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Çelebi A, Imak A, Üzen H, Budak Ü, Türkoğlu M, Hanbay D, Şengür A. Maxillary sinus detection on cone beam computed tomography images using ResNet and Swin Transformer-based UNet. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 138:149-161. [PMID: 37633787 DOI: 10.1016/j.oooo.2023.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 02/27/2023] [Accepted: 06/01/2023] [Indexed: 08/28/2023]
Abstract
OBJECTIVES This study, which uses artificial intelligence-based methods, aimed to determine the limits of pathologic conditions and infections related to the maxillary sinus in cone beam computed tomography (CBCT) images to facilitate the work of dentists. METHODS A new UNet architecture based on a state-of-the-art Swin transformer called Res-Swin-UNet was developed to detect the sinus. The encoder part of the proposed network model consists of a pre-trained ResNet architecture, and the decoder part consists of Swin transformer blocks. Swin transformers achieve powerful global context properties with self-attention mechanisms. Because the output of the Swin transformer generates sectorized features, the patch expanding layer was used in this section instead of the traditional upsampling layer. In the last layer of the decoder, sinus diagnosis was conducted through classical convolution and sigmoid function. In experimental works, we used a data set including 298 CBCT images. RESULTS The Res-Swin-UNet model achieved more success, with a 91.72% F1-score, 99% accuracy, and 84.71% IoU, outperforming the state-of-the-art models. CONCLUSIONS The deep learning-based model proposed in the present study can assist dentists in automatically detecting the boundaries of pathologic conditions and infections within the maxillary sinus based on CBCT images.
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Affiliation(s)
- Adalet Çelebi
- Oral and Maxillofacial Surgery Department, Faculty of Dentistry, Mersin University, Mersin, Turkey
| | - Andaç Imak
- Department of Electrical and Electronic Engineering, Faculty of Engineering, Munzur University, Tunceli, Turkey.
| | - Hüseyin Üzen
- Department of Computer Engineering, Faculty of Engineering, Bingol University, Bingol, Turkey
| | - Ümit Budak
- Department of Electrical and Electronics Engineering, Faculty of Engineering, Bitlis Eren University, Bitlis, Turkey
| | - Muammer Türkoğlu
- Department of Software Engineering, Faculty of Engineering, Samsun University, Samsun, Turkey
| | - Davut Hanbay
- Department of Computer Engineering, Faculty of Engineering, Inonu University, Malatya, Turkey
| | - Abdulkadir Şengür
- Department of Electrical and Electronic Engineering, Faculty of Technology, Firat University, Elazig, Turkey
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Costa ED, Gaêta-Araujo H, Carneiro JA, Zancan BAG, Baranauskas JA, Macedo AA, Tirapelli C. Development of a dental digital data set for research in artificial intelligence: the importance of labeling performed by radiologists. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 138:205-213. [PMID: 38632036 DOI: 10.1016/j.oooo.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 11/12/2023] [Accepted: 12/07/2023] [Indexed: 04/19/2024]
Abstract
OBJECTIVE The aim of this study was to present the development of a database (dataset) of panoramic radiographs. STUDY DESIGN Three radiologists labeled an image set consisting of 936 panoramic radiographs. Labeling includes tooth numbering (including teeth present and missing) and annotation of dental conditions (e.g., caries, dental restoration, residual root, endodontic treatment, implant, fixed prosthesis, incisal wear). The annotation process was performed in a Picture Archive and Communication System software customized for the study purposes using a small bounding box to delimit the entire tooth and items for radiographic diagnosis and a large bounding box to simultaneously delimit the 2 dental arches (maxilla and mandible). A JSON file was generated for each annotation. RESULTS The database encompassed 23,619 annotations; disagreement between radiologists occurred in 0.7% of the notes. CONCLUSIONS This work aimed to inform researchers about the importance of the labeling process, in addition to providing the scientific community with a bank of labeled images to implement artificial intelligence systems in clinical practice.
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Affiliation(s)
- Eliana Dantas Costa
- Department of Dental Materials and Prosthodontics, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil.
| | - Hugo Gaêta-Araujo
- Department of Stomatology, Public Health and Forensic Dentistry, Division of Oral Radiology, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - José Andery Carneiro
- Department of Computing and Mathematics, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | | | - José Augusto Baranauskas
- Department of Computing and Mathematics, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Alessandra Alaniz Macedo
- Department of Computing and Mathematics, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
| | - Camila Tirapelli
- Department of Dental Materials and Prosthodontics, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil
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Altındağ A, Bahrilli S, Çelik Ö, Bayrakdar İŞ, Orhan K. Tooth numbering and classification on bitewing radiographs: an artificial intelligence pilot study. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 137:679-689. [PMID: 38632035 DOI: 10.1016/j.oooo.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 01/13/2024] [Accepted: 02/08/2024] [Indexed: 04/19/2024]
Abstract
OBJECTIVE The aim of this study is to assess the efficacy of employing a deep learning methodology for the automated identification and enumeration of permanent teeth in bitewing radiographs. The experimental procedures and techniques employed in this study are described in the following section. STUDY DESIGN A total of 1248 bitewing radiography images were annotated using the CranioCatch labeling program, developed in Eskişehir, Turkey. The dataset has been partitioned into 3 subsets: training (n = 1000, 80% of the total), validation (n = 124, 10% of the total), and test (n = 124, 10% of the total) sets. The images were subjected to a 3 × 3 clash operation in order to enhance the clarity of the labeled regions. RESULTS The F1, sensitivity and precision results of the artificial intelligence model obtained using the Yolov5 architecture in the test dataset were found to be 0.9913, 0.9954, and 0.9873, respectively. CONCLUSION The utilization of numerical identification for teeth within deep learning-based artificial intelligence algorithms applied to bitewing radiographs has demonstrated notable efficacy. The utilization of clinical decision support system software, which is augmented by artificial intelligence, has the potential to enhance the efficiency and effectiveness of dental practitioners.
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Affiliation(s)
- Ali Altındağ
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Konya, Turkey.
| | - Serkan Bahrilli
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Konya, Turkey
| | - Özer Çelik
- Department of Mathematics-Computer, Eskisehir Osmangazi University Faculty of Science, Eskisehir, Turkey
| | - İbrahim Şevki Bayrakdar
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir, Turkey
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey
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Qiu P, Cao R, Li Z, Huang J, Zhang H, Zhang X. Applications of artificial intelligence for surgical extraction in stomatology: a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol 2024:S2212-4403(24)00285-2. [PMID: 38834501 DOI: 10.1016/j.oooo.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/16/2024] [Accepted: 05/03/2024] [Indexed: 06/06/2024]
Abstract
OBJECTIVES Artificial intelligence (AI) has been extensively used in the field of stomatology over the past several years. This study aimed to evaluate the effectiveness of AI-based models in the procedure, assessment, and treatment planning of surgical extraction. STUDY DESIGN Following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, a comprehensive search was conducted on the Web of Science, PubMed/MEDLINE, Embase, and Scopus databases, covering English publications up to September 2023. Two reviewers performed the study selection and data extraction independently. Only original research studies utilizing AI in surgical extraction of stomatology were included. The Cochrane risk of bias tool for randomized trials (RoB 2) was selected to perform the quality assessment of the selected literature. RESULTS From 2,336 retrieved references, 35 studies were deemed eligible. Among them, 28 researchers reported the pioneering role of AI in segmentation, classification, and detection, aligning with clinical needs. In addition, another 7 studies suggested promising results in tooth extraction decision-making, but further model refinement and validation were required. CONCLUSIONS Integration of AI in stomatology surgical extraction has significantly progressed, enhancing decision-making accuracy. Combining and comparing algorithmic outcomes across studies is essential for determining optimal clinical applications in the future.
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Affiliation(s)
- Piaopiao Qiu
- Department of Oral and Maxillofacial Surgery, Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology, Stomatological Hospital and Dental School, Tongji University, Shanghai, China
| | - Rongkai Cao
- Department of Oral and Maxillofacial Surgery, Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology, Stomatological Hospital and Dental School, Tongji University, Shanghai, China
| | - Zhaoyang Li
- Department of Oral and Maxillofacial Surgery, Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology, Stomatological Hospital and Dental School, Tongji University, Shanghai, China
| | - Jiaqi Huang
- Department of Oral and Maxillofacial Surgery, Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology, Stomatological Hospital and Dental School, Tongji University, Shanghai, China
| | - Huasheng Zhang
- Department of Oral and Maxillofacial Surgery, Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology, Stomatological Hospital and Dental School, Tongji University, Shanghai, China
| | - Xueming Zhang
- Department of Oral and Maxillofacial Surgery, Shanghai Engineering Research Center of Tooth Restoration and Regeneration & Tongji Research Institute of Stomatology, Stomatological Hospital and Dental School, Tongji University, Shanghai, China.
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Pringle AJ, Kumaran V, Missier MS, Nadar ASP. Perceptiveness and Attitude on the use of Artificial Intelligence (AI) in Dentistry among Dentists and Non-Dentists - A Regional Survey. JOURNAL OF PHARMACY AND BIOALLIED SCIENCES 2024; 16:S1481-S1486. [PMID: 38882768 PMCID: PMC11174187 DOI: 10.4103/jpbs.jpbs_1019_23] [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: 10/10/2023] [Revised: 10/14/2023] [Accepted: 10/22/2023] [Indexed: 06/18/2024] Open
Abstract
Artificial intelligence (AI) is an emerging tool in modern medicine and the digital world. AI can help dentists diagnose oral diseases, design treatment plans, monitor patient progress and automate administrative tasks. The aim of this study is to evaluate the perception and attitude on use of artificial intelligence in dentistry for diagnosis and treatment planning among dentists and non-dentists' population of south Tamil Nadu region in India. Materials and Methods A cross sectional online survey conducted using 20 close ended questionnaire google forms which were circulated among the dentists and non -dentists population of south Tamil Nadu region in India. The data collected from 264 participants (dentists -158, non-dentists -106) within a limited time frame were subjected to descriptive statistical analysis. Results 70.9% of dentists are aware of artificial intelligence in dentistry. 40.5% participants were not aware of AI in caries detection but aware of its use in interpretation of radiographs (43.9%) and in planning of orthognathic surgery (42.4%) which are statistically significant P < 0.05.44.7% support clinical experience of a human doctor better than AI diagnosis. Dentists of 54.4% agree to support AI use in dentistry. Conclusion The study concluded AI use in dentistry knowledge is more with dentists and perception of AI in dentistry is optimistic among dentists than non -dentists, majority of participants support AI in dentistry as an adjunct tool to diagnosis and treatment planning.
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Affiliation(s)
- A Jebilla Pringle
- Department of Orthodontics, Rajas Dental College and Hospitals, Kavalkinaru, Tamil Nadu, India
| | - V Kumaran
- Department of Orthodontics, J.K.K. Nataraja Dental College and Hospitals, Nammakal, Tamil Nadu, India
| | - Mary Sheloni Missier
- Department of Orthodontics, Rajas Dental College and Hospitals, Kavalkinaru, Tamil Nadu, India
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Bonny T, Al Nassan W, Obaideen K, Al Mallahi MN, Mohammad Y, El-damanhoury HM. Contemporary Role and Applications of Artificial Intelligence in Dentistry. F1000Res 2023; 12:1179. [PMID: 37942018 PMCID: PMC10630586 DOI: 10.12688/f1000research.140204.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/24/2023] [Indexed: 11/10/2023] Open
Abstract
Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.
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Affiliation(s)
- Talal Bonny
- Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Wafaa Al Nassan
- Department of Computer Engineering, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Khaled Obaideen
- Sustainable Energy and Power Systems Research Centre, RISE, University of Sharjah, Sharjah, 27272, United Arab Emirates
| | - Maryam Nooman Al Mallahi
- Department of Mechanical and Aerospace Engineering, United Arab Emirates University, Al Ain City, Abu Dhabi, 27272, United Arab Emirates
| | - Yara Mohammad
- College of Engineering and Information Technology, Ajman University, Ajman University, Ajman, Ajman, United Arab Emirates
| | - Hatem M. El-damanhoury
- Department of Preventive and Restorative Dentistry, College of Dental Medicine, University of Sharjah, Sharjah, 27272, United Arab Emirates
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Santana LADM, Floresta LG, Alves ÊVM, dos Santos MAL, Barbosa BF, de Vasconcellos SJDA, Valadares CV. Can GPT-4 be a viable alternative for discussing complex cases in digital oral radiology? A critical analysis. EXCLI JOURNAL 2023; 22:749-751. [PMID: 37662708 PMCID: PMC10471844 DOI: 10.17179/excli2023-6373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 07/20/2023] [Indexed: 09/05/2023]
Affiliation(s)
- Lucas Alves da Mota Santana
- Department of Dentistry, Federal University of Sergipe (UFS), Aracaju, SE, Brazil
- Department of Dentistry, Tiradentes University (UNIT), Aracaju, SE, Brazil
| | - Lara Góis Floresta
- Department of Dentistry, Tiradentes University (UNIT), Aracaju, SE, Brazil
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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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Al-Ekrish A, Hussain SA, ElGibreen H, Almurshed R, Alhusain L, Hörmann R, Widmann G. Prediction of the as Low as Diagnostically Acceptable CT Dose for Identification of the Inferior Alveolar Canal Using 3D Convolutional Neural Networks with Multi-Balancing Strategies. Diagnostics (Basel) 2023; 13:diagnostics13071220. [PMID: 37046438 PMCID: PMC10093627 DOI: 10.3390/diagnostics13071220] [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: 02/11/2023] [Revised: 03/14/2023] [Accepted: 03/22/2023] [Indexed: 04/14/2023] Open
Abstract
Ionizing radiation is necessary for diagnostic imaging and deciding the right radiation dose is extremely critical to obtain a decent quality image. However, increasing the dosage to improve the image quality has risks due to the potential harm from ionizing radiation. Thus, finding the optimal as low as diagnostically acceptable (ALADA) dosage is an open research problem that has yet to be tackled using artificial intelligence (AI) methods. This paper proposes a new multi-balancing 3D convolutional neural network methodology to build 3D multidetector computed tomography (MDCT) datasets and develop a 3D classifier model that can work properly with 3D CT scan images and balance itself over the heavy unbalanced multi-classes. The proposed models were exhaustively investigated through eighteen empirical experiments and three re-runs for clinical expert examination. As a result, it was possible to confirm that the proposed models improved the performance by an accuracy of 5% to 10% when compared to the baseline method. Furthermore, the resulting models were found to be consistent, and thus possibly applicable to different MDCT examinations and reconstruction techniques. The outcome of this paper can help radiologists to predict the suitability of CT dosages across different CT hardware devices and reconstruction algorithms. Moreover, the developed model is suitable for clinical application where the right dose needs to be predicted from numerous MDCT examinations using a certain MDCT device and reconstruction technique.
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Affiliation(s)
- Asma'a Al-Ekrish
- Department of Oral Medicine and Diagnostic Sciences, College of Dentistry, King Saud University, Riyadh 11545, Saudi Arabia
| | - Syed Azhar Hussain
- Department of Computer Science, Munster Technological University, Rossa Ave, Bishopstown, T12 P928 Cork, Ireland
| | - Hebah ElGibreen
- Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
- Artificial Intelligence Center of Advanced Studies (Thakaa), King Saud University, Riyadh 145111, Saudi Arabia
| | - Rana Almurshed
- Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
| | - Luluah Alhusain
- Information Technology Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia
| | - Romed Hörmann
- Division of Clinical and Functional Anatomy, Medical University of Innsbruck, Müllerstrasse 59, 6020 Innsbruck, Austria
| | - Gerlig Widmann
- Department of Radiology, Medical University of Innsbruck, Anichstr. 35, 6020 Innsbruck, Austria
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Developments and Performance of Artificial Intelligence Models Designed for Application in Endodontics: A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13030414. [PMID: 36766519 PMCID: PMC9913920 DOI: 10.3390/diagnostics13030414] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/18/2023] [Accepted: 01/20/2023] [Indexed: 01/26/2023] Open
Abstract
Technological advancements in health sciences have led to enormous developments in artificial intelligence (AI) models designed for application in health sectors. This article aimed at reporting on the application and performances of AI models that have been designed for application in endodontics. Renowned online databases, primarily PubMed, Scopus, Web of Science, Embase, and Cochrane and secondarily Google Scholar and the Saudi Digital Library, were accessed for articles relevant to the research question that were published from 1 January 2000 to 30 November 2022. In the last 5 years, there has been a significant increase in the number of articles reporting on AI models applied for endodontics. AI models have been developed for determining working length, vertical root fractures, root canal failures, root morphology, and thrust force and torque in canal preparation; detecting pulpal diseases; detecting and diagnosing periapical lesions; predicting postoperative pain, curative effect after treatment, and case difficulty; and segmenting pulp cavities. Most of the included studies (n = 21) were developed using convolutional neural networks. Among the included studies. datasets that were used were mostly cone-beam computed tomography images, followed by periapical radiographs and panoramic radiographs. Thirty-seven original research articles that fulfilled the eligibility criteria were critically assessed in accordance with QUADAS-2 guidelines, which revealed a low risk of bias in the patient selection domain in most of the studies (risk of bias: 90%; applicability: 70%). The certainty of the evidence was assessed using the GRADE approach. These models can be used as supplementary tools in clinical practice in order to expedite the clinical decision-making process and enhance the treatment modality and clinical operation.
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Schönewolf J, Meyer O, Engels P, Schlickenrieder A, Hickel R, Gruhn V, Hesenius M, Kühnisch J. Artificial intelligence-based diagnostics of molar-incisor-hypomineralization (MIH) on intraoral photographs. Clin Oral Investig 2022; 26:5923-5930. [PMID: 35608684 PMCID: PMC9474479 DOI: 10.1007/s00784-022-04552-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/13/2022] [Indexed: 11/09/2022]
Abstract
OBJECTIVE The aim of this study was to develop and validate a deep learning-based convolutional neural network (CNN) for the automated detection and categorization of teeth affected by molar-incisor-hypomineralization (MIH) on intraoral photographs. MATERIALS AND METHODS The data set consisted of 3241 intraoral images (767 teeth with no MIH/no intervention, 76 with no MIH/atypical restoration, 742 with no MIH/sealant, 815 with demarcated opacity/no intervention, 158 with demarcated opacity/atypical restoration, 181 with demarcated opacity/sealant, 290 with enamel breakdown/no intervention, 169 with enamel breakdown/atypical restoration, and 43 with enamel breakdown/sealant). These images were divided into a training (N = 2596) and a test sample (N = 649). All images were evaluated by an expert group, and each diagnosis served as a reference standard for cyclic training and evaluation of the CNN (ResNeXt-101-32 × 8d). Statistical analysis included the calculation of contingency tables, areas under the receiver operating characteristic curve (AUCs) and saliency maps. RESULTS The developed CNN was able to categorize teeth with MIH correctly with an overall diagnostic accuracy of 95.2%. The overall SE and SP amounted to 78.6% and 97.3%, respectively, which indicate that the CNN performed better in healthy teeth compared to those with MIH. The AUC values ranging from 0.873 (enamel breakdown/sealant) to 0.994 (atypical restoration/no MIH). CONCLUSION It was possible to categorize the majority of clinical photographs automatically by using a trained deep learning-based CNN with an acceptably high diagnostic accuracy. CLINICAL RELEVANCE Artificial intelligence-based dental diagnostics may support dental diagnostics in the future regardless of the need to improve accuracy.
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Affiliation(s)
- Jule Schönewolf
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University Munich, Goethestraße 70, 80336, Munich, Germany
| | - Ole Meyer
- Institute for Software Engineering, University of Duisburg-Essen, Essen, Germany
| | - Paula Engels
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University Munich, Goethestraße 70, 80336, Munich, Germany
| | - Anne Schlickenrieder
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University Munich, Goethestraße 70, 80336, Munich, Germany
| | - Reinhard Hickel
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University Munich, Goethestraße 70, 80336, Munich, Germany
| | - Volker Gruhn
- Institute for Software Engineering, University of Duisburg-Essen, Essen, Germany
| | - Marc Hesenius
- Institute for Software Engineering, University of Duisburg-Essen, Essen, Germany
| | - Jan Kühnisch
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians University Munich, Goethestraße 70, 80336, Munich, Germany.
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A dose-neutral image quality comparison of different CBCT and CT systems using paranasal sinus imaging protocols and phantoms. Eur Arch Otorhinolaryngol 2022; 279:4407-4414. [PMID: 35084532 PMCID: PMC9363284 DOI: 10.1007/s00405-022-07271-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/10/2022] [Indexed: 11/08/2022]
Abstract
Purpose To compare the image quality produced by equivalent low-dose and default sinus imaging protocols of a conventional dental cone-beam computed tomography (CBCT) scanner, an extremity CBCT scanner and a clinical multidetector computed tomography (MDCT) scanner.
Methods Three different phantoms were scanned using dose–neutral ultra-low-dose and low-dose sinus imaging protocols, as well as default sinus protocols of each device. Quantified parameters of image quality included modulation transfer function (MTF) to characterize the spatial response of the imaging system, contrast-to-noise ratio, low contrast visibility, image uniformity and Hounsfield unit accuracy. MTF was calculated using the line spread and edge spread functions (LSF and ESF).
Results The dental CBCT had superior performance over the extremity CBCT in each studied parameter at similar dose levels. The MDCT had better contrast-to-noise ratio, low contrast visibility and image uniformity than the CBCT scanners. However, the CBCT scanners had better resolution compared to the MDCT. Accuracy of HU values for different materials was on the same level between the dental CBCT and MDCT, but substantially poorer performance was observed with the extremity CBCT.
Conclusions The studied dental CBCT scanner showed superior performance over the studied extremity CBCT scanner when using dose–neutral imaging protocols. In case a dental CBCT is not available, the given extremity CBCT is still a viable option as it provides the benefit of high resolution over a conventional MDCT.
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Pauwels R, Del Rey YC. Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey. Dentomaxillofac Radiol 2021; 50:20200461. [PMID: 33353376 DOI: 10.1259/dmfr.20200461] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVES The aim of this study was to assess the attitude of dentists and dental students in Brazil regarding the impact of artificial intelligence (AI) in oral radiology, and to evaluate the effect of an introductory AI lecture on their attitude. METHODS A questionnaire was prepared, comprising statements regarding the future role of AI in oral radiology and dentistry. A lecture of approx. 1 h was prepared, comprising the basic principles of AI and a non-exhaustive overview of AI research in medicine and dentistry. Participants filled in the questionnaire prior to the lecture. After the lecture, the questionnaire was repeated. RESULTS Throughout 7 sessions at 6 locations, 293 questionnaires were collected. The majority of participants were undergraduate dental students (57%). Prior to the lecture, there was a strong agreement regarding the various future roles and expected impact of AI in oral radiology. Approximately, one-third of participants was concerned about AI. After the lecture, agreement regarding the different roles of AI in oral radiology increased, overall excitement regarding AI increased, and concerns regarding the potential replacement of oral radiologists decreased. CONCLUSIONS A generally positive attitude towards AI was found; an introductory lecture was beneficial towards this attitude and alleviated concerns regarding the effect of AI on the oral radiology profession. Given the unprecedented, ongoing revolution of AI-augmented radiology, it is pivotal to incorporate AI topics in dental training curricula.
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Affiliation(s)
- Ruben Pauwels
- Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark
| | - Yumi Chokyu Del Rey
- Department of Dental Materials and Prosthodontics, Ribeirão Preto Dental School, University of São Paulo, São Paulo, Brazil
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Wenzel A. Radiographic modalities for diagnosis of caries in a historical perspective: from film to machine-intelligence supported systems. Dentomaxillofac Radiol 2021; 50:20210010. [PMID: 33661697 PMCID: PMC8231685 DOI: 10.1259/dmfr.20210010] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 02/03/2021] [Accepted: 02/16/2021] [Indexed: 01/17/2023] Open
Abstract
Radiographic imaging for the diagnosis of caries lesions has been a supplement to clinical examination for approximately a century. Various methods, and particularly X-ray receptors, have been developed over the years, and computer systems have focused on aiding the dentist in the detection of lesions and in estimating lesion depth. The present historical review has sampled accuracy ex vivo studies and clinical studies on radiographic caries diagnosis that have compared two or more receptors for capturing the image. The epochs of film radiography, xeroradiography, digital intraoral radiography, panoramic radiography and other extraoral methods, TACT analysis, cone-beam CT and artificial intelligence systems aiding in decision-making are reviewed. The author of this review (43 years in academia) has been involved in caries research and contributed to the literature in all the mentioned epochs.
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Affiliation(s)
- Ann Wenzel
- Oral Radiology, Department of Dentistry and Oral Health, Aarhus University, Aarhus, Denmark
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15
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Pauwels R, Brasil DM, Yamasaki MC, Jacobs R, Bosmans H, Freitas DQ, Haiter-Neto F. Artificial intelligence for detection of periapical lesions on intraoral radiographs: Comparison between convolutional neural networks and human observers. Oral Surg Oral Med Oral Pathol Oral Radiol 2021; 131:610-616. [PMID: 33653645 DOI: 10.1016/j.oooo.2021.01.018] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 12/22/2020] [Accepted: 01/18/2021] [Indexed: 12/31/2022]
Abstract
OBJECTIVE The aim of this study was to compare the diagnostic performance of convolutional neural networks (CNNs) with the performance of human observers for the detection of simulated periapical lesions on periapical radiographs. STUDY DESIGN Ten sockets were prepared in bovine ribs. Periapical defects of 3 sizes were sequentially created. Periapical radiographs were acquired of each socket with no lesion and with each lesion size with a photostimulable storage phosphor system. Radiographs were evaluated with no filter and with 6 image filter settings. A CNN architecture was set up using Keras-TensorFlow. Separate CNNs were evaluated for randomly sampled training/validation data and for data split up by socket (5-fold cross-validation) and filter (7-fold cross-validation). CNN performance on validation data was compared with that of 3 oral radiologists for sensitivity, specificity, and area under the receiver operating characteristic curve (ROC-AUC). RESULTS Using random sampling, the CNN showed perfect accuracy for the validation data. When data were split up by socket, the mean sensitivity, specificity, and ROC-AUC values were 0.79, 0.88, and 0.86, respectively; when split up by filter, they were 0.87, 0.98, and 0.93, respectively. For radiologists, the values were 0.58, 0.83, and 0.75, respectively. CONCLUSIONS CNNs show promise in periapical lesion detection. The pretrained CNN model yielded in this study can be used for further training on larger samples and/or clinical radiographs.
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Affiliation(s)
- Ruben Pauwels
- Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark; Department of Imaging & Pathology, Biomedical Sciences Group, Catholic University of Leuven, Leuven, Belgium.
| | - Danieli Moura Brasil
- Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil
| | - Mayra Cristina Yamasaki
- Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil
| | - Reinhilde Jacobs
- Department of Imaging & Pathology, Biomedical Sciences Group, Catholic University of Leuven, Leuven, Belgium; Department of Dental Medicine, Karolinska Institutet, Huddinge, Sweden
| | - Hilde Bosmans
- Department of Imaging & Pathology, Biomedical Sciences Group, Catholic University of Leuven, Leuven, Belgium
| | - Deborah Queiroz Freitas
- Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil
| | - Francisco Haiter-Neto
- Department of Oral Diagnosis, Division of Oral Radiology, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil
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Prados-Privado M, García Villalón J, Martínez-Martínez CH, Ivorra C, Prados-Frutos JC. Dental Caries Diagnosis and Detection Using Neural Networks: A Systematic Review. J Clin Med 2020; 9:E3579. [PMID: 33172056 PMCID: PMC7694692 DOI: 10.3390/jcm9113579] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 10/30/2020] [Accepted: 11/03/2020] [Indexed: 11/16/2022] Open
Abstract
Dental caries is the most prevalent dental disease worldwide, and neural networks and artificial intelligence are increasingly being used in the field of dentistry. This systematic review aims to identify the state of the art of neural networks in caries detection and diagnosis. A search was conducted in PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. Data extraction was performed independently by two reviewers. The quality of the selected studies was assessed using the Cochrane Handbook tool. Thirteen studies were included. Most of the included studies employed periapical, near-infrared light transillumination, and bitewing radiography. The image databases ranged from 87 to 3000 images, with a mean of 669 images. Seven of the included studies labeled the dental caries in each image by experienced dentists. Not all of the studies detailed how caries was defined, and not all detailed the type of carious lesion detected. Each study included in this review used a different neural network and different outcome metrics. All this variability complicates the conclusions that can be made about the reliability or not of a neural network to detect and diagnose caries. A comparison between neural network and dentist results is also necessary.
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Affiliation(s)
- María Prados-Privado
- Asisa Dental, Research Department, C/José Abascal, 32, 28003 Madrid, Spain; (J.G.V.); (C.I.)
- Department of Signal Theory and Communications, Higher Polytechnic School, Universidad de Alcala de Henares, Ctra, Madrid-Barcelona, Km. 33,600, 28805 Alcala de Henares, Spain
- IDIBO GROUP (Group of High-Performance Research, Development and Innovation in Dental Biomaterials of Rey Juan Carlos University), Avenida de Atenas s/n, 28922 Alcorcon, Spain;
| | - Javier García Villalón
- Asisa Dental, Research Department, C/José Abascal, 32, 28003 Madrid, Spain; (J.G.V.); (C.I.)
| | | | - Carlos Ivorra
- Asisa Dental, Research Department, C/José Abascal, 32, 28003 Madrid, Spain; (J.G.V.); (C.I.)
| | - Juan Carlos Prados-Frutos
- IDIBO GROUP (Group of High-Performance Research, Development and Innovation in Dental Biomaterials of Rey Juan Carlos University), Avenida de Atenas s/n, 28922 Alcorcon, Spain;
- Department of Medical Specialties and Public Health, Faculty of Health Sciences, Universidad Rey Juan Carlos, Avenida de Atenas, 28922 Alcorcon, Spain
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