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Motmaen I, Xie K, Schönbrunn L, Berens J, Grunert K, Plum AM, Raufeisen J, Ferreira A, Hermans A, Egger J, Hölzle F, Truhn D, Puladi B. Insights into Predicting Tooth Extraction from Panoramic Dental Images: Artificial Intelligence vs. Dentists. Clin Oral Investig 2024; 28:381. [PMID: 38886242 PMCID: PMC11182848 DOI: 10.1007/s00784-024-05781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 06/11/2024] [Indexed: 06/20/2024]
Abstract
OBJECTIVES Tooth extraction is one of the most frequently performed medical procedures. The indication is based on the combination of clinical and radiological examination and individual patient parameters and should be made with great care. However, determining whether a tooth should be extracted is not always a straightforward decision. Moreover, visual and cognitive pitfalls in the analysis of radiographs may lead to incorrect decisions. Artificial intelligence (AI) could be used as a decision support tool to provide a score of tooth extractability. MATERIAL AND METHODS Using 26,956 single teeth images from 1,184 panoramic radiographs (PANs), we trained a ResNet50 network to classify teeth as either extraction-worthy or preservable. For this purpose, teeth were cropped with different margins from PANs and annotated. The usefulness of the AI-based classification as well that of dentists was evaluated on a test dataset. In addition, the explainability of the best AI model was visualized via a class activation mapping using CAMERAS. RESULTS The ROC-AUC for the best AI model to discriminate teeth worthy of preservation was 0.901 with 2% margin on dental images. In contrast, the average ROC-AUC for dentists was only 0.797. With a 19.1% tooth extractions prevalence, the AI model's PR-AUC was 0.749, while the dentist evaluation only reached 0.589. CONCLUSION AI models outperform dentists/specialists in predicting tooth extraction based solely on X-ray images, while the AI performance improves with increasing contextual information. CLINICAL RELEVANCE AI could help monitor at-risk teeth and reduce errors in indications for extractions.
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Affiliation(s)
- Ila Motmaen
- Department of Oral and Maxillofacial Surgery, University Hospital Knappschaftskrankenhaus Bochum, 44892, Bochum, Germany
| | - Kunpeng Xie
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
- Institute of Medical Informatics, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Leon Schönbrunn
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
- Institute of Medical Informatics, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Jeff Berens
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
- Institute of Medical Informatics, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Kim Grunert
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
- Institute of Medical Informatics, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Anna Maria Plum
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
- Institute of Medical Informatics, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Johannes Raufeisen
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
- Institute of Medical Informatics, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - André Ferreira
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
- Institute of Medical Informatics, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
- Centre Algoritmi / LASI, University of Minho, 4710-057, Braga, Portugal
- Institute for Artificial Intelligence in Medicine, Essen University Hospital, 45147, Essen, Germany
| | - Alexander Hermans
- Visual Computing Institute, Computer Science and Natural Sciences, RWTH Aachen University, 52074, Aachen, Germany
- Department of Diagnostic and Interventional Radiology, RWTH Aachen University, 52074, Aachen, Germany
| | - Jan Egger
- Institute for Artificial Intelligence in Medicine, Essen University Hospital, 45147, Essen, Germany
| | - Frank Hölzle
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, RWTH Aachen University, 52074, Aachen, Germany
| | - Behrus Puladi
- Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
- Institute of Medical Informatics, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
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Asci E, Kilic M, Celik O, Cantekin K, Bircan HB, Bayrakdar İS, Orhan K. A Deep Learning Approach to Automatic Tooth Caries Segmentation in Panoramic Radiographs of Children in Primary Dentition, Mixed Dentition, and Permanent Dentition. CHILDREN (BASEL, SWITZERLAND) 2024; 11:690. [PMID: 38929269 PMCID: PMC11202197 DOI: 10.3390/children11060690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 05/03/2024] [Accepted: 05/20/2024] [Indexed: 06/28/2024]
Abstract
OBJECTIVES The purpose of this study was to evaluate the effectiveness of dental caries segmentation on the panoramic radiographs taken from children in primary dentition, mixed dentition, and permanent dentition with Artificial Intelligence (AI) models developed using the deep learning method. METHODS This study used 6075 panoramic radiographs taken from children aged between 4 and 14 to develop the AI model. The radiographs included in the study were divided into three groups: primary dentition (n: 1857), mixed dentition (n: 1406), and permanent dentition (n: 2812). The U-Net model implemented with PyTorch library was used for the segmentation of caries lesions. A confusion matrix was used to evaluate model performance. RESULTS In the primary dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8525, 0.9128, and 0.8816, respectively. In the mixed dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.7377, 0.9192, and 0.8185, respectively. In the permanent dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found to be 0.8271, 0.9125, and 0.8677, respectively. In the total group including primary, mixed, and permanent dentition, the sensitivity, precision, and F1 scores calculated using the confusion matrix were 0.8269, 0.9123, and 0.8675, respectively. CONCLUSIONS Deep learning-based AI models are promising tools for the detection and diagnosis of caries in panoramic radiographs taken from children with different dentition.
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Affiliation(s)
- Esra Asci
- Department of Pediatric Dentistry, Faculty of Dentistry, Ataturk University, Erzurum 25240, Turkey; (E.A.); (H.B.B.)
| | - Munevver Kilic
- Department of Pediatric Dentistry, Faculty of Dentistry, Beykent University, İstanbul 34398, Turkey
| | - Ozer Celik
- Department of Mathematics Computer, Faculty of Science, Eskisehir Osmangazi University, Eskisehir 26040, Turkey;
- Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir Osmangazi University, Eskisehir 26040, Turkey;
| | - Kenan Cantekin
- Department of Pediatric Dentistry, Faculty of Dentistry, Sakarya University, Sakarya 54050, Turkey;
| | - Hasan Basri Bircan
- Department of Pediatric Dentistry, Faculty of Dentistry, Ataturk University, Erzurum 25240, Turkey; (E.A.); (H.B.B.)
| | - İbrahim Sevki Bayrakdar
- Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir Osmangazi University, Eskisehir 26040, Turkey;
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir 26040, Turkey
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara 06620, Turkey;
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Karakuş R, Öziç MÜ, Tassoker M. AI-Assisted Detection of Interproximal, Occlusal, and Secondary Caries on Bite-Wing Radiographs: A Single-Shot Deep Learning Approach. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01113-x. [PMID: 38743125 DOI: 10.1007/s10278-024-01113-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/28/2024] [Accepted: 04/01/2024] [Indexed: 05/16/2024]
Abstract
Tooth decay is a common oral disease worldwide, but errors in diagnosis can often be made in dental clinics, which can lead to a delay in treatment. This study aims to use artificial intelligence (AI) for the automated detection and localization of secondary, occlusal, and interproximal (D1, D2, D3) caries types on bite-wing radiographs. The eight hundred and sixty bite-wing radiographs were collected from the School of Dentistry database. Pre-processing and data augmentation operations were performed. Interproximal (D1, D2, D3), secondary, and occlusal caries on bite-wing radiographs were annotated by two oral radiologists. The data were split into 80% for training, 10% for validation, and 10% for testing. The AI-based training process was conducted using the YOLOv8 algorithm. A clinical decision support system interface was designed using the Python PyQT5 library, allowing for the use of dental caries detection without the need for complex programming procedures. In the test images, the average precision, average sensitivity, and average F1 score values for secondary, occlusal, and interproximal caries were obtained as 0.977, 0.932, and 0.954, respectively. The AI-based dental caries detection system yielded highly successful results in the test, receiving full approval from dentists for clinical use. YOLOv8 has the potential to increase sensitivity and reliability while reducing the burden on dentists and can prevent diagnostic errors in dental clinics.
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Affiliation(s)
- Rabia Karakuş
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Necmettin Erbakan University, Konya, Turkey
| | - Muhammet Üsame Öziç
- Faculty of Technology, Department of Biomedical Engineering, Pamukkale University, Denizli, Turkey.
| | - Melek Tassoker
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Necmettin Erbakan University, Konya, Turkey
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Naeimi SM, Darvish S, Salman BN, Luchian I. Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review. Bioengineering (Basel) 2024; 11:431. [PMID: 38790300 PMCID: PMC11118054 DOI: 10.3390/bioengineering11050431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/21/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Artificial intelligence (AI) has been recently introduced into clinical dentistry, and it has assisted professionals in analyzing medical data with unprecedented speed and an accuracy level comparable to humans. With the help of AI, meaningful information can be extracted from dental databases, especially dental radiographs, to devise machine learning (a subset of AI) models. This study focuses on models that can diagnose and assist with clinical conditions such as oral cancers, early childhood caries, deciduous teeth numbering, periodontal bone loss, cysts, peri-implantitis, osteoporosis, locating minor apical foramen, orthodontic landmark identification, temporomandibular joint disorders, and more. The aim of the authors was to outline by means of a review the state-of-the-art applications of AI technologies in several dental subfields and to discuss the efficacy of machine learning algorithms, especially convolutional neural networks (CNNs), among different types of patients, such as pediatric cases, that were neglected by previous reviews. They performed an electronic search in PubMed, Google Scholar, Scopus, and Medline to locate relevant articles. They concluded that even though clinicians encounter challenges in implementing AI technologies, such as data management, limited processing capabilities, and biased outcomes, they have observed positive results, such as decreased diagnosis costs and time, as well as early cancer detection. Thus, further research and development should be considered to address the existing complications.
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Affiliation(s)
| | - Shayan Darvish
- School of Dentistry, University of Michigan, Ann Arbor, MI 48104, USA;
| | - Bahareh Nazemi Salman
- Department of Pediatric Dentistry, School of Dentistry, Zanjan University of Medical Sciences, Zanjan 4513956184, Iran
| | - Ionut Luchian
- Department of Periodontology, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
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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.
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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.
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Dhanak N, Chougule VT, Nalluri K, Kakkad A, Dhimole A, Parihar AS. Artificial intelligence enabled smart phone app for real-time caries detection on bitewing radiographs. Bioinformation 2024; 20:243-247. [PMID: 38711998 PMCID: PMC11069605 DOI: 10.6026/973206300200243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 03/31/2024] [Accepted: 03/31/2024] [Indexed: 05/08/2024] Open
Abstract
Diagnosis of proximal caries is a difficult task. Artificial intelligence (AI) enabled diagnosis is gaining momentum. Therefore, it is of interest to evaluate the effectiveness of an artificial intelligence (AI) smart phone application for bitewing radiography towards real-time caries lesion detection. The Efficient Det-Lite1 artificial neural network was used after training 100 radiographic images obtained from the department of Oral Medicine. Trained model was then installed in a Google Pixel 6 (GP6) smartphone as artificial intelligence app. The back-facing mobile phone video camera of GP6 was utilised to detect caries lesions on 100 bitewing radiographs (BWR) with 80 carious lesion in real-time. Two different techniques such as scanning the static BWR on laptop with a moving mobile and scanning the moving radiograph on the laptop with stationery mobile were used. The average value of sensitivity/precision/F1 scores for both the techniques was 0.75/0.846 and 0.795 respectively. AI programme using the rear-facing mobile phone video camera was found to detect 75% of caries lesions in real time on 100 BWR with a precision of 84.6%. Thus, the use of AI with smart phone app is useful for caries diagnosis which is readily accessible, easy to use and fast.
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Affiliation(s)
- Nupur Dhanak
- Department of Conservative Dentistry and Endodontics, Government Dental College and Hospital, Ahmadabad, Gujarat, India
| | - Vaibhav T Chougule
- Department of Paediatric and Preventive Dentistry, Bharati Vidyapeeth (Deemed to be University) Dental College and Hospital, Sangli, Maharashtra, India
| | | | - Ankur Kakkad
- Department of Oral Medicine and Radiology, Hitkarini Dental College and Hospital, Jabalpur, MP, India
| | - Ankit Dhimole
- Department of Oral Medicine and Radiology, Hitkarini Dental College and Hospital, Jabalpur, MP, India
| | - Anuj Singh Parihar
- Department of Periodontology, People's Dental Academy, Bhopal, Madhya Pradesh, India
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van Nistelrooij N, Ghoul KE, Xi T, Saha A, Kempers S, Cenci M, Loomans B, Flügge T, van Ginneken B, Vinayahalingam S. Combining public datasets for automated tooth assessment in panoramic radiographs. BMC Oral Health 2024; 24:387. [PMID: 38532414 PMCID: PMC10964594 DOI: 10.1186/s12903-024-04129-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
OBJECTIVE Panoramic radiographs (PRs) provide a comprehensive view of the oral and maxillofacial region and are used routinely to assess dental and osseous pathologies. Artificial intelligence (AI) can be used to improve the diagnostic accuracy of PRs compared to bitewings and periapical radiographs. This study aimed to evaluate the advantages and challenges of using publicly available datasets in dental AI research, focusing on solving the novel task of predicting tooth segmentations, FDI numbers, and tooth diagnoses, simultaneously. MATERIALS AND METHODS Datasets from the OdontoAI platform (tooth instance segmentations) and the DENTEX challenge (tooth bounding boxes with associated diagnoses) were combined to develop a two-stage AI model. The first stage implemented tooth instance segmentation with FDI numbering and extracted regions of interest around each tooth segmentation, whereafter the second stage implemented multi-label classification to detect dental caries, impacted teeth, and periapical lesions in PRs. The performance of the automated tooth segmentation algorithm was evaluated using a free-response receiver-operating-characteristics (FROC) curve and mean average precision (mAP) metrics. The diagnostic accuracy of detection and classification of dental pathology was evaluated with ROC curves and F1 and AUC metrics. RESULTS The two-stage AI model achieved high accuracy in tooth segmentations with a FROC score of 0.988 and a mAP of 0.848. High accuracy was also achieved in the diagnostic classification of impacted teeth (F1 = 0.901, AUC = 0.996), whereas moderate accuracy was achieved in the diagnostic classification of deep caries (F1 = 0.683, AUC = 0.960), early caries (F1 = 0.662, AUC = 0.881), and periapical lesions (F1 = 0.603, AUC = 0.974). The model's performance correlated positively with the quality of annotations in the used public datasets. Selected samples from the DENTEX dataset revealed cases of missing (false-negative) and incorrect (false-positive) diagnoses, which negatively influenced the performance of the AI model. CONCLUSIONS The use and pooling of public datasets in dental AI research can significantly accelerate the development of new AI models and enable fast exploration of novel tasks. However, standardized quality assurance is essential before using the datasets to ensure reliable outcomes and limit potential biases.
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Affiliation(s)
- Niels van Nistelrooij
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Postal Number 590, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands
- Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany
| | - Khalid El Ghoul
- Department of Oral and Maxillofacial Surgery, Erasmus MC, Dr. Molewaterplein 40, Rotterdam, The Netherlands
| | - Tong Xi
- Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, Nijmegen, 6525 GA, The Netherlands
| | - Anindo Saha
- Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, Nijmegen, 6525 GA, The Netherlands
| | - Steven Kempers
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Postal Number 590, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands
| | - Max Cenci
- Department of Dentistry, Radboud University Medical Center, Ph. Van Leydenlaan 25, Nijmegen, 6525 EX, The Netherlands
| | - Bas Loomans
- Department of Dentistry, Radboud University Medical Center, Ph. Van Leydenlaan 25, Nijmegen, 6525 EX, The Netherlands
| | - Tabea Flügge
- Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany.
- Einstein Center for Digital Future, Wilhelmstraße 67, Berlin, Germany.
| | - Bram van Ginneken
- Department of Radiology, Radboud University Medical Center, Geert Grooteplein Zuid 10, Nijmegen, 6525 GA, The Netherlands
| | - Shankeeth Vinayahalingam
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Postal Number 590, P.O. Box 9101, Nijmegen, 6500 HB, The Netherlands
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Zanini LGK, Rubira-Bullen IRF, Nunes FDLDS. A Systematic Review on Caries Detection, Classification, and Segmentation from X-Ray Images: Methods, Datasets, Evaluation, and Open Opportunities. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01054-5. [PMID: 38429559 DOI: 10.1007/s10278-024-01054-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/19/2023] [Accepted: 01/02/2024] [Indexed: 03/03/2024]
Abstract
Dental caries occurs from the interaction between oral bacteria and sugars, generating acids that damage teeth over time. The importance of X-ray images for detecting oral problems is undeniable in dentistry. With technological advances, it is feasible to identify these lesions using techniques such as deep learning, machine learning, and image processing. Therefore, the survey and systematization of these methods are essential to determining the main computational approaches for identifying caries in X-ray images. In this systematic review, we investigated the primary computational methods used for classifying, detecting, and segmenting caries in X-ray images. Following the PRISMA methodology, we selected relevant studies and analyzed their methods, strengths, limitations, imaging modalities, evaluation metrics, datasets, and classification techniques. The review encompassed 42 studies retrieved from the Science Direct, IEEExplore, ACM Digital, and PubMed databases from the Computer Science and Health areas. The results indicate that 12% of the included articles utilized public datasets, with deep learning being the predominant approach, accounting for 69% of the studies. The majority of these studies (76%) focused on classifying dental caries, either in binary or multiclass classification. Panoramic imaging was the most commonly used radiographic modality, representing 29% of the cases studied. Overall, our systematic review provides a comprehensive overview of the computational methods employed in identifying caries in radiographic images and highlights trends, patterns, and challenges in this research field.
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Affiliation(s)
- Luiz Guilherme Kasputis Zanini
- Department of Computer Engineering and Digital Systems, University of São Paulo, Av. Prof. Luciano Gualberto 158, São Paulo, 05508-010, São Paulo, Brazil.
| | | | - Fátima de Lourdes Dos Santos Nunes
- Department of Computer Engineering and Digital Systems, University of São Paulo, Av. Prof. Luciano Gualberto 158, São Paulo, 05508-010, São Paulo, Brazil
- School of Arts, Sciences and Humanities, University of São Paulo, Rua Arlindo Béttio, 1000, São Paulo, 03828-000, São Paulo, Brazil
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Faadiya AN, Widyaningrum R, Arindra PK, Diba SF. The diagnostic performance of impacted third molars in the mandible: A review of deep learning on panoramic radiographs. Saudi Dent J 2024; 36:404-412. [PMID: 38525176 PMCID: PMC10960107 DOI: 10.1016/j.sdentj.2023.11.025] [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: 06/20/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 03/26/2024] Open
Abstract
Background Mandibular third molar is prone to impaction, resulting in its inability to erupt into the oral cavity. The radiographic examination is required to support the odontectomy of impacted teeth. The use of computer-aided diagnosis based on deep learning is emerging in the field of medical and dentistry with the advancement of artificial intelligence (AI) technology. This review describes the performance and prospects of deep learning for the detection, classification, and evaluation of third molar-mandibular canal relationships on panoramic radiographs. Methods This work was conducted using three databases: PubMed, Google Scholar, and Science Direct. Following the literature selection, 49 articles were reviewed, with the 12 main articles discussed in this review. Results Several models of deep learning are currently used for segmentation and classification of third molar impaction with or without the combination of other techniques. Deep learning has demonstrated significant diagnostic performance in identifying mandibular impacted third molars (ITM) on panoramic radiographs, with an accuracy range of 78.91% to 90.23%. Meanwhile, the accuracy of deep learning in determining the relationship between ITM and the mandibular canal (MC) ranges from 72.32% to 99%. Conclusion Deep learning-based AI with high performance for the detection, classification, and evaluation of the relationship of ITM to the MC using panoramic radiographs has been developed over the past decade. However, deep learning must be improved using large datasets, and the evaluation of diagnostic performance for deep learning models should be aligned with medical diagnostic test protocols. Future studies involving collaboration among oral radiologists, clinicians, and computer scientists are required to identify appropriate AI development models that are accurate, efficient, and applicable to clinical services.
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Affiliation(s)
- Amalia Nur Faadiya
- Dental Medicine Study Program, Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Rini Widyaningrum
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Pingky Krisna Arindra
- Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - Silviana Farrah Diba
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta, Indonesia
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Albano D, Galiano V, Basile M, Di Luca F, Gitto S, Messina C, Cagetti MG, Del Fabbro M, Tartaglia GM, Sconfienza LM. Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review. BMC Oral Health 2024; 24:274. [PMID: 38402191 PMCID: PMC10894487 DOI: 10.1186/s12903-024-04046-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Accepted: 02/17/2024] [Indexed: 02/26/2024] Open
Abstract
BACKGROUND The aim of this systematic review is to evaluate the diagnostic performance of Artificial Intelligence (AI) models designed for the detection of caries lesion (CL). MATERIALS AND METHODS An electronic literature search was conducted on PubMed, Web of Science, SCOPUS, LILACS and Embase databases for retrospective, prospective and cross-sectional studies published until January 2023, using the following keywords: artificial intelligence (AI), machine learning (ML), deep learning (DL), artificial neural networks (ANN), convolutional neural networks (CNN), deep convolutional neural networks (DCNN), radiology, detection, diagnosis and dental caries (DC). The quality assessment was performed using the guidelines of QUADAS-2. RESULTS Twenty articles that met the selection criteria were evaluated. Five studies were performed on periapical radiographs, nine on bitewings, and six on orthopantomography. The number of imaging examinations included ranged from 15 to 2900. Four studies investigated ANN models, fifteen CNN models, and two DCNN models. Twelve were retrospective studies, six cross-sectional and two prospective. The following diagnostic performance was achieved in detecting CL: sensitivity from 0.44 to 0.86, specificity from 0.85 to 0.98, precision from 0.50 to 0.94, PPV (Positive Predictive Value) 0.86, NPV (Negative Predictive Value) 0.95, accuracy from 0.73 to 0.98, area under the curve (AUC) from 0.84 to 0.98, intersection over union of 0.3-0.4 and 0.78, Dice coefficient 0.66 and 0.88, F1-score from 0.64 to 0.92. According to the QUADAS-2 evaluation, most studies exhibited a low risk of bias. CONCLUSION AI-based models have demonstrated good diagnostic performance, potentially being an important aid in CL detection. Some limitations of these studies are related to the size and heterogeneity of the datasets. Future studies need to rely on comparable, large, and clinically meaningful datasets. PROTOCOL PROSPERO identifier: CRD42023470708.
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Affiliation(s)
- Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy.
| | | | - Mariachiara Basile
- Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy
| | - Filippo Di Luca
- Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy
| | - Salvatore Gitto
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Carmelo Messina
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Maria Grazia Cagetti
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - Massimo Del Fabbro
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Ospedale Maggiore Policlinico, UOC Maxillo-Facial Surgery and Dentistry Fondazione IRCCS Cà Granda, Milan, Italy
| | - Gianluca Martino Tartaglia
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Ospedale Maggiore Policlinico, UOC Maxillo-Facial Surgery and Dentistry Fondazione IRCCS Cà Granda, Milan, Italy
| | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
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Ndiaye AD, Gasqui MA, Millioz F, Perard M, Leye Benoist F, Grosgogeat B. Exploring the Methodological Approaches of Studies on Radiographic Databases Used in Cariology to Feed Artificial Intelligence: A Systematic Review. Caries Res 2024; 58:117-140. [PMID: 38342096 DOI: 10.1159/000536277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 01/04/2024] [Indexed: 02/13/2024] Open
Abstract
INTRODUCTION A growing number of studies on diagnostic imaging show superior efficiency and accuracy of computer-aided diagnostic systems compared to those of certified dentists. This methodological systematic review aimed to evaluate the different methodological approaches used by studies focusing on machine learning and deep learning that have used radiographic databases to classify, detect, and segment dental caries. METHODS The protocol was registered in PROSPERO before data collection (CRD42022348097). Literature research was performed in MEDLINE, Embase, IEEE Xplore, and Web of Science until December 2022, without language restrictions. Studies and surveys using a dental radiographic database for the classification, detection, or segmentation of carious lesions were sought. Records deemed eligible were retrieved and further assessed for inclusion by two reviewers who resolved any discrepancies through consensus. A third reviewer was consulted when any disagreements or discrepancies persisted between the two reviewers. After data extraction, the same reviewers assessed the methodological quality using the CLAIM and QUADAS-AI checklists. RESULTS After screening 325 articles, 35 studies were eligible and included. The bitewing was the most commonly used radiograph (n = 17) at the time when detection (n = 15) was the most explored computer vision task. The sample sizes used ranged from 95 to 38,437, while the augmented training set ranged from 300 to 315,786. Convolutional neural network was the most commonly used model. The mean completeness of CLAIM items was 49% (SD ± 34%). The applicability of the CLAIM checklist items revealed several weaknesses in the methodology of the selected studies: most of the studies were monocentric, and only 9% of them used an external test set when evaluating the model's performance. The QUADAS-AI tool revealed that only 43% of the studies included in this systematic review were at low risk of bias concerning the standard reference domain. CONCLUSION This review demonstrates that the overall scientific quality of studies conducted to feed artificial intelligence algorithms is low. Some improvement in the design and validation of studies can be made with the development of a standardized guideline for the reproducibility and generalizability of results and, thus, their clinical applications.
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Affiliation(s)
- Amadou Diaw Ndiaye
- Service d'Odontologie Conservatrice-Endodontie, Université Cheikh Anta Diop, Dakar, Senegal,
| | - Marie Agnès Gasqui
- Laboratoire des Multimatériaux et Interfaces (LMI), UMR CNRS, Université Claude Bernard Lyon 1, Lyon, France
- Service d'Odontologie, Hospices Civils de Lyon, Lyon, France
| | - Fabien Millioz
- CREATIS (Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé) - CNRS UMR - INSERM U1294 - Université Claude Bernard Lyon 1 - INSA Lyon, Lyon - Université Jean Monnet Saint-Etienne, Saint-Etienne, France
| | - Matthieu Perard
- University Rennes, INSERM, Rennes, France
- CHU Rennes, Rennes, France
| | - Fatou Leye Benoist
- Service d'Odontologie Conservatrice-Endodontie, Université Cheikh Anta Diop, Dakar, Senegal
| | - Brigitte Grosgogeat
- Laboratoire des Multimatériaux et Interfaces (LMI), UMR CNRS, Université Claude Bernard Lyon 1, Lyon, France
- Service d'Odontologie, Hospices Civils de Lyon, Lyon, France
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Hartoonian S, Hosseini M, Yousefi I, Mahdian M, Ghazizadeh Ahsaie M. Applications of artificial intelligence in dentomaxillofacial imaging-a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol 2024:S2212-4403(23)01566-3. [PMID: 38637235 DOI: 10.1016/j.oooo.2023.12.790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/02/2023] [Accepted: 12/22/2023] [Indexed: 04/20/2024]
Abstract
BACKGROUND Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities. STUDY DESIGN A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another. RESULTS The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection. CONCLUSION Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.
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Affiliation(s)
- Serlie Hartoonian
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Matine Hosseini
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Iman Yousefi
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mina Mahdian
- Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Mitra Ghazizadeh Ahsaie
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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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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Vinayahalingam S, Kempers S, Schoep J, Hsu TMH, Moin DA, van Ginneken B, Flügge T, Hanisch M, Xi T. Intra-oral scan segmentation using deep learning. BMC Oral Health 2023; 23:643. [PMID: 37670290 PMCID: PMC10481506 DOI: 10.1186/s12903-023-03362-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 08/26/2023] [Indexed: 09/07/2023] Open
Abstract
OBJECTIVE Intra-oral scans and gypsum cast scans (OS) are widely used in orthodontics, prosthetics, implantology, and orthognathic surgery to plan patient-specific treatments, which require teeth segmentations with high accuracy and resolution. Manual teeth segmentation, the gold standard up until now, is time-consuming, tedious, and observer-dependent. This study aims to develop an automated teeth segmentation and labeling system using deep learning. MATERIAL AND METHODS As a reference, 1750 OS were manually segmented and labeled. A deep-learning approach based on PointCNN and 3D U-net in combination with a rule-based heuristic algorithm and a combinatorial search algorithm was trained and validated on 1400 OS. Subsequently, the trained algorithm was applied to a test set consisting of 350 OS. The intersection over union (IoU), as a measure of accuracy, was calculated to quantify the degree of similarity between the annotated ground truth and the model predictions. RESULTS The model achieved accurate teeth segmentations with a mean IoU score of 0.915. The FDI labels of the teeth were predicted with a mean accuracy of 0.894. The optical inspection showed excellent position agreements between the automatically and manually segmented teeth components. Minor flaws were mostly seen at the edges. CONCLUSION The proposed method forms a promising foundation for time-effective and observer-independent teeth segmentation and labeling on intra-oral scans. CLINICAL SIGNIFICANCE Deep learning may assist clinicians in virtual treatment planning in orthodontics, prosthetics, implantology, and orthognathic surgery. The impact of using such models in clinical practice should be explored.
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Affiliation(s)
- Shankeeth Vinayahalingam
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
- Department of Artificial Intelligence, Radboud University, Nijmegen, the Netherlands
- Department of Oral and Maxillofacial Surgery, Universitätsklinikum Münster, Münster, Germany
| | - Steven Kempers
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
- Department of Artificial Intelligence, Radboud University, Nijmegen, the Netherlands
| | - Julian Schoep
- Promaton Co. Ltd, 1076 GR, Amsterdam, The Netherlands
| | - Tzu-Ming Harry Hsu
- MIT Computer Science & Artificial Intelligence Laboratory, 32 Vassar St, Cambridge, MA, 02139, USA
| | | | - Bram van Ginneken
- Department of Radiology, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Tabea Flügge
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Department of Oral and Maxillofacial Surgery, Hindenburgdamm 30, 12203, Berlin, Germany.
| | - Marcel Hanisch
- Department of Oral and Maxillofacial Surgery, Universitätsklinikum Münster, Münster, Germany
- Promaton Co. Ltd, 1076 GR, Amsterdam, The Netherlands
| | - Tong Xi
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
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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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Zhu J, Chen Z, Zhao J, Yu Y, Li X, Shi K, Zhang F, Yu F, Shi K, Sun Z, Lin N, Zheng Y. Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study. BMC Oral Health 2023; 23:358. [PMID: 37270488 DOI: 10.1186/s12903-023-03027-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 05/09/2023] [Indexed: 06/05/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) has been introduced to interpret the panoramic radiographs (PRs). The aim of this study was to develop an AI framework to diagnose multiple dental diseases on PRs, and to initially evaluate its performance. METHODS The AI framework was developed based on 2 deep convolutional neural networks (CNNs), BDU-Net and nnU-Net. 1996 PRs were used for training. Diagnostic evaluation was performed on a separate evaluation dataset including 282 PRs. Sensitivity, specificity, Youden's index, the area under the curve (AUC), and diagnostic time were calculated. Dentists with 3 different levels of seniority (H: high, M: medium, L: low) diagnosed the same evaluation dataset independently. Mann-Whitney U test and Delong test were conducted for statistical analysis (ɑ=0.05). RESULTS Sensitivity, specificity, and Youden's index of the framework for diagnosing 5 diseases were 0.964, 0.996, 0.960 (impacted teeth), 0.953, 0.998, 0.951 (full crowns), 0.871, 0.999, 0.870 (residual roots), 0.885, 0.994, 0.879 (missing teeth), and 0.554, 0.990, 0.544 (caries), respectively. AUC of the framework for the diseases were 0.980 (95%CI: 0.976-0.983, impacted teeth), 0.975 (95%CI: 0.972-0.978, full crowns), and 0.935 (95%CI: 0.929-0.940, residual roots), 0.939 (95%CI: 0.934-0.944, missing teeth), and 0.772 (95%CI: 0.764-0.781, caries), respectively. AUC of the AI framework was comparable to that of all dentists in diagnosing residual roots (p > 0.05), and its AUC values were similar to (p > 0.05) or better than (p < 0.05) that of M-level dentists for diagnosing 5 diseases. But AUC of the framework was statistically lower than some of H-level dentists for diagnosing impacted teeth, missing teeth, and caries (p < 0.05). The mean diagnostic time of the framework was significantly shorter than that of all dentists (p < 0.001). CONCLUSIONS The AI framework based on BDU-Net and nnU-Net demonstrated high specificity on diagnosing impacted teeth, full crowns, missing teeth, residual roots, and caries with high efficiency. The clinical feasibility of AI framework was preliminary verified since its performance was similar to or even better than the dentists with 3-10 years of experience. However, the AI framework for caries diagnosis should be improved.
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Affiliation(s)
- Junhua Zhu
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Zhi Chen
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Jing Zhao
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Yueyuan Yu
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Xiaojuan Li
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Kangjian Shi
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Fan Zhang
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Feifei Yu
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Keying Shi
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Zhe Sun
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Nengjie Lin
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Yuanna Zheng
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
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Kempers S, van Lierop P, Hsu TMH, Moin DA, Bergé S, Ghaeminia H, Xi T, Vinayahalingam S. Positional assessment of lower third molar and mandibular canal using explainable artificial intelligence. J Dent 2023; 133:104519. [PMID: 37061117 DOI: 10.1016/j.jdent.2023.104519] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 03/28/2023] [Accepted: 04/12/2023] [Indexed: 04/17/2023] Open
Abstract
OBJECTIVE The aim of this study is to automatically assess the positional relationship between lower third molars (M3i) and the mandibular canal (MC) based on the panoramic radiograph(s) (PR(s)). MATERIAL AND METHODS A total of 1444 M3s were manually annotated and labeled on 863 PRs as a reference. A deep-learning approach, based on MobileNet-V2 combination with a skeletonization algorithm and a signed distance method, was trained and validated on 733 PRs with 1227 M3s to classify the positional relationship between M3i and MC into three categories. Subsequently, the trained algorithm was applied to a test set consisting of 130 PRs (217 M3s). Accuracy, precision, sensitivity, specificity, negative predictive value, and F1-score were calculated. RESULTS The proposed method achieved a weighted accuracy of 0.951, precision of 0.943, sensitivity of 0.941, specificity of 0.800, negative predictive value of 0.865 and an F1-score of 0.938. CONCLUSION AI-enhanced assessment of PRs can objectively, accurately, and reproducibly determine the positional relationship between M3i and MC. CLINICAL SIGNIFICANCE The use of such an explainable AI system can assist clinicians in the intuitive positional assessment of lower third molars and mandibular canals. Further research is required to automatically assess the risk of alveolar nerve injury on panoramic radiographs.
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Affiliation(s)
- Steven Kempers
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands; Department of Artificial Intelligence, Radboud University, Nijmegen, the Netherlands
| | - Pieter van Lierop
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Tzu-Ming Harry Hsu
- MIT Computer Science & Artificial Intelligence Laboratory, 32 Vassar St, Cambridge, MA 02139, United States
| | | | - Stefaan Bergé
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Hossein Ghaeminia
- Department of Oral and Maxillofacial Surgery, Rijnstate Hospital, Arnhem, the Netherlands
| | - Tong Xi
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
| | - Shankeeth Vinayahalingam
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands; Department of Artificial Intelligence, Radboud University, Nijmegen, the Netherlands; Department of Oral and Maxillofacial Surgery, Universitätsklinikum Münster, Münster, Germany.
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Tooth Type Enhanced Transformer for Children Caries Diagnosis on Dental Panoramic Radiographs. Diagnostics (Basel) 2023; 13:diagnostics13040689. [PMID: 36832177 PMCID: PMC9955042 DOI: 10.3390/diagnostics13040689] [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: 11/30/2022] [Revised: 02/01/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023] Open
Abstract
The objective of this study was to introduce a novel deep learning technique for more accurate children caries diagnosis on dental panoramic radiographs. Specifically, a swin transformer is introduced, which is compared with the state-of-the-art convolutional neural network (CNN) methods that are widely used for caries diagnosis. A tooth type enhanced swin transformer is further proposed by considering the differences among canine, molar and incisor. Modeling the above differences in swin transformer, the proposed method was expected to mine domain knowledge for more accurate caries diagnosis. To test the proposed method, a children panoramic radiograph database was built and labeled with a total of 6028 teeth. Swin transformer shows better diagnosis performance compared with typical CNN methods, which indicates the usefulness of this new technique for children caries diagnosis on panoramic radiographs. Furthermore, the proposed tooth type enhanced swin transformer outperforms the naive swin transformer with the accuracy, precision, recall, F1 and area-under-the-curve being 0.8557, 0.8832, 0.8317, 0.8567 and 0.9223, respectively. This indicates that the transformer model can be further improved with a consideration of domain knowledge instead of a copy of previous transformer models designed for natural images. Finally, we compare the proposed tooth type enhanced swin transformer with two attending doctors. The proposed method shows higher caries diagnosis accuracy for the first and second primary molars, which may assist dentists in caries diagnosis.
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Detection of oral squamous cell carcinoma in clinical photographs using a vision transformer. Sci Rep 2023; 13:2296. [PMID: 36759684 PMCID: PMC9911393 DOI: 10.1038/s41598-023-29204-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 01/31/2023] [Indexed: 02/11/2023] Open
Abstract
Oral squamous cell carcinoma (OSCC) is amongst the most common malignancies, with an estimated incidence of 377,000 and 177,000 deaths worldwide. The interval between the onset of symptoms and the start of adequate treatment is directly related to tumor stage and 5-year-survival rates of patients. Early detection is therefore crucial for efficient cancer therapy. This study aims to detect OSCC on clinical photographs (CP) automatically. 1406 CP(s) were manually annotated and labeled as a reference. A deep-learning approach based on Swin-Transformer was trained and validated on 1265 CP(s). Subsequently, the trained algorithm was applied to a test set consisting of 141 CP(s). The classification accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved a classification accuracy of 0.986 and an AUC of 0.99 for classifying OSCC on clinical photographs. Deep learning-based assistance of clinicians may raise the rate of early detection of oral cancer and hence the survival rate and quality of life of patients.
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Wang C, Zhang R, Wei X, Wang L, Wu P, Yao Q. Deep learning and sub-band fluorescence imaging-based method for caries and calculus diagnosis embeddable on different smartphones. BIOMEDICAL OPTICS EXPRESS 2023; 14:866-882. [PMID: 36874478 PMCID: PMC9979668 DOI: 10.1364/boe.479818] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 12/28/2022] [Accepted: 01/13/2023] [Indexed: 06/18/2023]
Abstract
Popularizing community and home early caries screening is essential for caries prevention and treatment. However, a high-precision, low-cost, and portable automated screening tool is currently lacking. This study constructed an automated diagnosis model for dental caries and calculus using fluorescence sub-band imaging combined with deep learning. The proposed method is divided into two stages: the first stage collects imaging information of dental caries in different fluorescence spectral bands and obtains six-channel fluorescence images. The second stage employs a 2-D-3-D hybrid convolutional neural network combined with the attention mechanism for classification and diagnosis. The experiments demonstrate that the method has competitive performance compared to existing methods. In addition, the feasibility of transferring this approach to different smartphones is discussed. This highly accurate, low-cost, portable method has potential applications in community and at-home caries detection.
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Affiliation(s)
- Cheng Wang
- Department of Optical Science and Engineering, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
| | - Rongjun Zhang
- Department of Optical Science and Engineering, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
| | - Xiaoling Wei
- Department of Endodontics, Shanghai Stomatological Hospital, Shanghai Key Laboratory of Craniomaxillofacial Development and Diseases, Fudan University, Shanghai 200001, China
| | - Le Wang
- Department of Optical Science and Engineering, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
| | - Peiyu Wu
- Department of Optical Science and Engineering, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
| | - Qi Yao
- Department of Optical Science and Engineering, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
- State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
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21
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Qayyum A, Tahir A, Butt MA, Luke A, Abbas HT, Qadir J, Arshad K, Assaleh K, Imran MA, Abbasi QH. Dental caries detection using a semi-supervised learning approach. Sci Rep 2023; 13:749. [PMID: 36639724 PMCID: PMC9839770 DOI: 10.1038/s41598-023-27808-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 01/09/2023] [Indexed: 01/15/2023] Open
Abstract
Early diagnosis of dental caries progression can prevent invasive treatment and enable preventive treatment. In this regard, dental radiography is a widely used tool to capture dental visuals that are used for the detection and diagnosis of caries. Different deep learning (DL) techniques have been used to automatically analyse dental images for caries detection. However, most of these techniques require large-scale annotated data to train DL models. On the other hand, in clinical settings, such medical images are scarcely available and annotations are costly and time-consuming. To this end, we present an efficient self-training-based method for caries detection and segmentation that leverages a small set of labelled images for training the teacher model and a large collection of unlabelled images for training the student model. We also propose to use centroid cropped images of the caries region and different augmentation techniques for the training of self-supervised models that provide computational and performance gains as compared to fully supervised learning and standard self-supervised learning methods. We present a fully labelled dental radiographic dataset of 141 images that are used for the evaluation of baseline and proposed models. Our proposed self-supervised learning strategy has provided performance improvement of approximately 6% and 3% in terms of average pixel accuracy and mean intersection over union, respectively as compared to standard self-supervised learning. Data and code will be made available to facilitate future research.
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Affiliation(s)
- Adnan Qayyum
- James Watt School of Engineering, University of Glasgow, Glasgow, UK.,Information Technology University of the Punjab, Lahore, Pakistan
| | - Ahsen Tahir
- James Watt School of Engineering, University of Glasgow, Glasgow, UK.,Department of Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan
| | | | - Alexander Luke
- Department of Clinical Sciences, College of Dentistry, Ajman University, Ajman, UAE.,Centre of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman, UAE
| | - Hasan Tahir Abbas
- James Watt School of Engineering, University of Glasgow, Glasgow, UK
| | - Junaid Qadir
- Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar
| | - Kamran Arshad
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, UAE
| | - Khaled Assaleh
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, UAE
| | - Muhammad Ali Imran
- James Watt School of Engineering, University of Glasgow, Glasgow, UK.,Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, UAE
| | - Qammer H Abbasi
- James Watt School of Engineering, University of Glasgow, Glasgow, UK.
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22
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A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs. Diagnostics (Basel) 2023; 13:diagnostics13020202. [PMID: 36673010 PMCID: PMC9858411 DOI: 10.3390/diagnostics13020202] [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: 12/04/2022] [Revised: 12/30/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
The study aims to evaluate the diagnostic performance of an artificial intelligence system based on deep learning for the segmentation of occlusal, proximal and cervical caries lesions on panoramic radiographs. The study included 504 anonymous panoramic radiographs obtained from the radiology archive of Inonu University Faculty of Dentistry's Department of Oral and Maxillofacial Radiology from January 2018 to January 2020. This study proposes Dental Caries Detection Network (DCDNet) architecture for dental caries segmentation. The main difference between DCDNet and other segmentation architecture is that the last part of DCDNet contains a Multi-Predicted Output (MPO) structure. In MPO, the final feature map split into three different paths for detecting occlusal, proximal and cervical caries. Extensive experimental analyses were executed to analyze the DCDNet network architecture performance. In these comparison results, while the proposed model achieved an average F1-score of 62.79%, the highest average F1-score of 15.69% was achieved with the state-of-the-art segmentation models. These results show that the proposed artificial intelligence-based model can be one of the indispensable auxiliary tools of dentists in the diagnosis and treatment planning of carious lesions by enabling their detection in different locations with high success.
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23
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Belam A, Rairam SG, Patil V, Ratnakar P, Patil S, Kulkarni S. Evaluation of detrimental effects of impacted Mandibular third molars on adjacent second molars - A retrospective observational study. J Conserv Dent 2023; 26:104-107. [PMID: 36908724 PMCID: PMC10003276 DOI: 10.4103/jcd.jcd_341_22] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 12/12/2022] Open
Abstract
Context Mandibular impacted third molars are the most frequently impacted teeth in humans and can predispose the adjacent second molar to an array of detrimental effects such as caries, periodontitis, and cervical resorption thus the aim of this study was to determine the same. Subjects and Methods A retrospective observational cross-sectional study involving patients with orthopantomography presenting with impacted lower third molar. The type, depth, and level of impaction, the extent of caries, periodontal changes, and the presence of cervical resorption were assessed. Statistical Analysis Used Data were analyzed using SPSS version 21.0. with Chi-square. Results Mesioangular impaction was most commonly noted. The pattern of impaction had a direct influence in the formation of carious lesions, cervical resorption, and periodontal ligament (PDL) changes. Conclusions Noting the pattern of third molar impaction helps the clinician to forecast problems that may occur on adjacent teeth and initiate necessary prophylactic treatment.
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Affiliation(s)
- Ambika Belam
- Department of Conservative Dentistry and Endodontics, HKES's S. Nijalingappa Institute of Dental Sciences and Research, Kalaburagi, Karnataka, India
| | - Surabhi G. Rairam
- Department of Conservative Dentistry and Endodontics, HKES's S. Nijalingappa Institute of Dental Sciences and Research, Kalaburagi, Karnataka, India
| | - Veerendra Patil
- Department of Conservative Dentistry and Endodontics, HKES's S. Nijalingappa Institute of Dental Sciences and Research, Kalaburagi, Karnataka, India
| | - P. Ratnakar
- Department of Conservative Dentistry and Endodontics, HKES's S. Nijalingappa Institute of Dental Sciences and Research, Kalaburagi, Karnataka, India
| | - Supriya Patil
- Department of Conservative Dentistry and Endodontics, HKES's S. Nijalingappa Institute of Dental Sciences and Research, Kalaburagi, Karnataka, India
| | - Sangeeta Kulkarni
- Department of Conservative Dentistry and Endodontics, HKES's S. Nijalingappa Institute of Dental Sciences and Research, Kalaburagi, Karnataka, India
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24
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Machine learning-based automatic identification and diagnosis of dental caries and calculus using hyperspectral fluorescence imaging. Photodiagnosis Photodyn Ther 2022; 41:103217. [PMID: 36462702 DOI: 10.1016/j.pdpdt.2022.103217] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 11/07/2022] [Accepted: 11/29/2022] [Indexed: 12/05/2022]
Abstract
PURPOSE Precise diagnosis and identification of early dental caries facilitates timely intervention and reverses the progression of the disease. Developing an objective, accurate and rapid caries and calculus automatic identification method advances clinical application and facilitates the promotion and screening of oral health in the community and family. METHODS In this study, based on 122 dental surfaces labeled by professional dentists, hyperspectral fluorescence imaging combined with machine learning algorithms was employed to construct a model for simultaneously diagnosing dental caries and calculus. Model trained by fusion features based on spectra, textures, and colors with the integrated learning algorithm has better performance and stronger generalization capabilities. RESULTS The experimental results showed that the diagnostic model's accuracy, sensitivity, and specificity for identifying four different caries stages and calculus were 98.6%, 98.4%, and 99.6%, respectively. CONCLUSIONS The proposed method can evaluate the whole tooth surface at the pixel level and provides discrimination enhancement and a quantitative parameter, which is expected to be a new approach for early caries diagnosis.
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25
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Vinayahalingam S, van Nistelrooij N, van Ginneken B, Bressem K, Tröltzsch D, Heiland M, Flügge T, Gaudin R. Detection of mandibular fractures on panoramic radiographs using deep learning. Sci Rep 2022; 12:19596. [PMID: 36379971 PMCID: PMC9666517 DOI: 10.1038/s41598-022-23445-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 10/31/2022] [Indexed: 11/16/2022] Open
Abstract
Mandibular fractures are among the most frequent facial traumas in oral and maxillofacial surgery, accounting for 57% of cases. An accurate diagnosis and appropriate treatment plan are vital in achieving optimal re-establishment of occlusion, function and facial aesthetics. This study aims to detect mandibular fractures on panoramic radiographs (PR) automatically. 1624 PR with fractures were manually annotated and labelled as a reference. A deep learning approach based on Faster R-CNN and Swin-Transformer was trained and validated on 1640 PR with and without fractures. Subsequently, the trained algorithm was applied to a test set consisting of 149 PR with and 171 PR without fractures. The detection accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an F1 score of 0.947 and an AUC of 0.977. Deep learning-based assistance of clinicians may reduce the misdiagnosis and hence the severe complications.
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Affiliation(s)
- Shankeeth Vinayahalingam
- grid.10417.330000 0004 0444 9382Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Postal Number 590, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands ,grid.7468.d0000 0001 2248 7639Department of Oral and Maxillofacial Surgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and HumboldtUniversität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Niels van Nistelrooij
- grid.10417.330000 0004 0444 9382Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Postal Number 590, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Bram van Ginneken
- grid.10417.330000 0004 0444 9382Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Postal Number 590, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Keno Bressem
- grid.7468.d0000 0001 2248 7639Department of Oral and Maxillofacial Surgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and HumboldtUniversität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany ,grid.484013.a0000 0004 6879 971XBerlin Institute of Health at Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Daniel Tröltzsch
- grid.7468.d0000 0001 2248 7639Department of Oral and Maxillofacial Surgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and HumboldtUniversität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Max Heiland
- grid.7468.d0000 0001 2248 7639Department of Oral and Maxillofacial Surgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and HumboldtUniversität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Tabea Flügge
- grid.7468.d0000 0001 2248 7639Department of Oral and Maxillofacial Surgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and HumboldtUniversität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany ,grid.512225.3Einstein Center for Digital Future, Wilhelmstraße 67, Berlin, Germany
| | - Robert Gaudin
- grid.7468.d0000 0001 2248 7639Department of Oral and Maxillofacial Surgery, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and HumboldtUniversität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
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Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review. Healthcare (Basel) 2022; 10:healthcare10101892. [PMID: 36292339 PMCID: PMC9602147 DOI: 10.3390/healthcare10101892] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/30/2022] [Accepted: 08/31/2022] [Indexed: 12/04/2022] Open
Abstract
Within the ever-growing healthcare industry, dental informatics is a burgeoning field of study. One of the major obstacles to the health care system’s transformation is obtaining knowledge and insightful data from complex, high-dimensional, and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, heterogeneous, poorly documented, and generally unstructured electronic health records, imaging, sensor data, and text. There were still certain restrictions even after many current techniques were used to extract more robust and useful elements from the data for analysis. New effective paradigms for building end-to-end learning models from complex data are provided by the most recent deep learning technology breakthroughs. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for dental informatics problems and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to some drawbacks and the need for better technique development and provide new perspectives about this exciting new development in the field.
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27
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Zhou X, Yu G, Yin Q, Liu Y, Zhang Z, Sun J. Context Aware Convolutional Neural Network for Children Caries Diagnosis on Dental Panoramic Radiographs. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:6029245. [PMID: 36188109 PMCID: PMC9519291 DOI: 10.1155/2022/6029245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 08/10/2022] [Accepted: 08/23/2022] [Indexed: 11/21/2022]
Abstract
The objective of this study is to improve traditional convolutional neural networks for more accurate children dental caries diagnosis on panoramic radiographs. A context aware convolutional neural network (CNN) is proposed by considering information among adjacent teeth, based on the fact that caries of teeth often affects each other due to the same growing environment. Specifically, when performing caries diagnosis on a tooth, information from its adjacent teeth will be collected and adaptively fused for final classification. Children panoramic radiographs of 210 patients with one or more caries and 94 patients without caries are utilized, among which there are a total of 6028 teeth with 3039 to be caries. The proposed context aware CNN outperforms typical CNN baseline with the accuracy, precision, recall, F1 score, and area-under-the-curve (AUC) being 0.8272, 0.8538, 0.8770, 0.8652, and 0.9005, respectively, showing potential to improve typical CNN instead of just copying them in previous works. Specially, the proposed method performs better than two five-year attending doctors for the second primary molar caries diagnosis. Considering the results obtained, it is beneficial to promote CNN based deep learning methods for assisting dentists for caries diagnosis in hospitals.
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Affiliation(s)
- Xiaojie Zhou
- Department of Stomatology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, China
| | - Guoxia Yu
- Department of Stomatology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, China
| | - Qiyue Yin
- Institute of Automation, Chinese Academy of Sciences, China
| | - Yan Liu
- Department of Stomatology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, China
| | - Zhiling Zhang
- Department of Stomatology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, China
| | - Jie Sun
- Department of Stomatology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, China
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Okazaki S, Mine Y, Iwamoto Y, Urabe S, Mitsuhata C, Nomura R, Kakimoto N, Murayama T. Analysis of the feasibility of using deep learning for multiclass classification of dental anomalies on panoramic radiographs. Dent Mater J 2022; 41:889-895. [PMID: 36002296 DOI: 10.4012/dmj.2022-098] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The aim of the feasibility study was to construct deep learning models for the classification of multiple dental anomalies in panoramic radiographs. Panoramic radiographs with single supernumerary teeth and/or odontomas were considered the "case" group; panoramic radiographs with no dental anomalies were considered the "control" group. The dataset comprised 150 panoramic radiographs: 50 each of no dental anomalies, single supernumerary teeth, and odontomas. To classify the panoramic radiographs into case and control categories, we employed AlexNet, which is a convolutional neural network model. AlexNet was able to classify whole panoramic radiographs into two or three classes, according to the presence or absence of supernumerary teeth or odontomas. The performance metrics of the three-class classification were 70%, 70.8%, 70%, and 69.7% for accuracy, precision, sensitivity, and F1 score, respectively, in the macro average. These results support the feasibility of using deep learning to detect multiple dental anomalies in panoramic radiographs.
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Affiliation(s)
- Shota Okazaki
- Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Yuichi Mine
- Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Yuko Iwamoto
- Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Shiho Urabe
- Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Chieko Mitsuhata
- Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Ryota Nomura
- Department of Pediatric Dentistry, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Naoya Kakimoto
- Department of Oral and Maxillofacial Radiology, Graduate School of Biomedical and Health Sciences, Hiroshima University
| | - Takeshi Murayama
- Department of Medical System Engineering, Graduate School of Biomedical and Health Sciences, Hiroshima University
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29
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Application of artificial intelligence technology in financial data inspection and manufacturing bond default prediction in small and medium-sized enterprises (SMEs). OPERATIONS MANAGEMENT RESEARCH 2022. [DOI: 10.1007/s12063-022-00314-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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Artificial Intelligence-Based Prediction of Oroantral Communication after Tooth Extraction Utilizing Preoperative Panoramic Radiography. Diagnostics (Basel) 2022; 12:diagnostics12061406. [PMID: 35741216 PMCID: PMC9221677 DOI: 10.3390/diagnostics12061406] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/02/2022] [Accepted: 06/04/2022] [Indexed: 02/01/2023] Open
Abstract
Oroantral communication (OAC) is a common complication after tooth extraction of upper molars. Profound preoperative panoramic radiography analysis might potentially help predict OAC following tooth extraction. In this exploratory study, we evaluated n = 300 consecutive cases (100 OAC and 200 controls) and trained five machine learning algorithms (VGG16, InceptionV3, MobileNetV2, EfficientNet, and ResNet50) to predict OAC versus non-OAC (binary classification task) from the input images. Further, four oral and maxillofacial experts evaluated the respective panoramic radiography and determined performance metrics (accuracy, area under the curve (AUC), precision, recall, F1-score, and receiver operating characteristics curve) of all diagnostic approaches. Cohen’s kappa was used to evaluate the agreement between expert evaluations. The deep learning algorithms reached high specificity (highest specificity 100% for InceptionV3) but low sensitivity (highest sensitivity 42.86% for MobileNetV2). The AUCs from VGG16, InceptionV3, MobileNetV2, EfficientNet, and ResNet50 were 0.53, 0.60, 0.67, 0.51, and 0.56, respectively. Expert 1–4 reached an AUC of 0.550, 0.629, 0.500, and 0.579, respectively. The specificity of the expert evaluations ranged from 51.74% to 95.02%, whereas sensitivity ranged from 14.14% to 59.60%. Cohen’s kappa revealed a poor agreement for the oral and maxillofacial expert evaluations (Cohen’s kappa: 0.1285). Overall, present data indicate that OAC cannot be sufficiently predicted from preoperative panoramic radiography. The false-negative rate, i.e., the rate of positive cases (OAC) missed by the deep learning algorithms, ranged from 57.14% to 95.24%. Surgeons should not solely rely on panoramic radiography when evaluating the probability of OAC occurrence. Clinical testing of OAC is warranted after each upper-molar tooth extraction.
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31
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Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review. Diagnostics (Basel) 2022; 12:diagnostics12051083. [PMID: 35626239 PMCID: PMC9139989 DOI: 10.3390/diagnostics12051083] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 04/12/2022] [Accepted: 04/25/2022] [Indexed: 01/27/2023] Open
Abstract
Evolution in the fields of science and technology has led to the development of newer applications based on Artificial Intelligence (AI) technology that have been widely used in medical sciences. AI-technology has been employed in a wide range of applications related to the diagnosis of oral diseases that have demonstrated phenomenal precision and accuracy in their performance. The aim of this systematic review is to report on the diagnostic accuracy and performance of AI-based models designed for detection, diagnosis, and prediction of dental caries (DC). Eminent electronic databases (PubMed, Google scholar, Scopus, Web of science, Embase, Cochrane, Saudi Digital Library) were searched for relevant articles that were published from January 2000 until February 2022. A total of 34 articles that met the selection criteria were critically analyzed based on QUADAS-2 guidelines. The certainty of the evidence of the included studies was assessed using the GRADE approach. AI has been widely applied for prediction of DC, for detection and diagnosis of DC and for classification of DC. These models have demonstrated excellent performance and can be used in clinical practice for enhancing the diagnostic performance, treatment quality and patient outcome and can also be applied to identify patients with a higher risk of developing DC.
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32
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Engels P, Meyer O, Schönewolf J, Schlickenrieder A, Hickel R, Hesenius M, Gruhn V, Kühnisch J. Automated detection of posterior restorations in permanent teeth using artificial intelligence on intraoral photographs. J Dent 2022; 121:104124. [PMID: 35395346 DOI: 10.1016/j.jdent.2022.104124] [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: 02/28/2022] [Accepted: 03/31/2022] [Indexed: 10/18/2022] Open
Abstract
OBJECTIVES Intraoral photographs might be considered the machine-readable equivalent of a clinical-based visual examination and can potentially be used to detect and categorize dental restorations. The first objective of this study was to develop a deep learning-based convolutional neural network (CNN) for automated detection and categorization of posterior composite, cement, amalgam, gold and ceramic restorations on clinical photographs. Second, this study aimed to determine the diagnostic accuracy for the developed CNN (test method) compared to that of an expert evaluation (reference standard). METHODS The whole image set of 1,761 images (483 of unrestored teeth, 570 of composite restorations, 213 of cements, 278 of amalgam restorations, 125 of gold restorations and 92 of ceramic restorations) was divided into a training set (N=1,407, 401, 447, 66, 231, 93, and 169, respectively) and a test set (N=354, 82, 123, 26, 47, 32, and 44). The expert diagnoses served as a reference standard for cyclic training and repeated evaluation of the CNN (ResNeXt-101-32x8d), which was trained by using image augmentation and transfer learning. Statistical analysis included the calculation of contingency tables, areas under the receiver operating characteristic curve and saliency maps. RESULTS After training was complete, the CNN was able to categorize restorations correctly with the following diagnostic accuracy values: 94.9% for unrestored teeth, 92.9% for composites, 98.3% for cements, 99.2% for amalgam restorations, 99.4% for gold restorations and 97.8% for ceramic restorations. CONCLUSIONS It was possible to categorize different types of posterior restorations on intraoral photographs automatically with a good diagnostic accuracy. CLINICAL SIGNIFICANCE Dental diagnostics might be supported by artificial intelligence-based algorithms in the future. However, further improvements are needed to increase accuracy and practicability.
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Affiliation(s)
- Paula Engels
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University Munich, Germany
| | - Ole Meyer
- Institute for Software Engineering, University of Duisburg-Essen, Essen, Germany
| | - Jule Schönewolf
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University Munich, Germany
| | - Anne Schlickenrieder
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University Munich, Germany
| | - Reinhard Hickel
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University Munich, Germany
| | - Marc Hesenius
- Institute for Software Engineering, University of Duisburg-Essen, Essen, Germany
| | - Volker Gruhn
- Institute for Software Engineering, University of Duisburg-Essen, Essen, Germany
| | - Jan Kühnisch
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University Munich, Germany.
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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.
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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
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Deep learning based diagnosis for cysts and tumors of jaw with massive healthy samples. Sci Rep 2022; 12:1855. [PMID: 35115624 PMCID: PMC8814152 DOI: 10.1038/s41598-022-05913-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 01/14/2022] [Indexed: 11/09/2022] Open
Abstract
We aimed to develop an explainable and reliable method to diagnose cysts and tumors of the jaw with massive panoramic radiographs of healthy peoples based on deep learning, since collecting and labeling massive lesion samples are time-consuming, and existing deep learning-based methods lack explainability. Based on the collected 872 lesion samples and 10,000 healthy samples, a two-branch network was proposed for classifying the cysts and tumors of the jaw. The two-branch network is firstly pretrained on massive panoramic radiographs of healthy peoples, then is trained for classifying the sample categories and segmenting the lesion area. Totally, 200 healthy samples and 87 lesion samples were included in the testing stage. The average accuracy, precision, sensitivity, specificity, and F1 score of classification are 88.72%, 65.81%, 66.56%, 92.66%, and 66.14%, respectively. The average accuracy, precision, sensitivity, specificity, and F1 score of classification will reach 90.66%, 85.23%, 84.27%, 93.50%, and 84.74%, if only classifying the lesion samples and healthy samples. The proposed method showed encouraging performance in the diagnosis of cysts and tumors of the jaw. The classified categories and segmented lesion areas serve as the diagnostic basis for further diagnosis, which provides a reliable tool for diagnosing jaw tumors and cysts.
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Chen L, Liu Z, Su H, Lin F, Mao W. Automated rock mass condition assessment during TBM tunnel excavation using deep learning. Sci Rep 2022; 12:1722. [PMID: 35110623 PMCID: PMC8810907 DOI: 10.1038/s41598-022-05727-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 01/18/2022] [Indexed: 11/09/2022] Open
Abstract
Rock mass condition assessment during tunnel excavation is a critical step for the intelligent control of tunnel boring machine (TBM). To address this and achieve automatic detection, a visual assessment system is installed to the TBM and a lager in-situ rock mass image dataset is collected from the water conveyance channel project. The rock mass condition assessment task is transformed into a fine-grain classification task. To fulfill the task, a self-convolution based attention fusion network (SAFN) is designed in this paper. The core of our method is the discovery and fusion of the object attention map within a deep neural network. The network consists of two novel modules, the self-convolution based attention extractor (SAE) module and the self-convolution based attention pooling algorithm (SAP) module. The former is designed to detect the intact rock regions generating the attention map, and the latter is designed to improve the performance of classifier by fusing the attention map that focuses on the intact rock regions. The results of SAFN are evaluated from aspects of interpretability, ablation, accuracy and cross-validation, and it outperforms state-of-the-art models in the rock mass assessment dataset. Furthermore, the dynamic filed test show that our assessment system based on the SAFN model is accurate and efficient for automated classification of rock mass.
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Affiliation(s)
- Liang Chen
- State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, 310027, China
| | - Zhitao Liu
- State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, 310027, China
| | - Hongye Su
- State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, 310027, China
| | - Fulong Lin
- General Institute of Design and Research, China Railway Engineering Equipment Group Co., LTD, Zhengzhou, 450016, China
| | - Weijie Mao
- State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, 310027, China.
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Automated Prediction of Extraction Difficulty and Inferior Alveolar Nerve Injury for Mandibular Third Molar Using a Deep Neural Network. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12010475] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Extraction of mandibular third molars is a common procedure in oral and maxillofacial surgery. There are studies that simultaneously predict the extraction difficulty of mandibular third molar and the complications that may occur. Thus, we propose a method of automatically detecting mandibular third molars in the panoramic radiographic images and predicting the extraction difficulty and likelihood of inferior alveolar nerve (IAN) injury. Our dataset consists of 4903 panoramic radiographic images acquired from various dental hospitals. Seven dentists annotated detection and classification labels. The detection model determines the mandibular third molar in the panoramic radiographic image. The region of interest (ROI) includes the detected mandibular third molar, adjacent teeth, and IAN, which is cropped in the panoramic radiographic image. The classification models use ROI as input to predict the extraction difficulty and likelihood of IAN injury. The achieved detection performance was 99.0% mAP over the intersection of union (IOU) 0.5. In addition, we achieved an 83.5% accuracy for the prediction of extraction difficulty and an 81.1% accuracy for the prediction of the likelihood of IAN injury. We demonstrated that a deep learning method can support the diagnosis for extracting the mandibular third molar.
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Vinayahalingam S, Goey RS, Kempers S, Schoep J, Cherici T, Moin DA, Hanisch M. Automated chart filing on panoramic radiographs using deep learning. J Dent 2021; 115:103864. [PMID: 34715247 DOI: 10.1016/j.jdent.2021.103864] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 10/19/2021] [Accepted: 10/24/2021] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE The aim of this study is to automatically detect, segment and label teeth, crowns, fillings, root canal fillings, implants and root remnants on panoramic radiographs (PR(s)). MATERIAL AND METHODS As a reference, 2000 PR(s) were manually annotated and labeled. A deep-learning approach based on mask R-CNN with Resnet-50 in combination with a rule-based heuristic algorithm and a combinatorial search algorithm was trained and validated on 1800 PR(s). Subsquently, the trained algorithm was applied onto a test set consisting of 200 PR(s). F1 scores, as a measure of accuracy, were calculated to quantify the degree of similarity between the annotated ground-truth and the model predictions. The F1-score considers the harmonic mean of precison (positive predictive value) and recall (specificity). RESULTS The proposes method achieved F1 scores up to 0.993, 0.952 and 0.97 for detection, segmentation and labeling, respectivley. CONCLUSION The proposed method forms a promising foundation for the further development of automatic chart filing on PR(s). CLINICAL SIGNIFICANCE Deep learning may assist clinicians in summarizing the radiological findings on panoramic radiographs. The impact of using such models in clinical practice should be explored.
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Affiliation(s)
- Shankeeth Vinayahalingam
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB, Nijmegen, the Netherlands; Department of Artificial Intelligence, Radboud University, Nijmegen, the Netherlands; Department of Oral and Maxillofacial Surgery, Universitätsklinikum Münster, Münster, Germany.
| | - Ru-Shan Goey
- Promaton Co. Ltd., Amsterdam 1076 GR, the Netherlands
| | - Steven Kempers
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB, Nijmegen, the Netherlands; Department of Artificial Intelligence, Radboud University, Nijmegen, the Netherlands
| | - Julian Schoep
- Promaton Co. Ltd., Amsterdam 1076 GR, the Netherlands
| | - Teo Cherici
- Promaton Co. Ltd., Amsterdam 1076 GR, the Netherlands
| | | | - Marcel Hanisch
- Promaton Co. Ltd., Amsterdam 1076 GR, the Netherlands; Department of Oral and Maxillofacial Surgery, Universitätsklinikum Münster, Münster, Germany
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Schlickenrieder A, Meyer O, Schönewolf J, Engels P, Hickel R, Gruhn V, Hesenius M, Kühnisch J. Automatized Detection and Categorization of Fissure Sealants from Intraoral Digital Photographs Using Artificial Intelligence. Diagnostics (Basel) 2021; 11:1608. [PMID: 34573949 PMCID: PMC8469974 DOI: 10.3390/diagnostics11091608] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 08/31/2021] [Accepted: 09/01/2021] [Indexed: 02/02/2023] Open
Abstract
The aim of the present study was to investigate the diagnostic performance of a trained convolutional neural network (CNN) for detecting and categorizing fissure sealants from intraoral photographs using the expert standard as reference. An image set consisting of 2352 digital photographs from permanent posterior teeth (461 unsealed tooth surfaces/1891 sealed surfaces) was divided into a training set (n = 1881/364/1517) and a test set (n = 471/97/374). All the images were scored according to the following categories: unsealed molar, intact, sufficient and insufficient sealant. Expert diagnoses served as the reference standard for cyclic training and repeated evaluation of the CNN (ResNeXt-101-32x8d), which was trained by using image augmentation and transfer learning. A statistical analysis was performed, including the calculation of contingency tables and areas under the receiver operating characteristic curve (AUC). The results showed that the CNN accurately detected sealants in 98.7% of all the test images, corresponding to an AUC of 0.996. The diagnostic accuracy and AUC were 89.6% and 0.951, respectively, for intact sealant; 83.2% and 0.888, respectively, for sufficient sealant; 92.4 and 0.942, respectively, for insufficient sealant. On the basis of the documented results, it was concluded that good agreement with the reference standard could be achieved for automatized sealant detection by using artificial intelligence methods. Nevertheless, further research is necessary to improve the model performance.
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Affiliation(s)
- Anne Schlickenrieder
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University Munich, 80336 Munich, Germany; (A.S.); (J.S.); (P.E.); (R.H.)
| | - Ole Meyer
- Institute for Software Engineering, University of Duisburg-Essen, 45147 Essen, Germany; (O.M.); (V.G.); (M.H.)
| | - Jule Schönewolf
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University Munich, 80336 Munich, Germany; (A.S.); (J.S.); (P.E.); (R.H.)
| | - Paula Engels
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University Munich, 80336 Munich, Germany; (A.S.); (J.S.); (P.E.); (R.H.)
| | - Reinhard Hickel
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University Munich, 80336 Munich, Germany; (A.S.); (J.S.); (P.E.); (R.H.)
| | - Volker Gruhn
- Institute for Software Engineering, University of Duisburg-Essen, 45147 Essen, Germany; (O.M.); (V.G.); (M.H.)
| | - Marc Hesenius
- Institute for Software Engineering, University of Duisburg-Essen, 45147 Essen, Germany; (O.M.); (V.G.); (M.H.)
| | - Jan Kühnisch
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University Munich, 80336 Munich, Germany; (A.S.); (J.S.); (P.E.); (R.H.)
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