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Altun O, Özen DÇ, Duman ŞB, Dedeoğlu N, Bayrakdar İŞ, Eşer G, Çelik Ö, Sümbüllü MA, Syed AZ. Automatic maxillary sinus segmentation and pathology classification on cone-beam computed tomographic images using deep learning. BMC Oral Health 2024; 24:1208. [PMID: 39390490 PMCID: PMC11468140 DOI: 10.1186/s12903-024-04924-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Accepted: 09/18/2024] [Indexed: 10/12/2024] Open
Abstract
BACKGROUND Maxillofacial complex automated segmentation could alternative traditional segmentation methods to increase the effectiveness of virtual workloads. The use of DL systems in the detection of maxillary sinus and pathologies will both facilitate the work of physicians and be a support mechanism before the planned surgeries. OBJECTIVE The aim was to use a modified You Only Look Oncev5x (YOLOv5x) architecture with transfer learning capabilities to segment both maxillary sinuses and maxillary sinus diseases on Cone-Beam Computed Tomographic (CBCT) images. METHODS Data set consists of 307 anonymised CBCT images of patients (173 women and 134 males) obtained from the radiology archive of the Department of Oral and Maxillofacial Radiology. Bilateral maxillary sinuses CBCT scans were used to identify mucous retention cysts (MRC), mucosal thickenings (MT), total and partial opacifications, and healthy maxillary sinuses without any radiological features. RESULTS Recall, precision and F1 score values for total maxillary sinus segmentation were 1, 0.985 and 0.992, respectively; 1, 0.931 and 0.964 for healthy maxillary sinus segmentation; 0.858, 0.923 and 0.889 for MT segmentation; 0.977, 0.877 and 0.924 for MRC segmentation; 1, 0.942 and 0.970 for sinusitis segmentation. CONCLUSION This study demonstrates that maxillary sinuses can be segmented, and maxillary sinus diseases can be accurately detected using the AI model.
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Affiliation(s)
- Oğuzhan Altun
- Department of Oral and Dentomaxillofacial Radiology, Faculty of Dentistry, Inonu University, Malatya, Turkey
| | - Duygu Çelik Özen
- Department of Oral and Dentomaxillofacial Radiology, Faculty of Dentistry, Inonu University, Malatya, Turkey
| | - Şuayip Burak Duman
- Department of Oral and Dentomaxillofacial Radiology, Faculty of Dentistry, Inonu University, Malatya, Turkey.
- Department of Oral and Maxillofacial Medicine and Diagnostic Sciences, School of Dental Medicine, Case Western Reserve University, Cleveland, OH, USA.
| | - Numan Dedeoğlu
- Department of Oral and Dentomaxillofacial Radiology, Faculty of Dentistry, Inonu University, Malatya, Turkey
| | - İbrahim Şevki Bayrakdar
- Department of Oral and Dentomaxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Gözde Eşer
- Department of Oral and Dentomaxillofacial Radiology, Faculty of Dentistry, Inonu University, Malatya, Turkey
| | - Özer Çelik
- Department of Mathematics-Computer, Eskişehir Osmangazi University Faculty of Science, Eskişehir, Turkey
| | - Muhammed Akif Sümbüllü
- Department of Oral and Dentomaxillofacial Radiology, Faculty of Dentistry, Ataturk University, Erzurum, Turkey
| | - Ali Zakir Syed
- Department of Oral and Maxillofacial Medicine and Diagnostic Sciences, School of Dental Medicine, Case Western Reserve University, Cleveland, OH, USA
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Zou C, Ji H, Cui J, Qian B, Chen YC, Zhang Q, He S, Sui Y, Bai Y, Zhong Y, Zhang X, Ni T, Che Z. Preliminary study on AI-assisted diagnosis of bone remodeling in chronic maxillary sinusitis. BMC Med Imaging 2024; 24:140. [PMID: 38858631 PMCID: PMC11165780 DOI: 10.1186/s12880-024-01316-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/30/2024] [Indexed: 06/12/2024] Open
Abstract
OBJECTIVE To construct the deep learning convolution neural network (CNN) model and machine learning support vector machine (SVM) model of bone remodeling of chronic maxillary sinusitis (CMS) based on CT image data to improve the accuracy of image diagnosis. METHODS Maxillary sinus CT data of 1000 samples in 500 patients from January 2018 to December 2021 in our hospital was collected. The first part is the establishment and testing of chronic maxillary sinusitis detection model by 461 images. The second part is the establishment and testing of the detection model of chronic maxillary sinusitis with bone remodeling by 802 images. The sensitivity, specificity and accuracy and area under the curve (AUC) value of the test set were recorded, respectively. RESULTS Preliminary application results of CT based AI in the diagnosis of chronic maxillary sinusitis and bone remodeling. The sensitivity, specificity and accuracy of the test set of 93 samples of CMS, were 0.9796, 0.8636 and 0.9247, respectively. Simultaneously, the value of AUC was 0.94. And the sensitivity, specificity and accuracy of the test set of 161 samples of CMS with bone remodeling were 0.7353, 0.9685 and 0.9193, respectively. Simultaneously, the value of AUC was 0.89. CONCLUSION It is feasible to use artificial intelligence research methods such as deep learning and machine learning to automatically identify CMS and bone remodeling in MSCT images of paranasal sinuses, which is helpful to standardize imaging diagnosis and meet the needs of clinical application.
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Affiliation(s)
- Caiyun Zou
- Department of Radiology, Nanjing Tongren Hospital, School of Medicine, Southeast University, No. 2007, Ji Yin Avenue, Jiang Ning District, Nanjing, 211102, PR China
| | - Hongbo Ji
- Department of Radiology, Nanjing Tongren Hospital, School of Medicine, Southeast University, No. 2007, Ji Yin Avenue, Jiang Ning District, Nanjing, 211102, PR China
| | - Jie Cui
- Department of Radiology, Nanjing Tongren Hospital, School of Medicine, Southeast University, No. 2007, Ji Yin Avenue, Jiang Ning District, Nanjing, 211102, PR China
| | - Bo Qian
- Department of Radiology, Nanjing Tongren Hospital, School of Medicine, Southeast University, No. 2007, Ji Yin Avenue, Jiang Ning District, Nanjing, 211102, PR China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, PR China
| | - Qingxiang Zhang
- Department of Otolaryngology Head and Neck Surgery, Nanjing Tongren Hospital, School of Medicine, Southeast University, Nanjing, PR China
| | - Shuangba He
- Department of Otolaryngology Head and Neck Surgery, Nanjing Tongren Hospital, School of Medicine, Southeast University, Nanjing, PR China
| | - Yang Sui
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, PR China
| | - Yang Bai
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, PR China
| | - Yeming Zhong
- Department of Radiology, Nanjing Tongren Hospital, School of Medicine, Southeast University, No. 2007, Ji Yin Avenue, Jiang Ning District, Nanjing, 211102, PR China
| | - Xu Zhang
- Department of Radiology, Nanjing Tongren Hospital, School of Medicine, Southeast University, No. 2007, Ji Yin Avenue, Jiang Ning District, Nanjing, 211102, PR China
| | - Ting Ni
- Department of Radiology, Nanjing Tongren Hospital, School of Medicine, Southeast University, No. 2007, Ji Yin Avenue, Jiang Ning District, Nanjing, 211102, PR China
| | - Zigang Che
- Department of Radiology, Nanjing Tongren Hospital, School of Medicine, Southeast University, No. 2007, Ji Yin Avenue, Jiang Ning District, Nanjing, 211102, PR China.
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Kise Y, Kuwada C, Mori M, Fukuda M, Ariji Y, Ariji E. Deep learning system for distinguishing between nasopalatine duct cysts and radicular cysts arising in the midline region of the anterior maxilla on panoramic radiographs. Imaging Sci Dent 2024; 54:33-41. [PMID: 38571775 PMCID: PMC10985522 DOI: 10.5624/isd.20230169] [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: 08/04/2023] [Revised: 10/31/2023] [Accepted: 11/22/2023] [Indexed: 04/05/2024] Open
Abstract
Purpose The aims of this study were to create a deep learning model to distinguish between nasopalatine duct cysts (NDCs), radicular cysts, and no-lesions (normal) in the midline region of the anterior maxilla on panoramic radiographs and to compare its performance with that of dental residents. Materials and Methods One hundred patients with a confirmed diagnosis of NDC (53 men, 47 women; average age, 44.6±16.5 years), 100 with radicular cysts (49 men, 51 women; average age, 47.5±16.4 years), and 100 with normal groups (56 men, 44 women; average age, 34.4±14.6 years) were enrolled in this study. Cases were randomly assigned to the training datasets (80%) and the test dataset (20%). Then, 20% of the training data were randomly assigned as validation data. A learning model was created using a customized DetectNet built in Digits version 5.0 (NVIDIA, Santa Clara, USA). The performance of the deep learning system was assessed and compared with that of two dental residents. Results The performance of the deep learning system was superior to that of the dental residents except for the recall of radicular cysts. The areas under the curve (AUCs) for NDCs and radicular cysts in the deep learning system were significantly higher than those of the dental residents. The results for the dental residents revealed a significant difference in AUC between NDCs and normal groups. Conclusion This study showed superior performance in detecting NDCs and radicular cysts and in distinguishing between these lesions and normal groups.
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Affiliation(s)
- Yoshitaka Kise
- Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan
| | - Chiaki Kuwada
- Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan
| | - Mizuho Mori
- Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan
| | - Motoki Fukuda
- Department of Oral Radiology, School of Dentistry, Osaka Dental University, Osaka, Japan
| | - Yoshiko Ariji
- Department of Oral Radiology, School of Dentistry, Osaka Dental University, Osaka, Japan
| | - Eiichiro Ariji
- Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan
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Gwak M, Yun JP, Lee JY, Han SS, Park P, Lee C. Attention-guided jaw bone lesion diagnosis in panoramic radiography using minimal labeling effort. Sci Rep 2024; 14:4981. [PMID: 38424124 PMCID: PMC10904826 DOI: 10.1038/s41598-024-55677-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 02/26/2024] [Indexed: 03/02/2024] Open
Abstract
Developing a deep-learning-based diagnostic model demands extensive labor for medical image labeling. Attempts to reduce the labor often lead to incomplete or inaccurate labeling, limiting the diagnostic performance of models. This paper (i) constructs an attention-guiding framework that enhances the diagnostic performance of jaw bone pathology by utilizing attention information with partially labeled data; (ii) introduces an additional loss to minimize the discrepancy between network attention and its label; (iii) introduces a trapezoid augmentation method to maximize the utility of minimally labeled data. The dataset includes 716 panoramic radiograph data for jaw bone lesions and normal cases collected and labeled by two radiologists from January 2019 to February 2021. Experiments show that guiding network attention with even 5% of attention-labeled data can enhance the diagnostic accuracy for pathology from 92.41 to 96.57%. Furthermore, ablation studies reveal that the proposed augmentation methods outperform prior preprocessing and augmentation combinations, achieving an accuracy of 99.17%. The results affirm the capability of the proposed framework in fine-grained diagnosis using minimally labeled data, offering a practical solution to the challenges of medical image analysis.
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Affiliation(s)
- Minseon Gwak
- Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea
| | - Jong Pil Yun
- Manufacturing AI Research Center, Korea Institute of Industrial Technology, Incheon, 21999, Republic of Korea
- KITECH School, University of Science and Technology, Daejeon, 34113, Republic of Korea
| | - Ji Yun Lee
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, 03722, Republic of Korea
| | - Sang-Sun Han
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, 03722, Republic of Korea
| | - PooGyeon Park
- Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea.
| | - Chena Lee
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, 03722, Republic of Korea.
- Institute for Innovation in Digital Healthcare, Yonsei University, Seoul, 03722, Republic of Korea.
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Tassoker M, Öziç MÜ, Yuce F. Performance evaluation of a deep learning model for automatic detection and localization of idiopathic osteosclerosis on dental panoramic radiographs. Sci Rep 2024; 14:4437. [PMID: 38396289 PMCID: PMC10891049 DOI: 10.1038/s41598-024-55109-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 02/20/2024] [Indexed: 02/25/2024] Open
Abstract
Idiopathic osteosclerosis (IO) are focal radiopacities of unknown etiology observed in the jaws. These radiopacities are incidentally detected on dental panoramic radiographs taken for other reasons. In this study, we investigated the performance of a deep learning model in detecting IO using a small dataset of dental panoramic radiographs with varying contrasts and features. Two radiologists collected 175 IO-diagnosed dental panoramic radiographs from the dental school database. The dataset size is limited due to the rarity of IO, with its incidence in the Turkish population reported as 2.7% in studies. To overcome this limitation, data augmentation was performed by horizontally flipping the images, resulting in an augmented dataset of 350 panoramic radiographs. The images were annotated by two radiologists and divided into approximately 70% for training (245 radiographs), 15% for validation (53 radiographs), and 15% for testing (52 radiographs). The study employing the YOLOv5 deep learning model evaluated the results using precision, recall, F1-score, mAP (mean Average Precision), and average inference time score metrics. The training and testing processes were conducted on the Google Colab Pro virtual machine. The test process's performance criteria were obtained with a precision value of 0.981, a recall value of 0.929, an F1-score value of 0.954, and an average inference time of 25.4 ms. Although radiographs diagnosed with IO have a small dataset and exhibit different contrasts and features, it has been observed that the deep learning model provides high detection speed, accuracy, and localization results. The automatic identification of IO lesions using artificial intelligence algorithms, with high success rates, can contribute to the clinical workflow of dentists by preventing unnecessary biopsy procedure.
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Affiliation(s)
- Melek Tassoker
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Necmettin Erbakan University, Bağlarbaşı Street, 42090, Meram, Konya, Turkey.
| | - Muhammet Üsame Öziç
- Faculty of Technology, Department of Biomedical Engineering, Pamukkale University, Denizli, Turkey
| | - Fatma Yuce
- Faculty of Dentistry, Department of Oral and Maxillofacial Radiology, Istanbul Okan University, Istanbul, Turkey
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Bağ İ, Bilgir E, Bayrakdar İŞ, Baydar O, Atak FM, Çelik Ö, Orhan K. An artificial intelligence study: automatic description of anatomic landmarks on panoramic radiographs in the pediatric population. BMC Oral Health 2023; 23:764. [PMID: 37848870 PMCID: PMC10583406 DOI: 10.1186/s12903-023-03532-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 10/11/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND Panoramic radiographs, in which anatomic landmarks can be observed, are used to detect cases closely related to pediatric dentistry. The purpose of the study is to investigate the success and reliability of the detection of maxillary and mandibular anatomic structures observed on panoramic radiographs in children using artificial intelligence. METHODS A total of 981 mixed images of pediatric patients for 9 different pediatric anatomic landmarks including maxillary sinus, orbita, mandibular canal, mental foramen, foramen mandible, incisura mandible, articular eminence, condylar and coronoid processes were labelled, the training was carried out using 2D convolutional neural networks (CNN) architectures, by giving 500 training epochs and Pytorch-implemented YOLO-v5 models were produced. The success rate of the AI model prediction was tested on a 10% test data set. RESULTS A total of 14,804 labels including maxillary sinus (1922), orbita (1944), mandibular canal (1879), mental foramen (884), foramen mandible (1885), incisura mandible (1922), articular eminence (1645), condylar (1733) and coronoid (990) processes were made. The most successful F1 Scores were obtained from orbita (1), incisura mandible (0.99), maxillary sinus (0.98), and mandibular canal (0.97). The best sensitivity values were obtained from orbita, maxillary sinus, mandibular canal, incisura mandible, and condylar process. The worst sensitivity values were obtained from mental foramen (0.92) and articular eminence (0.92). CONCLUSIONS The regular and standardized labelling, the relatively larger areas, and the success of the YOLO-v5 algorithm contributed to obtaining these successful results. Automatic segmentation of these structures will save time for physicians in clinical diagnosis and will increase the visibility of pathologies related to structures and the awareness of physicians.
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Affiliation(s)
- İrem Bağ
- Department of Pediatric Dentistry, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey.
| | - Elif Bilgir
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey
| | - İbrahim Şevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey
| | - Oğuzhan Baydar
- Dentomaxillofacial Radiology Specialist, Faculty of Dentistry, Ege University, İzmir, Turkey
| | - Fatih Mehmet Atak
- Department of Computer Engineering, The Faculty of Engineering, Boğaziçi University, İstanbul, Turkey
| | - Özer Çelik
- Department of Mathematics-Computer, Eskisehir Osmangazi University Faculty of Science, Eskisehir, Turkey
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
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郑 天, 杨 娜, 耿 诗, 赵 先, 王 跃, 程 德, 赵 蕾. [An Improved Object Detection Algorithm for Thyroid Nodule Ultrasound Image Based on Faster R-CNN]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2023; 54:915-922. [PMID: 37866946 PMCID: PMC10579083 DOI: 10.12182/20230960106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Indexed: 10/24/2023]
Abstract
Objective To propose an improved algorithm for thyroid nodule object detection based on Faster R-CNN so as to improve the detection precision of thyroid nodules in ultrasound images. Methods The algorithm used ResNeSt50 combined with deformable convolution (DC) as the backbone network to improve the detection effect of irregularly shaped nodules. Feature pyramid networks (FPN) and Region of Interest (RoI) Align were introduced in the back of the trunk network. The former was used to reduce missed or mistaken detection of thyroid nodules, and the latter was used to improve the detection precision of small nodules. To improve the generalization ability of the model, parameters were updated during backpropagation with an optimizer improved by Sharpness-Aware Minimization (SAM). Results In this experiment, 6 261 thyroid ultrasound images from the Affiliated Hospital of Xuzhou Medical University and the First Hospital of Nanjing were used to compare and evaluate the effectiveness of the improved algorithm. According to the findings, the algorithm showed optimization effect to a certain degree, with the AP50 of the final test set being as high as 97.4% and AP@50:5:95 also showing a 10.0% improvement compared with the original model. Compared with both the original model and the existing models, the improved algorithm had higher detection precision and improved capacity to detect thyroid nodules with better accuracy and precision. In particular, the improved algorithm had a higher recall rate under the requirement of lower detection frame precision. Conclusion The improved method proposed in the study is an effective object detection algorithm for thyroid nodules and can be used to detect thyroid nodules with accuracy and precision.
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Affiliation(s)
- 天雷 郑
- 中国矿业大学信息与控制工程学院 (徐州 221116)School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
- 徐州医科大学附属医院 医疗设备管理处 人工智能研究组 (徐州 221004)Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou 221004, China
| | - 娜 杨
- 中国矿业大学信息与控制工程学院 (徐州 221116)School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - 诗 耿
- 中国矿业大学信息与控制工程学院 (徐州 221116)School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - 先云 赵
- 中国矿业大学信息与控制工程学院 (徐州 221116)School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - 跃 王
- 中国矿业大学信息与控制工程学院 (徐州 221116)School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - 德强 程
- 中国矿业大学信息与控制工程学院 (徐州 221116)School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - 蕾 赵
- 中国矿业大学信息与控制工程学院 (徐州 221116)School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
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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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9
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Amanian A, Heffernan A, Ishii M, Creighton FX, Thamboo A. The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review. Otolaryngol Head Neck Surg 2023; 169:21-30. [PMID: 35787221 PMCID: PMC11110957 DOI: 10.1177/01945998221110076] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/10/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To provide a comprehensive overview on the applications of artificial intelligence (AI) in rhinology, highlight its limitations, and propose strategies for its integration into surgical practice. DATA SOURCES Medline, Embase, CENTRAL, Ei Compendex, IEEE, and Web of Science. REVIEW METHODS English studies from inception until January 2022 and those focusing on any application of AI in rhinology were included. Study selection was independently performed by 2 authors; discrepancies were resolved by the senior author. Studies were categorized by rhinology theme, and data collection comprised type of AI utilized, sample size, and outcomes, including accuracy and precision among others. CONCLUSIONS An overall 5435 articles were identified. Following abstract and title screening, 130 articles underwent full-text review, and 59 articles were selected for analysis. Eleven studies were from the gray literature. Articles were stratified into image processing, segmentation, and diagnostics (n = 27); rhinosinusitis classification (n = 14); treatment and disease outcome prediction (n = 8); optimizing surgical navigation and phase assessment (n = 3); robotic surgery (n = 2); olfactory dysfunction (n = 2); and diagnosis of allergic rhinitis (n = 3). Most AI studies were published from 2016 onward (n = 45). IMPLICATIONS FOR PRACTICE This state of the art review aimed to highlight the increasing applications of AI in rhinology. Next steps will entail multidisciplinary collaboration to ensure data integrity, ongoing validation of AI algorithms, and integration into clinical practice. Future research should be tailored at the interplay of AI with robotics and surgical education.
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Affiliation(s)
- Ameen Amanian
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery, University of British Columbia, Vancouver, Canada
| | - Austin Heffernan
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery, University of British Columbia, Vancouver, Canada
| | - Masaru Ishii
- Department of Otolaryngology–Head and Neck Surgery, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Francis X. Creighton
- Department of Otolaryngology–Head and Neck Surgery, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Andrew Thamboo
- Division of Otolaryngology–Head and Neck Surgery, Department of Surgery, University of British Columbia, Vancouver, Canada
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Vodanović M, Subašić M, Milošević D, Savić Pavičin I. Artificial Intelligence in Medicine and Dentistry. Acta Stomatol Croat 2023; 57:70-84. [PMID: 37288152 PMCID: PMC10243707 DOI: 10.15644/asc57/1/8] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/01/2023] [Indexed: 09/14/2023] Open
Abstract
INTRODUCTION Artificial intelligence has been applied in various fields throughout history, but its integration into daily life is more recent. The first applications of AI were primarily in academia and government research institutions, but as technology has advanced, AI has also been applied in industry, commerce, medicine and dentistry. OBJECTIVE Considering that the possibilities of applying artificial intelligence are developing rapidly and that this field is one of the areas with the greatest increase in the number of newly published articles, the aim of this paper was to provide an overview of the literature and to give an insight into the possibilities of applying artificial intelligence in medicine and dentistry. In addition, the aim was to discuss its advantages and disadvantages. CONCLUSION The possibilities of applying artificial intelligence to medicine and dentistry are just being discovered. Artificial intelligence will greatly contribute to developments in medicine and dentistry, as it is a tool that enables development and progress, especially in terms of personalized healthcare that will lead to much better treatment outcomes.
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Affiliation(s)
- Marin Vodanović
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
| | - Marko Subašić
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Denis Milošević
- Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia
| | - Ivana Savić Pavičin
- Department of Dental Anthropology, School of Dental Medicine, University of Zagreb, Croatia
- University Hospital Centre Zagreb, Croatia
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11
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Artificial intelligence, machine learning, and deep learning in rhinology: a systematic review. Eur Arch Otorhinolaryngol 2023; 280:529-542. [PMID: 36260141 PMCID: PMC9849161 DOI: 10.1007/s00405-022-07701-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 10/10/2022] [Indexed: 01/22/2023]
Abstract
PURPOSE This PRISMA-compliant systematic review aims to analyze the existing applications of artificial intelligence (AI), machine learning, and deep learning for rhinological purposes and compare works in terms of data pool size, AI systems, input and outputs, and model reliability. METHODS MEDLINE, Embase, Web of Science, Cochrane Library, and ClinicalTrials.gov databases. Search criteria were designed to include all studies published until December 2021 presenting or employing AI for rhinological applications. We selected all original studies specifying AI models reliability. After duplicate removal, abstract and full-text selection, and quality assessment, we reviewed eligible articles for data pool size, AI tools used, input and outputs, and model reliability. RESULTS Among 1378 unique citations, 39 studies were deemed eligible. Most studies (n = 29) were technical papers. Input included compiled data, verbal data, and 2D images, while outputs were in most cases dichotomous or selected among nominal classes. The most frequently employed AI tools were support vector machine for compiled data and convolutional neural network for 2D images. Model reliability was variable, but in most cases was reported to be between 80% and 100%. CONCLUSIONS AI has vast potential in rhinology, but an inherent lack of accessible code sources does not allow for sharing results and advancing research without reconstructing models from scratch. While data pools do not necessarily represent a problem for model construction, presently available tools appear limited in allowing employment of raw clinical data, thus demanding immense interpretive work prior to the analytic process.
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Miloglu O, Guller MT, Tosun ZT. The Use of Artificial Intelligence in Dentistry Practices. Eurasian J Med 2022; 54:34-42. [PMID: 36655443 PMCID: PMC11163356 DOI: 10.5152/eurasianjmed.2022.22301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 11/30/2022] [Indexed: 01/19/2023] Open
Abstract
Artificial intelligence can be defined as "understanding human thinking and trying to develop computer processes that will produce a similar structure." Thus, it is an attempt by a programmed computer to think. According to a broader definition, artificial intelligence is a computer equipped with human intelligencespecific capacities such as acquiring information, perceiving, seeing, thinking, and making decisions. Quality demands in dental treatments have constantly been increasing in recent years. In parallel with this, using image-based methods and multimedia-supported explanation systems on the computer is becoming widespread to evaluate the available information. The use of artificial intelligence in dentistry will greatly contribute to the reduction of treatment times and the effort spent by the dentist, reduce the need for a specialist dentist, and give a new perspective to how dentistry is practiced. In this review, we aim to review the studies conducted with artificial intelligence in dentistry and to inform our dentists about the existence of this new technology.
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Affiliation(s)
- Ozkan Miloglu
- Department of Oral, Dental and Maxillofacial Radiology, Atatürk University Faculty of Dentistry, Erzurum, Turkey
| | - Mustafa Taha Guller
- Department of Dentistry Services, Oral and Dental Health Program, Binali Yıldırım University Vocational School of Health Services, , Erzincan, Turkey
| | - Zeynep Turanli Tosun
- Department of Oral, Dental and Maxillofacial Radiology, Atatürk University Faculty of Dentistry, Erzurum, Turkey
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Fatima A, Shafi I, Afzal H, Díez IDLT, Lourdes DRSM, Breñosa J, Espinosa JCM, Ashraf I. Advancements in Dentistry with Artificial Intelligence: Current Clinical Applications and Future Perspectives. Healthcare (Basel) 2022; 10:2188. [PMID: 36360529 PMCID: PMC9690084 DOI: 10.3390/healthcare10112188] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 10/11/2022] [Accepted: 10/26/2022] [Indexed: 08/31/2023] Open
Abstract
Artificial intelligence has been widely used in the field of dentistry in recent years. The present study highlights current advances and limitations in integrating artificial intelligence, machine learning, and deep learning in subfields of dentistry including periodontology, endodontics, orthodontics, restorative dentistry, and oral pathology. This article aims to provide a systematic review of current clinical applications of artificial intelligence within different fields of dentistry. The preferred reporting items for systematic reviews (PRISMA) statement was used as a formal guideline for data collection. Data was obtained from research studies for 2009-2022. The analysis included a total of 55 papers from Google Scholar, IEEE, PubMed, and Scopus databases. Results show that artificial intelligence has the potential to improve dental care, disease diagnosis and prognosis, treatment planning, and risk assessment. Finally, this study highlights the limitations of the analyzed studies and provides future directions to improve dental care.
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Affiliation(s)
- Anum Fatima
- National Centre for Robotics, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Imran Shafi
- College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Hammad Afzal
- Military College of Signals (MCS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan
| | - Isabel De La Torre Díez
- Department of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Del Rio-Solá M. Lourdes
- Department of Vascular Surgery, University Hospital of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Jose Breñosa
- Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Universidad Internacional Iberoamericana, Arecibo, PR 00613, USA
- Universidade Internacional do Cuanza, Estrada Nacional 250, Bairro Kaluapanda Cuito- Bié, Angola
| | - Julio César Martínez Espinosa
- Universidad Europea del Atlántico, Isabel Torres 21, 39011 Santander, Spain
- Universidad Internacional Iberoamericana, Campeche 24560, Mexico
- Fundación Universitaria Internacional de Colombia, Calle 39A #19-18 Bogotá D.C, Colombia
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
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Bayrakdar IS, Orhan K, Akarsu S, Çelik Ö, Atasoy S, Pekince A, Yasa Y, Bilgir E, Sağlam H, Aslan AF, Odabaş A. Deep-learning approach for caries detection and segmentation on dental bitewing radiographs. Oral Radiol 2022; 38:468-479. [PMID: 34807344 DOI: 10.1007/s11282-021-00577-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 11/09/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVES The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer. METHODS A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University. VGG-16 and U-Net implemented with PyTorch models were used for the detection and segmentation of caries lesions, respectively. RESULTS The sensitivity, precision, and F-measure rates for caries detection and caries segmentation were 0.84, 0.81; 0.84, 0.86; and 0.84, 0.84, respectively. Comparing to 5 different experienced observers and AI models on external radiographic dataset, AI models showed superiority to assistant specialists. CONCLUSION CNN-based AI algorithms can have the potential to detect and segmentation of dental caries accurately and effectively in bitewing radiographs. AI algorithms based on the deep-learning method have the potential to assist clinicians in routine clinical practice for quickly and reliably detecting the tooth caries. The use of these algorithms in clinical practice can provide to important benefit to physicians as a clinical decision support system in dentistry.
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Affiliation(s)
- Ibrahim Sevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey.
- Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir, Turkey.
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
- Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey
| | - Serdar Akarsu
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Özer Çelik
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
- Ankara University Medical Design Application and Research Center (MEDITAM), Ankara, Turkey
| | - Samet Atasoy
- Department of Restorative Dentistry, Faculty of Dentistry, Ordu University, Ordu, Turkey
| | - Adem Pekince
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Karabuk University, Karabuk, Turkey
| | - Yasin Yasa
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ordu University, Ordu, Turkey
| | - Elif Bilgir
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey
| | - Hande Sağlam
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, 26240, Eskisehir, Turkey
| | - Ahmet Faruk Aslan
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
| | - Alper Odabaş
- Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey
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Kotaki S, Nishiguchi T, Araragi M, Akiyama H, Fukuda M, Ariji E, Ariji Y. Transfer learning in diagnosis of maxillary sinusitis using panoramic radiography and conventional radiography. Oral Radiol 2022:10.1007/s11282-022-00658-3. [DOI: 10.1007/s11282-022-00658-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 09/19/2022] [Indexed: 10/14/2022]
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Zhou D, Chen G, Xu F. Application of Deep Learning Technology in Strength Training of Football Players and Field Line Detection of Football Robots. Front Neurorobot 2022; 16:867028. [PMID: 35845757 PMCID: PMC9278879 DOI: 10.3389/fnbot.2022.867028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 04/19/2022] [Indexed: 11/25/2022] Open
Abstract
The purpose of the study is to improve the performance of intelligent football training. Based on deep learning (DL), the training of football players and detection of football robots are analyzed. First, the research status of the training of football players and football robots is introduced, and the basic structure of the neuron model and convolutional neural network (CNN) and the mainstream framework of DL are mainly expounded. Second, combined with the spatial stream network, a CNN-based action recognition system is constructed in the context of artificial intelligence (AI). Finally, by the football robot, a field line detection model based on a fully convolutional network (FCN) is proposed, and the effective applicability of the system is evaluated. The results demonstrate that the recognition effect of the dual-stream network is the best, reaching 92.8%. The recognition rate of the timestream network is lower than that of the dual-stream network, and the maximum recognition rate is 88%. The spatial stream network has the lowest recognition rate of 86.5%. The processing power of the four different algorithms on the dataset is stronger than that of the ordinary video set. The recognition rate of the time-segmented dual-stream fusion network is the highest, which is second only to the designed network. The recognition rate of the basic dual-stream network is 88.6%, and the recognition rate of the 3D CNN is the lowest, which is 86.2%. Under the intelligent training system, the recognition accuracy rates of jumping, kicking, grabbing, and starting actions range to 97.6, 94.5, 92.5, and 89.8% respectively, which are slightly lower than other actions. The recognition accuracy rate of passing action is 91.3%, and the maximum upgrade rate of intelligent training is 25.7%. The pixel accuracy of the improved field line detection of the model and the mean intersection over union (MIoU) are both improved by 5%. Intelligent training systems and the field line detection of football robots are more feasible. The research provides a reference for the development of AI in the field of sports training.
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Affiliation(s)
- Daliang Zhou
- School of PE, Nanjing Xiaozhuang University, Nanjing, China
| | - Gang Chen
- School of PE, Nanjing Xiaozhuang University, Nanjing, China
| | - Fei Xu
- School of Physical Education, Hangzhou Normal University, Hangzhou, China
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Patil S, Albogami S, Hosmani J, Mujoo S, Kamil MA, Mansour MA, Abdul HN, Bhandi S, Ahmed SSSJ. Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls. Diagnostics (Basel) 2022; 12:diagnostics12051029. [PMID: 35626185 PMCID: PMC9139975 DOI: 10.3390/diagnostics12051029] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/12/2022] [Accepted: 04/18/2022] [Indexed: 12/19/2022] Open
Abstract
Background: Machine learning (ML) is a key component of artificial intelligence (AI). The terms machine learning, artificial intelligence, and deep learning are erroneously used interchangeably as they appear as monolithic nebulous entities. This technology offers immense possibilities and opportunities to advance diagnostics in the field of medicine and dentistry. This necessitates a deep understanding of AI and its essential components, such as machine learning (ML), artificial neural networks (ANN), and deep learning (DP). Aim: This review aims to enlighten clinicians regarding AI and its applications in the diagnosis of oral diseases, along with the prospects and challenges involved. Review results: AI has been used in the diagnosis of various oral diseases, such as dental caries, maxillary sinus diseases, periodontal diseases, salivary gland diseases, TMJ disorders, and oral cancer through clinical data and diagnostic images. Larger data sets would enable AI to predict the occurrence of precancerous conditions. They can aid in population-wide surveillance and decide on referrals to specialists. AI can efficiently detect microfeatures beyond the human eye and augment its predictive power in critical diagnosis. Conclusion: Although studies have recognized the benefit of AI, the use of artificial intelligence and machine learning has not been integrated into routine dentistry. AI is still in the research phase. The coming decade will see immense changes in diagnosis and healthcare built on the back of this research. Clinical significance: This paper reviews the various applications of AI in dentistry and illuminates the shortcomings faced while dealing with AI research and suggests ways to tackle them. Overcoming these pitfalls will aid in integrating AI seamlessly into dentistry.
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Affiliation(s)
- Shankargouda Patil
- Department of Maxillofacial Surgery and Diagnostic Sciences, Division of Oral Pathology, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia
- Correspondence:
| | - Sarah Albogami
- Department of Biotechnology, College of Science, Taif University, Taif 21944, Saudi Arabia;
| | - Jagadish Hosmani
- Department of Diagnostic Dental Sciences, Oral Pathology Division, Faculty of Dentistry, College of Dentistry, King Khalid University, Abha 61411, Saudi Arabia;
| | - Sheetal Mujoo
- Division of Oral Medicine & Radiology, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia;
| | - Mona Awad Kamil
- Department of Preventive Dental Science, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia;
| | - Manawar Ahmad Mansour
- Department of Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia; (M.A.M.); (H.N.A.)
| | - Hina Naim Abdul
- Department of Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia; (M.A.M.); (H.N.A.)
| | - Shilpa Bhandi
- Department of Restorative Dental Sciences, Division of Operative Dentistry, College of Dentistry, Jazan University, Jazan 45142, Saudi Arabia;
| | - Shiek S. S. J. Ahmed
- Multi-Omics and Drug Discovery Lab, Chettinad Academy of Research and Education, Chennai 600130, India;
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Celik ME. Deep Learning Based Detection Tool for Impacted Mandibular Third Molar Teeth. Diagnostics (Basel) 2022; 12:diagnostics12040942. [PMID: 35453990 PMCID: PMC9025752 DOI: 10.3390/diagnostics12040942] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/29/2022] [Accepted: 04/08/2022] [Indexed: 12/01/2022] Open
Abstract
Third molar impacted teeth are a common issue with all ages, possibly causing tooth decay, root resorption, and pain. This study was aimed at developing a computer-assisted detection system based on deep convolutional neural networks for the detection of third molar impacted teeth using different architectures and to evaluate the potential usefulness and accuracy of the proposed solutions on panoramic radiographs. A total of 440 panoramic radiographs from 300 patients were randomly divided. As a two-stage technique, Faster RCNN with ResNet50, AlexNet, and VGG16 as a backbone and one-stage technique YOLOv3 were used. The Faster-RCNN, as a detector, yielded a mAP@0.5 rate of 0.91 with ResNet50 backbone while VGG16 and AlexNet showed slightly lower performances: 0.87 and 0.86, respectively. The other detector, YOLO v3, provided the highest detection efficacy with a mAP@0.5 of 0.96. Recall and precision were 0.93 and 0.88, respectively, which supported its high performance. Considering the findings from different architectures, it was seen that the proposed one-stage detector YOLOv3 had excellent performance for impacted mandibular third molar tooth detection on panoramic radiographs. Promising results showed that diagnostic tools based on state-ofthe-art deep learning models were reliable and robust for clinical decision-making.
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Affiliation(s)
- Mahmut Emin Celik
- Department of Electrical Electronics Engineering, Faculty of Engineering, Gazi University, Eti mah. Yukselis sk. No: 5 Maltepe, Ankara 06570, Turkey
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Automatic detection and segmentation of morphological changes of the maxillary sinus mucosa on cone-beam computed tomography images using a three-dimensional convolutional neural network. Clin Oral Investig 2022; 26:3987-3998. [DOI: 10.1007/s00784-021-04365-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 12/29/2021] [Indexed: 02/07/2023]
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20
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Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning. Sci Rep 2022; 12:221. [PMID: 34997167 PMCID: PMC8741780 DOI: 10.1038/s41598-021-04354-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 12/20/2021] [Indexed: 02/06/2023] Open
Abstract
Temporomandibular disorders are typically accompanied by a number of clinical manifestations that involve pain and dysfunction of the masticatory muscles and temporomandibular joint. The most important subgroup of articular abnormalities in patients with temporomandibular disorders includes patients with different forms of articular disc displacement and deformation. Here, we propose a fully automated articular disc detection and segmentation system to support the diagnosis of temporomandibular disorder on magnetic resonance imaging. This system uses deep learning-based semantic segmentation approaches. The study included a total of 217 magnetic resonance images from 10 patients with anterior displacement of the articular disc and 10 healthy control subjects with normal articular discs. These images were used to evaluate three deep learning-based semantic segmentation approaches: our proposed convolutional neural network encoder-decoder named 3DiscNet (Detection for Displaced articular DISC using convolutional neural NETwork), U-Net, and SegNet-Basic. Of the three algorithms, 3DiscNet and SegNet-Basic showed comparably good metrics (Dice coefficient, sensitivity, and positive predictive value). This study provides a proof-of-concept for a fully automated deep learning-based segmentation methodology for articular discs on magnetic resonance images, and obtained promising initial results, indicating that the method could potentially be used in clinical practice for the assessment of temporomandibular disorders.
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Reyes LT, Knorst JK, Ortiz FR, Ardenghi TM. Scope and challenges of machine learning-based diagnosis and prognosis in clinical dentistry: A literature review. J Clin Transl Res 2021; 7:523-539. [PMID: 34541366 PMCID: PMC8445629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 04/17/2021] [Accepted: 05/24/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Machine learning (ML) has emerged as a branch of artificial intelligence dealing with the analysis of large amounts of data. The applications of ML algorithms have also expanded to health care, including dentistry. Recent advances in this field point to future improvements in diagnostic techniques and the prognosis of various diseases of the teeth and other maxillofacial structures. AIM The aim of this literature review is to describe the basis for ML being applied to different dental sub-fields in recent years, to identify typical algorithms used in the studies, and to summarize the scope and challenges of using these techniques in dental clinical practice. RELEVANCE FOR PATIENTS The proficiency of emerging technologies that have begun to show encouraging results in the diagnosis and prognosis of oral diseases can improve the precision in the selection of treatment for patients. It is necessary to understand the challenges associated with using these tools to effectively use them in dental services and ensure a higher quality of care for patients.
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Affiliation(s)
- Lilian Toledo Reyes
- Department of Stomatology, School of Dentistry, Federal University of Santa Maria, Santa Maria, Brazil
| | - Jessica Klöckner Knorst
- Department of Stomatology, School of Dentistry, Federal University of Santa Maria, Santa Maria, Brazil
| | - Fernanda Ruffo Ortiz
- Department of Stomatology, School of Dentistry, Federal University of Santa Maria, Santa Maria, Brazil
| | - Thiago Machado Ardenghi
- Department of Stomatology, School of Dentistry, Federal University of Santa Maria, Santa Maria, Brazil,
Corresponding author Thiago Machado Ardenghi Departamento de Estomatologia, Faculdade de Odontologia da Universidade Federal de Santa Maria, Av. Roraima, 1000, Cidade Universitária - 26F, 97015-372, Santa Maria, RS, Brazil. Fax: +55.55-3220-9272 E-mail:
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Mori M, Ariji Y, Fukuda M, Kitano T, Funakoshi T, Nishiyama W, Kohinata K, Iida Y, Ariji E, Katsumata A. Performance of deep learning technology for evaluation of positioning quality in periapical radiography of the maxillary canine. Oral Radiol 2021; 38:147-154. [PMID: 34041639 PMCID: PMC8741711 DOI: 10.1007/s11282-021-00538-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 05/13/2021] [Indexed: 11/29/2022]
Abstract
Objectives The aim of the present study was to create and test an automatic system for assessing the technical quality of positioning in periapical radiography of the maxillary canines using deep learning classification and segmentation techniques. Methods We created and tested two deep learning systems using 500 periapical radiographs (250 each of good- and bad-quality images). We assigned 350, 70, and 80 images as the training, validation, and test datasets, respectively. The learning model of system 1 was created with only the classification process, whereas system 2 consisted of both the segmentation and classification models. In each model, 500 epochs of training were performed using AlexNet and U-net for classification and segmentation, respectively. The segmentation results were evaluated by the intersection over union method, with values of 0.6 or more considered as success. The classification results were compared between the two systems. Results The segmentation performance of system 2 was recall, precision, and F measure of 0.937, 0.961, and 0.949, respectively. System 2 showed better classification performance values than those obtained by system 1. The area under the receiver operating characteristic curve values differed significantly between system 1 (0.649) and system 2 (0.927). Conclusions The deep learning systems we created appeared to have potential benefits in evaluation of the technical positioning quality of periapical radiographs through the use of segmentation and classification functions.
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Affiliation(s)
- Mizuho Mori
- Department of Oral Radiology, Asahi University School of Dentistry, 1851 Hozumi, Mizuho-city, Gifu, 501-0296, Japan.
| | - Yoshiko Ariji
- Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, Nagoya, Japan
| | - Motoki Fukuda
- Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, Nagoya, Japan
| | - Tomoya Kitano
- Department of Oral Radiology, Asahi University School of Dentistry, 1851 Hozumi, Mizuho-city, Gifu, 501-0296, Japan
| | - Takuma Funakoshi
- Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, Nagoya, Japan
| | - Wataru Nishiyama
- Department of Oral Radiology, Asahi University School of Dentistry, 1851 Hozumi, Mizuho-city, Gifu, 501-0296, Japan
| | - Kiyomi Kohinata
- Department of Oral Radiology, Asahi University School of Dentistry, 1851 Hozumi, Mizuho-city, Gifu, 501-0296, Japan
| | - Yukihiro Iida
- Department of Oral Radiology, Asahi University School of Dentistry, 1851 Hozumi, Mizuho-city, Gifu, 501-0296, Japan
| | - Eiichiro Ariji
- Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, Nagoya, Japan
| | - Akitoshi Katsumata
- Department of Oral Radiology, Asahi University School of Dentistry, 1851 Hozumi, Mizuho-city, Gifu, 501-0296, Japan
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A deep transfer learning approach for the detection and diagnosis of maxillary sinusitis on panoramic radiographs. Odontology 2021; 109:941-948. [PMID: 34023953 DOI: 10.1007/s10266-021-00615-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 05/10/2021] [Indexed: 10/21/2022]
Abstract
To investigate the use of transfer learning when applying a deep learning source model from one institution (institution A) to another institution (institution B) for creating effective models (target models) for the detection of maxillary sinuses and diagnosis of maxillary sinusitis on panoramic radiographs. In addition, to determine appropriate numbers of training data for the transfer learning. Source model was created using 350 panoramic radiographs from institution A as training data. Transfer learning was performed by adding 25, 50, 100, 150, or 225 panoramic radiographs as training data from institution B to the source model; this yielded the target models T25, T50, T100, T150 and T225. Each model was then evaluated using test data that comprised 40 images from institution A, 30 images from institution B. The performance indices (recall, precision and F1 score) for detecting the maxillary sinuses by the source model exceeded 0.98 when using test data A from institution A, but they deteriorated when using test data B from institution B. In the evaluation of target models using test data B, model T25 showed improved detection performance (recall of 0.967). The diagnostic performance of model T50 for maxillary sinusitis exceeded 0.9 in sensitivity. Transfer learning, which involves applying a small amount of data to the source model, yielded high performances in detecting the maxillary sinuses and diagnosing the maxillary sinusitis on panoramic radiographs. This study serves as a reference when adapting source models to other institutions.
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Jeon SJ, Yun JP, Yeom HG, Shin WS, Lee JH, Jeong SH, Seo MS. Deep-learning for predicting C-shaped canals in mandibular second molars on panoramic radiographs. Dentomaxillofac Radiol 2021; 50:20200513. [PMID: 33405976 DOI: 10.1259/dmfr.20200513] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVE The aim of this study was to evaluate the use of a convolutional neural network (CNN) system for predicting C-shaped canals in mandibular second molars on panoramic radiographs. METHODS Panoramic and cone beam CT (CBCT) images obtained from June 2018 to May 2020 were screened and 1020 patients were selected. Our dataset of 2040 sound mandibular second molars comprised 887 C-shaped canals and 1153 non-C-shaped canals. To confirm the presence of a C-shaped canal, CBCT images were analyzed by a radiologist and set as the gold standard. A CNN-based deep-learning model for predicting C-shaped canals was built using Xception. The training and test sets were set to 80 to 20%, respectively. Diagnostic performance was evaluated using accuracy, sensitivity, specificity, and precision. Receiver-operating characteristics (ROC) curves were drawn, and the area under the curve (AUC) values were calculated. Further, gradient-weighted class activation maps (Grad-CAM) were generated to localize the anatomy that contributed to the predictions. RESULTS The accuracy, sensitivity, specificity, and precision of the CNN model were 95.1, 92.7, 97.0, and 95.9%, respectively. Grad-CAM analysis showed that the CNN model mainly identified root canal shapes converging into the apex to predict the C-shaped canals, while the root furcation was predominantly used for predicting the non-C-shaped canals. CONCLUSIONS The deep-learning system had significant accuracy in predicting C-shaped canals of mandibular second molars on panoramic radiographs.
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Affiliation(s)
- Su-Jin Jeon
- Department of Conservative Dentistry, Wonkwang University Daejeon Dental Hospital, Daejeon, South Korea
| | - Jong-Pil Yun
- Safety System Research Group, Korea Institute of Industrial Technology (KITECH), Gyeongsan, South Korea
| | - Han-Gyeol Yeom
- Department of Oral and Maxillofacial Radiology, Wonkwang University Daejeon Dental Hospital, Daejeon, South Korea
| | - Woo-Sang Shin
- Safety System Research Group, Korea Institute of Industrial Technology (KITECH), Gyeongsan, South Korea.,School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea
| | - Jong-Hyun Lee
- Safety System Research Group, Korea Institute of Industrial Technology (KITECH), Gyeongsan, South Korea.,School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea
| | - Seung-Hyun Jeong
- Safety System Research Group, Korea Institute of Industrial Technology (KITECH), Gyeongsan, South Korea
| | - Min-Seock Seo
- Department of Conservative Dentistry, Wonkwang University Daejeon Dental Hospital, Daejeon, South Korea
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