + mRMR-based ML techniques on 10 subdatasets and ML models developed using 2 feature selection techniques outperformed CNN models. CONCLUSIONS The new DenseNet121 + GAP + mRMR-based support vector machine model developed in this study achieved higher performance in periodontal bone loss classification compared to other models in the literature by detecting effective features from raw images without the need for manual selection.
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Affiliation(s)
- Berceste Guler Ayyildiz
- Faculty of Dentistry, Department of Periodontology, Kutahya Health Sciences University, Kutahya, 43100, Turkey
| | - Rukiye Karakis
- Faculty of Technology, Department of Software Engineering, Sivas Cumhuriyet University, Sivas, 58140, Turkey
| | - Busra Terzioglu
- Faculty of Dentistry, Department of Periodontology, Kutahya Health Sciences University, Kutahya, 43100, Turkey
- Tavsanlõ Vocational School, Oral Health Department, Kutahya Health Sciences University, Kütahya, 43410, Turkey
| | - Durmus Ozdemir
- Faculty of Engineering, Department of Computer Engineering, Kutahya Dumlupinar University, Kutahya, 43020, Turkey
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Bumann EE, Al-Qarni S, Chandrashekar G, Sabzian R, Bohaty B, Lee Y. A novel collaborative learning model for mixed dentition and fillings segmentation in panoramic radiographs. J Dent 2024; 140:104779. [PMID: 38007173 DOI: 10.1016/j.jdent.2023.104779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 11/10/2023] [Accepted: 11/11/2023] [Indexed: 11/27/2023] Open
Abstract
INTRODUCTION It is critical for dentists to identify and differentiate primary and permanent teeth, fillings, dental restorations and areas with pathological findings when reviewing dental radiographs to ensure that an accurate diagnosis is made and the optimal treatment can be planned. Unfortunately, dental radiographs are sometimes read incorrectly due to human error or low-quality images. While secondary or group review can help catch errors, many dentists work in practice alone and/or do not have time to review all of their patients' radiographs with another dentist. Artificial intelligence may facilitate the accurate interpretation of radiographs. To help support the review of panoramic radiographs, we developed a novel collaborative learning model that simultaneously identifies and differentiates primary and permanent teeth and detects fillings. METHODS We used publicly accessible dental panoramic radiographic images and images obtained from the University of Missouri-Kansas City, School of Dentistry to develop and optimize two high-performance classifiers: (1) a system for tooth segmentation that can differentiate primary and permanent teeth and (2) a system to detect dental fillings. RESULTS By utilizing these high-performance classifiers, we created models that can identify primary and permanent teeth (mean average precision [mAP] 95.32 % and performance [F-1] 92.50 %), as well as their associated dental fillings (mAP 91.53 % and F-1 91.00 %). We also designed a novel method for collaborative learning that utilizes these two classifiers to enhance recognition performance (mAP 94.09 % and F-1 93.41 %). CONCLUSIONS Our model improves upon the existing machine learning models to simultaneously identify and differentiate primary and permanent teeth, and to identify any associated fillings. CLINICAL SIGNIFICANCE Human error can lead to incorrect readings of panoramic radiographs. By developing artificial intelligence and machine learning methods to analyze panoramic radiographs, dentists can use this information to support their radiograph interpretations, help communicate the information to patients, and assist dental students learning to read radiographs.
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Affiliation(s)
- Erin Ealba Bumann
- Department of Oral and Craniofacial Sciences, University of Missouri-Kansas City, USA.
| | - Saeed Al-Qarni
- Department of Computer Science, University of Missouri-Kansas City, USA; Department of Computing and Informatics, Saudi Electronic University, Saudi Arabia
| | | | - Roya Sabzian
- Department of Oral and Craniofacial Sciences, University of Missouri-Kansas City, USA
| | - Brenda Bohaty
- Department of Pediatric Dentistry, University of Missouri-Kansas City, USA; Department of Pediatric Dentistry, Children's Mercy Hospital, USA
| | - Yugyung Lee
- Department of Computer Science, University of Missouri-Kansas City, USA
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Li X, Zhao D, Xie J, Wen H, Liu C, Li Y, Li W, Wang S. Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis. BMC Oral Health 2023; 23:1017. [PMID: 38114946 PMCID: PMC10729340 DOI: 10.1186/s12903-023-03751-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/08/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND The development of deep learning (DL) algorithms for use in dentistry is an emerging trend. Periodontitis is one of the most prevalent oral diseases, which has a notable impact on the life quality of patients. Therefore, it is crucial to classify periodontitis accurately and efficiently. This systematic review aimed to identify the application of DL for the classification of periodontitis and assess the accuracy of this approach. METHODS A literature search up to November 2023 was implemented through EMBASE, PubMed, Web of Science, Scopus, and Google Scholar databases. Inclusion and exclusion criteria were used to screen eligible studies, and the quality of the studies was evaluated by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology with the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool. Random-effects inverse-variance model was used to perform the meta-analysis of a diagnostic test, with which pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) were calculated, and a summary receiver operating characteristic (SROC) plot was constructed. RESULTS Thirteen studies were included in the meta-analysis. After excluding an outlier, the pooled sensitivity, specificity, positive LR, negative LR and DOR were 0.88 (95%CI 0.82-0.92), 0.82 (95%CI 0.72-0.89), 4.9 (95%CI 3.2-7.5), 0.15 (95%CI 0.10-0.22) and 33 (95%CI 19-59), respectively. The area under the SROC was 0.92 (95%CI 0.89-0.94). CONCLUSIONS The accuracy of DL-based classification of periodontitis is high, and this approach could be employed in the future to reduce the workload of dental professionals and enhance the consistency of classification.
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Affiliation(s)
- Xin Li
- School of Public Health, National Institute for Data Science in Health and Medicine, Capital Medical University, Beijing, China
| | - Dan Zhao
- Department of Implant Dentistry, Beijing Stomatological Hospital, Capital Medical University, Beijing, China
| | - Jinxuan Xie
- School of Public Health, National Institute for Data Science in Health and Medicine, Capital Medical University, Beijing, China
| | - Hao Wen
- City University of Hong Kong, Hong Kong SAR, China
| | - Chunhua Liu
- City University of Hong Kong, Hong Kong SAR, China
| | - Yajie Li
- School of Public Health, National Institute for Data Science in Health and Medicine, Capital Medical University, Beijing, China
| | - Wenbin Li
- Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Songlin Wang
- Salivary Gland Disease Center and Beijing Key Laboratory of Tooth Regeneration and Function Reconstruction, Beijing Laboratory of Oral Health and Beijing Stomatological Hospital, Capital Medical University, Beijing, 100050, China.
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12
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Farajollahi M, Safarian MS, Hatami M, Esmaeil Nejad A, Peters OA. Applying artificial intelligence to detect and analyse oral and maxillofacial bone loss-A scoping review. AUST ENDOD J 2023; 49:720-734. [PMID: 37439465 DOI: 10.1111/aej.12775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 07/03/2023] [Accepted: 07/04/2023] [Indexed: 07/14/2023]
Abstract
Radiographic evaluation of bone changes is one of the main tools in the diagnosis of many oral and maxillofacial diseases. However, this approach to assessment has limitations in accuracy, inconsistency and comparatively low diagnostic efficiency. Recently, artificial intelligence (AI)-based algorithms like deep learning networks have been introduced as a solution to overcome these challenges. Based on recent studies, AI can improve the detection accuracy of an expert clinician for periapical pathology, periodontal diseases and their prognostication, as well as peri-implant bone loss. Also, AI has been successfully used to detect and diagnose oral and maxillofacial lesions with a high predictive value. This study aims to review the current evidence on artificial intelligence applications in the detection and analysis of bone loss in the oral and maxillofacial regions.
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Affiliation(s)
- Mehran Farajollahi
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Sadegh Safarian
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Masoud Hatami
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Azadeh Esmaeil Nejad
- Iranian Center for Endodontic Research, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ove A Peters
- School of Dentistry, The University of Queensland, Herston, Queensland, Australia
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13
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Hoss P, Meyer O, Wölfle UC, Wülk A, Meusburger T, Meier L, Hickel R, Gruhn V, Hesenius M, Kühnisch J, Dujic H. Detection of Periodontal Bone Loss on Periapical Radiographs-A Diagnostic Study Using Different Convolutional Neural Networks. J Clin Med 2023; 12:7189. [PMID: 38002799 PMCID: PMC10672399 DOI: 10.3390/jcm12227189] [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: 11/03/2023] [Revised: 11/14/2023] [Accepted: 11/18/2023] [Indexed: 11/26/2023] Open
Abstract
Interest in machine learning models and convolutional neural networks (CNNs) for diagnostic purposes is steadily increasing in dentistry. Here, CNNs can potentially help in the classification of periodontal bone loss (PBL). In this study, the diagnostic performance of five CNNs in detecting PBL on periapical radiographs was analyzed. A set of anonymized periapical radiographs (N = 21,819) was evaluated by a group of trained and calibrated dentists and classified into radiographs without PBL or with mild, moderate, or severe PBL. Five CNNs were trained over five epochs. Statistically, diagnostic performance was analyzed using accuracy (ACC), sensitivity (SE), specificity (SP), and area under the receiver operating curve (AUC). Here, overall ACC ranged from 82.0% to 84.8%, SE 88.8-90.7%, SP 66.2-71.2%, and AUC 0.884-0.913, indicating similar diagnostic performance of the five CNNs. Furthermore, performance differences were evident in the individual sextant groups. Here, the highest values were found for the mandibular anterior teeth (ACC 94.9-96.0%) and the lowest values for the maxillary posterior teeth (78.0-80.7%). It can be concluded that automatic assessment of PBL seems to be possible, but that diagnostic accuracy varies depending on the location in the dentition. Future research is needed to improve performance for all tooth groups.
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Affiliation(s)
- Patrick Hoss
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.); (H.D.)
| | - Ole Meyer
- Institute for Software Engineering, University of Duisburg-Essen, 45127 Essen, Germany; (O.M.); (V.G.); (M.H.)
| | - Uta Christine Wölfle
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.); (H.D.)
| | - Annika Wülk
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.); (H.D.)
| | - Theresa Meusburger
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.); (H.D.)
| | - Leon Meier
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.); (H.D.)
| | - Reinhard Hickel
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.); (H.D.)
| | - Volker Gruhn
- Institute for Software Engineering, University of Duisburg-Essen, 45127 Essen, Germany; (O.M.); (V.G.); (M.H.)
| | - Marc Hesenius
- Institute for Software Engineering, University of Duisburg-Essen, 45127 Essen, Germany; (O.M.); (V.G.); (M.H.)
| | - Jan Kühnisch
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.); (H.D.)
| | - Helena Dujic
- Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, 80336 Munich, Germany; (P.H.); (U.C.W.); (A.W.); (T.M.); (L.M.); (R.H.); (H.D.)
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14
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Radha RC, Raghavendra BS, Subhash BV, Rajan J, Narasimhadhan AV. Machine learning techniques for periodontitis and dental caries detection: A narrative review. Int J Med Inform 2023; 178:105170. [PMID: 37595373 DOI: 10.1016/j.ijmedinf.2023.105170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 07/07/2023] [Accepted: 07/31/2023] [Indexed: 08/20/2023]
Abstract
OBJECTIVES In recent years, periodontitis, and dental caries have become common in humans and need to be diagnosed in the early stage to prevent severe complications and tooth loss. These dental issues are diagnosed by visual inspection, measuring pocket probing depth, and radiographs findings from experienced dentists. Though a glut of machine learning (ML) algorithms has been proposed for the automated detection of periodontitis, and dental caries, determining which ML techniques are suitable for clinical practice remains under debate. This review aims to identify the research challenges by analyzing the limitations of current methods and how to address these to obtain robust systems suitable for clinical use or point-of-care testing. METHODS An extensive search of the literature published from 2015 to 2022 written in English, related to the subject of study was sought by searching the electronic databases: PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. RESULTS The initial electronic search yielded 1743 titles, and 55 studies were eventually included based on the selection criteria adopted in this review. Studies selected were on ML applications for the automatic detection of periodontitis and dental caries and related dental issues: Apical lessons, Periodontal bone loss, and Vertical root fracture. CONCLUSION While most of the ML-based studies use radiograph images for the detection of periodontitis and dental caries, few pieces of the literature revealed that good diagnostic accuracy could be achieved by training the ML model even with mobile photos representing the images of dental issues. Nowadays smartphones are used in every sector for different applications. Training the ML model with as many images of dental issues captured by the smartphone can achieve good accuracy, reduce the cost of clinical diagnosis, and provide user interaction.
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Affiliation(s)
- R C Radha
- Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India.
| | - B S Raghavendra
- Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India
| | - B V Subhash
- Department of Oral Medicine and Radiology, DAPM R V Dental College, Bengaluru, India
| | - Jeny Rajan
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India
| | - A V Narasimhadhan
- Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, India
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15
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Patil S, Joda T, Soffe B, Awan KH, Fageeh HN, Tovani-Palone MR, Licari FW. Efficacy of artificial intelligence in the detection of periodontal bone loss and classification of periodontal diseases: A systematic review. J Am Dent Assoc 2023; 154:795-804.e1. [PMID: 37452813 DOI: 10.1016/j.adaj.2023.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 05/13/2023] [Accepted: 05/17/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND Artificial intelligence (AI) can aid in the diagnosis and treatment planning of periodontal disease by means of reducing subjectivity. This systematic review aimed to evaluate the efficacy of AI models in detecting radiographic periodontal bone loss (PBL) and accuracy in classifying lesions. TYPES OF STUDIES REVIEWED The authors conducted an electronic search of PubMed, Scopus, and Web of Science for articles published through August 2022. Articles evaluating the efficacy of AI in determining PBL were included. The authors assessed the articles using the Quality Assessment for Studies of Diagnostic Accuracy tool. They used the Grading of Recommendations Assessment, Development and Evaluation criteria to evaluate the certainty of evidence. RESULTS Of the 13 articles identified through electronic search, 6 studies met the inclusion criteria, using a variety of AI algorithms and different modalities, including panoramic and intraoral radiographs. Sensitivity, specificity, accuracy, and pixel accuracy were the outcomes measured. Although some studies found no substantial difference between AI and dental clinicians' performance, others showed AI's superiority in detecting PBL. Evidence suggests that AI has the potential to aid in the detection of PBL and classification of periodontal diseases. However, further research is needed to standardize AI algorithms and validate their clinical usefulness. PRACTICAL IMPLICATIONS Although the use of AI may offer some benefits in the detection and classification of periodontal diseases, the low level of evidence and the inconsistent performance of AI algorithms suggest that caution should be exercised when considering the use of AI models in diagnosing PBL. This review was registered at PROSPERO (CRD42022364600).
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16
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Sivari E, Senirkentli GB, Bostanci E, Guzel MS, Acici K, Asuroglu T. Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review. Diagnostics (Basel) 2023; 13:2512. [PMID: 37568875 PMCID: PMC10416832 DOI: 10.3390/diagnostics13152512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 07/21/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning. In addition, the review critically evaluates the literature to guide and further develop future studies in this field. An extensive literature search was conducted for the 2019-May 2023 range using the Medline (PubMed) and Google Scholar databases to identify eligible articles, and 101 studies were shortlisted, including applications for diagnosing dental anomalies (n = 22) and diseases (n = 79) using deep learning for classification, object detection, and segmentation tasks. According to the results, the most commonly used task type was classification (n = 51), the most commonly used dental image material was panoramic radiographs (n = 55), and the most frequently used performance metric was sensitivity/recall/true positive rate (n = 87) and accuracy (n = 69). Dataset sizes ranged from 60 to 12,179 images. Although deep learning algorithms are used as individual or at least individualized architectures, standardized architectures such as pre-trained CNNs, Faster R-CNN, YOLO, and U-Net have been used in most studies. Few studies have used the explainable AI method (n = 22) and applied tests comparing human and artificial intelligence (n = 21). Deep learning is promising for better diagnosis and treatment planning in dentistry based on the high-performance results reported by the studies. For all that, their safety should be demonstrated using a more reproducible and comparable methodology, including tests with information about their clinical applicability, by defining a standard set of tests and performance metrics.
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Affiliation(s)
- Esra Sivari
- Department of Computer Engineering, Cankiri Karatekin University, Cankiri 18100, Turkey
| | | | - Erkan Bostanci
- Department of Computer Engineering, Ankara University, Ankara 06830, Turkey
| | | | - Koray Acici
- Department of Artificial Intelligence and Data Engineering, Ankara University, Ankara 06830, Turkey
| | - Tunc Asuroglu
- Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
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17
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Shafi I, Fatima A, Afzal H, Díez IDLT, Lipari V, Breñosa J, Ashraf I. A Comprehensive Review of Recent Advances in Artificial Intelligence for Dentistry E-Health. Diagnostics (Basel) 2023; 13:2196. [PMID: 37443594 DOI: 10.3390/diagnostics13132196] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/14/2023] [Accepted: 06/23/2023] [Indexed: 07/15/2023] Open
Abstract
Artificial intelligence has made substantial progress in medicine. Automated dental imaging interpretation is one of the most prolific areas of research using AI. X-ray and infrared imaging systems have enabled dental clinicians to identify dental diseases since the 1950s. However, the manual process of dental disease assessment is tedious and error-prone when diagnosed by inexperienced dentists. Thus, researchers have employed different advanced computer vision techniques, and machine- and deep-learning models for dental disease diagnoses using X-ray and near-infrared imagery. Despite the notable development of AI in dentistry, certain factors affect the performance of the proposed approaches, including limited data availability, imbalanced classes, and lack of transparency and interpretability. Hence, it is of utmost importance for the research community to formulate suitable approaches, considering the existing challenges and leveraging findings from the existing studies. Based on an extensive literature review, this survey provides a brief overview of X-ray and near-infrared imaging systems. Additionally, a comprehensive insight into challenges faced by researchers in the dental domain has been brought forth in this survey. The article further offers an amalgamative assessment of both performances and methods evaluated on public benchmarks and concludes with ethical considerations and future research avenues.
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Affiliation(s)
- Imran Shafi
- College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Anum Fatima
- National Centre for Robotics, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Hammad Afzal
- Military College of Signals (MCS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Isabel de la Torre Díez
- Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Vivian Lipari
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Fundación Universitaria Internacional de Colombia, Bogotá 111311, Colombia
| | - Jose Breñosa
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Universidade Internacional do Cuanza, Cuito EN250, Bié, Angola
- Research Unit in Food Technologies, Agro-Food Industries and Nutrition, Universidad Internacional Iberoamericana Arecibo, Puerto Rico, PR 00613, USA
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
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Uzun Saylan BC, Baydar O, Yeşilova E, Kurt Bayrakdar S, Bilgir E, Bayrakdar İŞ, Çelik Ö, Orhan K. Assessing the Effectiveness of Artificial Intelligence Models for Detecting Alveolar Bone Loss in Periodontal Disease: A Panoramic Radiograph Study. Diagnostics (Basel) 2023; 13:diagnostics13101800. [PMID: 37238284 DOI: 10.3390/diagnostics13101800] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 04/13/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
The assessment of alveolar bone loss, a crucial element of the periodontium, plays a vital role in the diagnosis of periodontitis and the prognosis of the disease. In dentistry, artificial intelligence (AI) applications have demonstrated practical and efficient diagnostic capabilities, leveraging machine learning and cognitive problem-solving functions that mimic human abilities. This study aims to evaluate the effectiveness of AI models in identifying alveolar bone loss as present or absent across different regions. To achieve this goal, alveolar bone loss models were generated using the PyTorch-based YOLO-v5 model implemented via CranioCatch software, detecting periodontal bone loss areas and labeling them using the segmentation method on 685 panoramic radiographs. Besides general evaluation, models were grouped according to subregions (incisors, canines, premolars, and molars) to provide a targeted evaluation. Our findings reveal that the lowest sensitivity and F1 score values were associated with total alveolar bone loss, while the highest values were observed in the maxillary incisor region. It shows that artificial intelligence has a high potential in analytical studies evaluating periodontal bone loss situations. Considering the limited amount of data, it is predicted that this success will increase with the provision of machine learning by using a more comprehensive data set in further studies.
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Affiliation(s)
- Bilge Cansu Uzun Saylan
- Department of Periodontology, Faculty of Dentistry, Dokuz Eylul University, İzmir 35220, Turkey
| | - Oğuzhan Baydar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ege University, İzmir 35040, Turkey
| | - Esra Yeşilova
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir 26040, Turkey
| | - Sevda Kurt Bayrakdar
- Department of Periodontology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir 26040, Turkey
| | - Elif Bilgir
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir 26040, Turkey
| | - İbrahim Şevki Bayrakdar
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir 26040, Turkey
| | - Özer Çelik
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir 26480, Turkey
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06830, Turkey
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Bu WQ, Guo YX, Zhang D, Du SY, Han MQ, Wu ZX, Tang Y, Chen T, Guo YC, Meng HT. Automatic sex estimation using deep convolutional neural network based on orthopantomogram images. Forensic Sci Int 2023; 348:111704. [PMID: 37094502 DOI: 10.1016/j.forsciint.2023.111704] [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: 12/29/2022] [Revised: 04/09/2023] [Accepted: 04/19/2023] [Indexed: 04/26/2023]
Abstract
Sex estimation is very important in forensic applications as part of individual identification. Morphological sex estimation methods predominantly focus on anatomical measurements. Based on the close relationship between sex chromosome genes and facial characterization, craniofacial hard tissues morphology shows sex dimorphism. In order to establish a more labor-saving, rapid, and accurate reference for sex estimation, the study investigated a deep learning network-based artificial intelligence (AI) model using orthopantomograms (OPG) to estimate sex in northern Chinese subjects. In total, 10703 OPG images were divided into training (80%), validation (10%), and test sets (10%). At the same time, different age thresholds were selected to compare the accuracy differences between adults and minors. The accuracy of sex estimation using CNN (convolutional neural network) model was higher for adults (90.97%) compared with minors (82.64%). This work demonstrated that the proposed model trained with a large dataset could be used in automatic morphological sex-related identification with favorable performance and practical significance in forensic science for adults in northern China, while also providing a reference for minors to some extent.
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Affiliation(s)
- Wen-Qing Bu
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China; Department of Orthodontics, Stomatological Hospital of Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Yu-Xin Guo
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Dong Zhang
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, People's Republic of China
| | - Shao-Yi Du
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, People's Republic of China
| | - Meng-Qi Han
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Zi-Xuan Wu
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China; Department of Orthodontics, Stomatological Hospital of Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Yu Tang
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Teng Chen
- College of Medicine and Forensics, Xi'an Jiaotong University Health Science Center, 76 West Yanta Road, Xi'an 710004, Shaanxi, People's Republic of China
| | - Yu-Cheng Guo
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China; Department of Orthodontics, Stomatological Hospital of Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China; National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, People's Republic of China.
| | - Hao-Tian Meng
- Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, 98 XiWu Road, Xi'an 710004, Shaanxi, People's Republic of China.
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20
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Widyaningrum R, Candradewi I, Aji NRAS, Aulianisa R. Comparison of Multi-Label U-Net and Mask R-CNN for panoramic radiograph segmentation to detect periodontitis. Imaging Sci Dent 2022; 52:383-391. [PMID: 36605859 PMCID: PMC9807794 DOI: 10.5624/isd.20220105] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 09/03/2022] [Accepted: 09/09/2022] [Indexed: 12/28/2022] Open
Abstract
Purpose Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches. Materials and Methods Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models. Results The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics (i.e., dice coefficient and intersection-over-union [IoU] score). Multi-Label U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively. Conclusion Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.
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Affiliation(s)
- Rini Widyaningrum
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Ika Candradewi
- Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | | | - Rona Aulianisa
- Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta, Indonesia
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Kearney VP, Yansane AIM, Brandon RG, Vaderhobli R, Lin GH, Hekmatian H, Deng W, Joshi N, Bhandari H, Sadat AS, White JM. A generative adversarial inpainting network to enhance prediction of periodontal clinical attachment level. J Dent 2022; 123:104211. [PMID: 35760207 DOI: 10.1016/j.jdent.2022.104211] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/16/2022] [Accepted: 06/23/2022] [Indexed: 10/17/2022] Open
Abstract
OBJECTIVES Bone level as measured by clinical attachment levels (CAL) are critical findings that determine the diagnosis of periodontal disease. Deep learning algorithms are being used to determine CAL which aid in the diagnosis of periodontal disease. However, the limited field-of-view of bitewing x-rays poses a challenge for convolutional neural networks (CNN) because out-of-view anatomy cannot be directly considered. This study presents an inpainting algorithm using generative adversarial networks (GANs) coupled with partial convolutions to predict out-of-view anatomy to enhance CAL prediction accuracy. METHODS Retrospective purposive sampling of cases with healthy periodontium and diseased periodontium with bitewing and periapical radiographs and clinician recorded CAL were utilized. Data utilized was from July 1, 2016 through January 30, 2020. 80,326 images were used for training, 12,901 images were used for validation and 10,687 images were used to compare non-inpainted methods to inpainted methods for CAL predictions. Statistical analyses were mean bias error (MBE), mean absolute error (MAE) and Dunn's pairwise test comparing CAL at p=0.05. RESULTS Comparator p-values demonstrated statistically significant improvement in CAL prediction accuracy between corresponding inpainted and non-inpainted methods with MAE of 1.04 mm and 1.50 mm respectively. The Dunn's pairwise test indicated statistically significant improvement in CAL prediction accuracy between inpainted methods compared to their non-inpainted counterparts, with the best performing methods achieving a Dunn's pairwise value of -63.89. CONCLUSIONS This study demonstrates the superiority of using a generative adversarial inpainting network with partial convolutions to predict CAL from bitewing and periapical images. CLINICAL SIGNIFICANCE Artificial intelligence was developed and utilized to predict clinical attachment level compared to clinical measurements. A generative adversarial inpainting network with partial convolutions was developed, tested and validated to predict clinical attachment level. The inpainting approach was found to be superior to non-inpainted methods and within the 1mm clinician-determined measurement standard.
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Affiliation(s)
- Vasant P Kearney
- Retrace Labs, Incorporated, 1 Market Street, Spear Tower, 35(th) Floor, San Francisco, CA, 94105
| | - Alfa-Ibrahim M Yansane
- Department of Preventive and Restorative Dental Sciences, University of California, San Francisco, School of Dentistry, 707 Parnassus Avenue, San Francisco, CA, 94105
| | - Ryan G Brandon
- Department of Preventive and Restorative Dental Sciences, University of California, San Francisco, School of Dentistry, 707 Parnassus Avenue, San Francisco, CA, 94105
| | - Ram Vaderhobli
- Department of Preventive and Restorative Dental Sciences, University of California, San Francisco, School of Dentistry, 707 Parnassus Avenue, San Francisco, CA, 94105
| | - Guo-Hao Lin
- Department of Orofacial Sciences, University of California, San Francisco, School of Dentistry, 707 Parnassus Avenue, San Francisco, CA, 94105
| | - Hamid Hekmatian
- Retrace Labs, Incorporated, 1 Market Street, Spear Tower, 35(th) Floor, San Francisco, CA, 94105
| | - Wenxiang Deng
- Retrace Labs, Incorporated, 1 Market Street, Spear Tower, 35(th) Floor, San Francisco, CA, 94105
| | - Neha Joshi
- Department of Preventive and Restorative Dental Sciences, University of California, San Francisco, School of Dentistry, 707 Parnassus Avenue, San Francisco, CA, 94105
| | - Harsh Bhandari
- Department of Preventive and Restorative Dental Sciences, University of California, San Francisco, School of Dentistry, 707 Parnassus Avenue, San Francisco, CA, 94105
| | - Ali S Sadat
- Retrace Labs, Incorporated, 1 Market Street, Spear Tower, 35(th) Floor, San Francisco, CA, 94105
| | - Joel M White
- Department of Preventive and Restorative Dental Sciences, University of California, San Francisco, School of Dentistry, 707 Parnassus Avenue, San Francisco, CA, 94105.
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