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Tan R, Zhu X, Chen S, Zhang J, Liu Z, Li Z, Fan H, Wang X, Yang L. Caries lesions diagnosis with deep convolutional neural network in intraoral QLF images by handheld device. BMC Oral Health 2024; 24:754. [PMID: 38951770 PMCID: PMC11218122 DOI: 10.1186/s12903-024-04517-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 06/21/2024] [Indexed: 07/03/2024] Open
Abstract
OBJECTIVES This study investigated the effectiveness of a deep convolutional neural network (CNN) in diagnosing and staging caries lesions in quantitative light-induced fluorescence (QLF) images taken by a self-manufactured handheld device. METHODS A small toothbrush-like device consisting of a 400 nm UV light-emitting lamp with a 470 nm filter was manufactured for intraoral imaging. A total of 133 cases with 9,478 QLF images of teeth were included for caries lesion evaluation using a CNN model. The database was divided into development, validation, and testing cohorts at a 7:2:1 ratio. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) were calculated for model performance. RESULTS The overall caries prevalence was 19.59%. The CNN model achieved an AUC of 0.88, an accuracy of 0.88, a specificity of 0.94, and a sensitivity of 0.64 in the validation cohort. They achieved an overall accuracy of 0.92, a sensitivity of 0.95 and a specificity of 0.55 in the testing cohort. The model can distinguish different stages of caries well, with the best performance in detecting deep caries followed by intermediate and superficial lesions. CONCLUSIONS Caries lesions have typical characteristics in QLF images and can be detected by CNNs. A QLF-based device with CNNs can assist in caries screening in the clinic or at home. TRIAL REGISTRATION The clinical trial was registered in the Chinese Clinical Trial Registry (No. ChiCTR2300073487, Date: 12/07/2023).
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Affiliation(s)
- Rukeng Tan
- Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, 56th Lingyuanxi Road, Guangzhou, 510055, Guangdong, China
- Guangdong Province Key Laboratory of Stomatology, No. 74, 2nd Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Xinyu Zhu
- Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, 56th Lingyuanxi Road, Guangzhou, 510055, Guangdong, China
- Guangdong Province Key Laboratory of Stomatology, No. 74, 2nd Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Sishi Chen
- Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, 56th Lingyuanxi Road, Guangzhou, 510055, Guangdong, China
- Guangdong Province Key Laboratory of Stomatology, No. 74, 2nd Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Jie Zhang
- Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, 56th Lingyuanxi Road, Guangzhou, 510055, Guangdong, China
- Guangdong Province Key Laboratory of Stomatology, No. 74, 2nd Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Zhixin Liu
- Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, 56th Lingyuanxi Road, Guangzhou, 510055, Guangdong, China
- Guangdong Province Key Laboratory of Stomatology, No. 74, 2nd Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Zhengshi Li
- Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, 56th Lingyuanxi Road, Guangzhou, 510055, Guangdong, China
- Guangdong Province Key Laboratory of Stomatology, No. 74, 2nd Zhongshan Road, Guangzhou, 510080, Guangdong, China
| | - Hang Fan
- Guangzhou Stars Pulse Co., Ltd, 239th Tianhe North Road, Tianhe District, Guangzhou, 510610, Guangdong, China
| | - Xi Wang
- Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, 56th Lingyuanxi Road, Guangzhou, 510055, Guangdong, China.
- Guangdong Province Key Laboratory of Stomatology, No. 74, 2nd Zhongshan Road, Guangzhou, 510080, Guangdong, China.
| | - Le Yang
- Guanghua School of Stomatology, Hospital of Stomatology, Sun Yat-sen University, 56th Lingyuanxi Road, Guangzhou, 510055, Guangdong, China.
- Guangdong Province Key Laboratory of Stomatology, No. 74, 2nd Zhongshan Road, Guangzhou, 510080, Guangdong, China.
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Schropp L, Sørensen APS, Devlin H, Matzen LH. Use of artificial intelligence software in dental education: A study on assisted proximal caries assessment in bitewing radiographs. EUROPEAN JOURNAL OF DENTAL EDUCATION : OFFICIAL JOURNAL OF THE ASSOCIATION FOR DENTAL EDUCATION IN EUROPE 2024; 28:490-496. [PMID: 37961027 DOI: 10.1111/eje.12973] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/14/2023] [Accepted: 10/13/2023] [Indexed: 11/15/2023]
Abstract
INTRODUCTION Teaching of dental caries diagnostics is an essential part of dental education. Diagnosing proximal caries is a challenging task, and automated systems applying artificial intelligence (AI) have been introduced to assist in this respect. Thus, the implementation of AI for teaching purposes may be considered. The aim of this study was to assess the impact of an AI software on students' ability to detect enamel-only proximal caries in bitewing radiographs (BWs) and to assess whether proximal tooth overlap interferes with caries detection. MATERIALS AND METHODS The study included 74 dental students randomly allocated to either a test or control group. At two sessions, both groups assessed proximal enamel caries in BWs. At the first session, the test group registered caries in 25 BWs using AI software (AssistDent®) and the control group without using AI. One month later, both groups detected caries in another 25 BWs in a clinical setup without using the software. The student's registrations were compared with a reference standard. Positive agreement (caries) and negative agreement (no caries) were calculated, and t-tests were applied to assess whether the test and control groups performed differently. Moreover, t-tests were applied to test whether proximal overlap interfered with caries registration. RESULTS At the first and second sessions, 56 and 52 tooth surfaces, respectively, were detected with enamel-only caries according to the reference standard. At session 1, no significant difference between the control (48%) and the test (42%) group was found for positive agreement (p = .08), whereas the negative agreement was higher for the test group (86% vs. 80%; p = .02). At session 2, there was no significant difference between the groups. The test group improved for positive agreement from session 1 to session 2 (p < .001), while the control group improved for negative agreement (p < .001). Thirty-eight per cent of the tooth surfaces overlapped, and the mean positive agreement and negative agreement were significantly lower for overlapping surfaces than non-overlapping surfaces (p < .001) in both groups. CONCLUSION Training with the AI software did not impact on dental students' ability to detect proximal enamel caries in bitewing radiographs although the positive agreement improved over time. It was revealed that proximal tooth overlap interfered with caries detection.
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Affiliation(s)
- Lars Schropp
- Oral Radiology, Department of Dentistry and Oral Health, Aarhus University, Aarhus C, Denmark
| | - Anders Peter Sejersdal Sørensen
- Oral Radiology, Department of Dentistry and Oral Health, Aarhus University, Aarhus C, Denmark
- Private practice, Tandlægerne Sydcentret, Kolding, Denmark
| | - Hugh Devlin
- Division of Dentistry, School of Medical Sciences, The University of Manchester, Manchester, UK
| | - Louise Hauge Matzen
- Oral Radiology, Department of Dentistry and Oral Health, Aarhus University, Aarhus C, Denmark
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Revilla-León M, Gómez-Polo M, Vyas S, Barmak AB, Özcan M, Att W, Krishnamurthy VR. Artificial intelligence applications in restorative dentistry: A systematic review. J Prosthet Dent 2022; 128:867-875. [PMID: 33840515 DOI: 10.1016/j.prosdent.2021.02.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Revised: 02/03/2021] [Accepted: 02/04/2021] [Indexed: 11/17/2022]
Abstract
STATEMENT OF PROBLEM Artificial intelligence (AI) applications are increasing in restorative procedures. However, the current development and performance of AI in restorative dentistry applications has not yet been systematically documented and analyzed. PURPOSE The purpose of this systematic review was to identify and evaluate the ability of AI models in restorative dentistry to diagnose dental caries and vertical tooth fracture, detect tooth preparation margins, and predict restoration failure. MATERIAL AND METHODS An electronic systematic review was performed in 5 databases: MEDLINE/PubMed, EMBASE, World of Science, Cochrane, and Scopus. A manual search was also conducted. Studies with AI models were selected based on 4 criteria: diagnosis of dental caries, diagnosis of vertical tooth fracture, detection of the tooth preparation finishing line, and prediction of restoration failure. Two investigators independently evaluated the quality assessment of the studies by applying the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies (nonrandomized experimental studies). A third investigator was consulted to resolve lack of consensus. RESULTS A total of 34 articles were included in the review: 29 studies included AI techniques for the diagnosis of dental caries or the elaboration of caries and postsensitivity prediction models, 2 for the diagnosis of vertical tooth fracture, 1 for the tooth preparation finishing line location, and 2 for the prediction of the restoration failure. Among the studies reviewed, the AI models tested obtained a caries diagnosis accuracy ranging from 76% to 88.3%, sensitivity ranging from 73% to 90%, and specificity ranging from 61.5% to 93%. The caries prediction accuracy among the studies ranged from 83.6% to 97.1%. The studies reported an accuracy for the vertical tooth fracture diagnosis ranging from 88.3% to 95.7%. The article using AI models to locate the finishing line reported an accuracy ranging from 90.6% to 97.4%. CONCLUSIONS AI models have the potential to provide a powerful tool for assisting in the diagnosis of caries and vertical tooth fracture, detecting the tooth preparation margin, and predicting restoration failure. However, the dental applications of AI models are still in development. Further studies are required to assess the clinical performance of AI models in restorative dentistry.
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Affiliation(s)
- Marta Revilla-León
- Assistant Professor and Assistant Program Director AEGD Residency, Department of Comprehensive Dentistry, College of Dentistry, Texas A&M University, Dallas, Texas; Affiliate Faculty Graduate Prosthodontics, Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Wash; Researcher at Revilla Research Center, Madrid, Spain
| | - Miguel Gómez-Polo
- Associate Professor, Department of Conservative Dentistry and Prosthodontics, School of Dentistry, Complutense University of Madrid, Madrid, Spain.
| | - Shantanu Vyas
- Graduate Research Assistant, J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, Dallas, Texas
| | - Abdul Basir Barmak
- Assistant Professor Clinical Research and Biostatistics, Eastman Institute of Oral Health, University of Rochester Medical Center, Rochester, NY
| | - Mutlu Özcan
- Professor and Head, Division of Dental Biomaterials, Clinic for Reconstructive Dentistry, Center for Dental and Oral Medicine, University of Zürich, Zürich, Switzerland
| | - Wael Att
- Professor and Chair, Department of Prosthodontics, Tufts University School of Dental Medicine, Boston, Mass
| | - Vinayak R Krishnamurthy
- Assistant Professor, J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, College Station, Texas
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Hamdan MH, Tuzova L, Mol A, Tawil PZ, Tuzoff D, Tyndall DA. The effect of a deep-learning tool on dentists' performances in detecting apical radiolucencies on periapical radiographs. Dentomaxillofac Radiol 2022; 51:20220122. [PMID: 35980437 PMCID: PMC9522978 DOI: 10.1259/dmfr.20220122] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 08/03/2022] [Accepted: 08/06/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES To determine the efficacy of a deep-learning (DL) tool in assisting dentists in detecting apical radiolucencies on periapical radiographs. METHODS Sixty-eight intraoral periapical radiographs with CBCT-proven presence or absence of apical radiolucencies were selected to serve as the testing subset. Eight readers examined the subset, denoted the positions of apical radiolucencies, and used a 5-point confidence scale to score each radiolucency. The same subset was assessed by readers under two conditions: with and without Denti.AI DL tool predictions. For the two sessions, the performance of the readers was compared. The comparison was performed with the alternate free response receiver operating characteristic (AFROC) methodology. RESULTS Localization of lesion accuracy (AFROC-AUC), specificity and sensitivity (by lesion) detection demonstrated improvements in the DL aided session in comparison with the unaided reading session. Subgroup performance analysis revealed an increase in sensitivity for small radiolucencies and in radiolucencies located apical to endodontically treated teeth.. CONCLUSION The study revealed that the DL technology (Denti.AI) enhances dental professionals' abilities to detect apical radiolucencies on intraoral radiographs. ADVANCES IN KNOWLEDGE DL tools have the potential to improve diagnostic efficacy of dentists in identifying apical radiolucencies on periapical radiographs.
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Affiliation(s)
- Manal H. Hamdan
- Department of General Dental Sciences, Marquette University School of Dentistry, Milwaukee, WI, United States
| | - Lyudmila Tuzova
- Department of General Dental Sciences, Marquette University School of Dentistry, Milwaukee, WI, United States
| | - André Mol
- Division of Diagnostic Sciences, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, United States
| | - Peter Z. Tawil
- Division of Comprehensive Oral Health, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, United States
| | | | - Donald A. Tyndall
- Division of Diagnostic Sciences, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, United States
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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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Kishimoto T, Goto T, Matsuda T, Iwawaki Y, Ichikawa T. Application of artificial intelligence in the dental field: A literature review. J Prosthodont Res 2021; 66:19-28. [PMID: 33441504 DOI: 10.2186/jpr.jpr_d_20_00139] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
PURPOSE The purpose of this study was to comprehensively review the literature regarding the application of artificial intelligence (AI) in the dental field,focusing on the evaluation criteria and architecture types. STUDY SELECTION Electronic databases (PubMed, Cochrane Library, Scopus) were searched. Full-text articles describing the clinical application of AI for the detection, diagnosis, and treatment of lesions and the AI method/architecture were included. RESULTS The primary search presented 422 studies from 1996 to 2019, and 58 studies were finally selected. Regarding the year of publication, the oldest study, which was reported in 1996, focused on "oral and maxillofacial surgery." Machine-learning architectures were employed in the selected studies, while approximately half of them (29/58) employed neural networks. Regarding the evaluation criteria, eight studies compared the results obtained by AI with the diagnoses formulated by dentists, while several studies compared two or more architectures in terms of performance. The following parameters were employed for evaluating the AI performance: accuracy, sensitivity, specificity, mean absolute error, root mean squared error, and area under the receiver operating characteristic curve. CONCLUSIONS Application of AI in the dental field has progressed; however, the criteria for evaluating the efficacy of AI have not been clarified. It is necessary to obtain better quality data for machine learning to achieve the effective diagnosis of lesions and suitable treatment planning.
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Affiliation(s)
- Takahiro Kishimoto
- Department of Prosthodontics & Oral Rehabilitation, Tokushima University Graduate School of Biomedical Sciences
| | - Takaharu Goto
- Department of Prosthodontics & Oral Rehabilitation, Tokushima University Graduate School of Biomedical Sciences
| | - Takashi Matsuda
- Department of Prosthodontics & Oral Rehabilitation, Tokushima University Graduate School of Biomedical Sciences
| | - Yuki Iwawaki
- Department of Prosthodontics & Oral Rehabilitation, Tokushima University Graduate School of Biomedical Sciences
| | - Tetsuo Ichikawa
- Department of Prosthodontics & Oral Rehabilitation, Tokushima University Graduate School of Biomedical Sciences
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Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 2018; 77:106-111. [PMID: 30056118 DOI: 10.1016/j.jdent.2018.07.015] [Citation(s) in RCA: 317] [Impact Index Per Article: 52.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 07/21/2018] [Accepted: 07/25/2018] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVES Deep convolutional neural networks (CNNs) are a rapidly emerging new area of medical research, and have yielded impressive results in diagnosis and prediction in the fields of radiology and pathology. The aim of the current study was to evaluate the efficacy of deep CNN algorithms for detection and diagnosis of dental caries on periapical radiographs. MATERIALS AND METHODS A total of 3000 periapical radiographic images were divided into a training and validation dataset (n = 2400 [80%]) and a test dataset (n = 600 [20%]). A pre-trained GoogLeNet Inception v3 CNN network was used for preprocessing and transfer learning. The diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, and area under the curve (AUC) were calculated for detection and diagnostic performance of the deep CNN algorithm. RESULTS The diagnostic accuracies of premolar, molar, and both premolar and molar models were 89.0% (80.4-93.3), 88.0% (79.2-93.1), and 82.0% (75.5-87.1), respectively. The deep CNN algorithm achieved an AUC of 0.917 (95% CI 0.860-0.975) on premolar, an AUC of 0.890 (95% CI 0.819-0.961) on molar, and an AUC of 0.845 (95% CI 0.790-0.901) on both premolar and molar models. The premolar model provided the best AUC, which was significantly greater than those for other models (P < 0.001). CONCLUSIONS This study highlighted the potential utility of deep CNN architecture for the detection and diagnosis of dental caries. A deep CNN algorithm provided considerably good performance in detecting dental caries in periapical radiographs. CLINICAL SIGNIfiCANCE: Deep CNN algorithms are expected to be among the most effective and efficient methods for diagnosing dental caries.
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Affiliation(s)
- Jae-Hong Lee
- Department of Periodontology, Daejeon Dental Hospital, Institute of Wonkwang Dental Research, Wonkwang University College of Dentistry, Daejeon, Republic of Korea.
| | - Do-Hyung Kim
- Department of Periodontology, Daejeon Dental Hospital, Institute of Wonkwang Dental Research, Wonkwang University College of Dentistry, Daejeon, Republic of Korea
| | - Seong-Nyum Jeong
- Department of Periodontology, Daejeon Dental Hospital, Institute of Wonkwang Dental Research, Wonkwang University College of Dentistry, Daejeon, Republic of Korea
| | - Seong-Ho Choi
- Department of Periodontology, Research Institute for Periodontal Regeneration, Yonsei University College of Dentistry, Seoul, Republic of Korea
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Valizadeh S, Goodini M, Ehsani S, Mohseni H, Azimi F, Bakhshandeh H. Designing of a Computer Software for Detection of Approximal Caries in Posterior Teeth. IRANIAN JOURNAL OF RADIOLOGY 2015; 12:e16242. [PMID: 26793287 PMCID: PMC4711028 DOI: 10.5812/iranjradiol.12(2)2015.16242] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2013] [Revised: 11/12/2014] [Accepted: 12/03/2014] [Indexed: 11/16/2022]
Abstract
Background: Radiographs, adjunct to clinical examination are always valuable complementary methods for dental caries detection. Recently, progressing in digital imaging system provides possibility of software designing for automatically dental caries detection. Objectives: The aim of this study was to develop and assess the function of diagnostic computer software designed for evaluation of approximal caries in posterior teeth. This software should be able to indicate the depth and location of caries on digital radiographic images. Materials and Methods: Digital radiographs were obtained of 93 teeth including 183 proximal surfaces. These images were used as a database for designing the software and training the software designer. In the design phase, considering the summed density of pixels in rows and columns of the images, the teeth were separated from each other and the unnecessary regions; for example, the root area in the alveolar bone was eliminated. Therefore, based on summed intensities, each image was segmented such that each segment contained only one tooth. Subsequently, based on the fuzzy logic, a well-known data-clustering algorithm named fuzzy c-means (FCM) was applied to the images to cluster or segment each tooth. This algorithm is referred to as a soft clustering method, which assigns data elements to one or more clusters with a specific membership function. Using the extracted clusters, the tooth border was determined and assessed for cavity. The results of histological analysis were used as the gold standard for comparison with the results obtained from the software. Depth of caries was measured, and finally Intraclass Correlation Coefficient (ICC) and Bland-Altman plot were used to show the agreement between the methods. Results: The software diagnosed 60% of enamel caries. The ICC (for detection of enamel caries) between the computer software and histological analysis results was determined as 0.609 (95% confidence interval [CI] = 0.159-0.849) (P = 0.006). Also, the computer program diagnosed 97% of dentin caries and the ICC between the software and histological analysis results for dentin caries was determined as 0.937 (95% CI=0.906-0.958) (P < 0.001). Bland-Altman plot showed an acceptable agreement for measuring the depth of caries in enamel and dentin. Conclusions: The designed software was able to detect a significant number of dentin caries and acceptable measuring of the depth of carious lesions in enamel and dentin. However, the software had limited ability in detecting enamel lesions.
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Affiliation(s)
- Solmaz Valizadeh
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mostafa Goodini
- Department of Endodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sara Ehsani
- Dental Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Corresponding author: Sara Ehsani, Dental Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Tel: +98-2122413897, Fax: +98-2122427753, E-mail:
| | - Hadis Mohseni
- Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
| | - Fateme Azimi
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hooman Bakhshandeh
- Cardiovascular Intervention Research Center, Rajaie Cardiovascular Medical and Research Center, Tehran University of Medical Sciences, Tehran, Iran
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Benson BW, Flint DJ, Liang H, Opatowsky MJ. Advances in diagnostic imaging for pathologic conditions of the jaws. Head Neck Pathol 2014; 8:383-91. [PMID: 25409843 PMCID: PMC4245416 DOI: 10.1007/s12105-014-0575-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2014] [Accepted: 10/13/2014] [Indexed: 12/26/2022]
Abstract
Advances in dental and maxillofacial imaging are delineated along with the advantages and disadvantages of each imaging modality. The imaging modalities that are included are intraoral radiography, panoramic radiography, cone-beam computed tomography, multidetector computed tomography, magnetic resonance imaging, nuclear medicine, and ultrasound.
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Affiliation(s)
- Byron W. Benson
- grid.252888.dDivision of Oral and Maxillofacial Radiology, Department of Diagnostic Sciences, Texas A&M University, Baylor College of Dentistry, 3302 Gaston Avenue, Dallas, TX 75246 USA
| | - Diane J. Flint
- grid.252888.dDivision of Oral and Maxillofacial Radiology, Department of Diagnostic Sciences, Texas A&M University, Baylor College of Dentistry, 3302 Gaston Avenue, Dallas, TX 75246 USA
| | - Hui Liang
- grid.252888.dDivision of Oral and Maxillofacial Radiology, Department of Diagnostic Sciences, Texas A&M University, Baylor College of Dentistry, 3302 Gaston Avenue, Dallas, TX 75246 USA
| | - Michael J. Opatowsky
- grid.264756.40000000446872082Texas A&M University College of Medicine, Bryan, TX USA ,grid.411588.10000000121679807Department of Radiology, Baylor University Medical Center, Dallas, TX USA
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Affiliation(s)
- DJ Manton
- Elsdon Storey Chair of Child Dental Health; Melbourne Dental School; The University of Melbourne; Victoria
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Application of Bayesian classifier for the diagnosis of dental pain. J Med Syst 2010; 36:1425-39. [PMID: 20945154 DOI: 10.1007/s10916-010-9604-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2010] [Accepted: 09/23/2010] [Indexed: 02/05/2023]
Abstract
Toothache is the most common symptom encountered in dental practice. It is subjective and hence, there is a possibility of under or over diagnosis of oral pathologies where patients present with only toothache. Addressing the issue, the paper proposes a methodology to develop a Bayesian classifier for diagnosing some common dental diseases (D = 10) using a set of 14 pain parameters (P = 14). A questionnaire is developed using these variables and filled up by ten dentists (n = 10) with various levels of expertise. Each questionnaire is consisted of 40 real-world cases. Total 14*10*10 combinations of data are hence collected. The reliability of the data (P and D sets) has been tested by measuring (Cronbach's alpha). One-way ANOVA has been used to note the intra and intergroup mean differences. Multiple linear regressions are used for extracting the significant predictors among P and D sets as well as finding the goodness of the model fit. A naïve Bayesian classifier (NBC) is then designed initially that predicts either presence/absence of diseases given a set of pain parameters. The most informative and highest quality datasheet is used for training of NBC and the remaining sheets are used for testing the performance of the classifier. Hill climbing algorithm is used to design a Learned Bayes' classifier (LBC), which learns the conditional probability table (CPT) entries optimally. The developed LBC showed an average accuracy of 72%, which is clinically encouraging to the dentists.
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Li G, Qu XM, Chen Y, Zhang J, Zhang ZY, Ma XC. Diagnostic accuracy of proximal caries by digital radiographs: an in vivo and in vitro comparative study. ACTA ACUST UNITED AC 2010; 109:463-7. [DOI: 10.1016/j.tripleo.2009.10.022] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2009] [Revised: 09/14/2009] [Accepted: 10/11/2009] [Indexed: 10/19/2022]
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