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Pul U, Schwendicke F. Artificial intelligence for detecting periapical radiolucencies: A systematic review and meta-analysis. J Dent 2024; 147:105104. [PMID: 38851523 DOI: 10.1016/j.jdent.2024.105104] [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: 04/20/2024] [Revised: 05/24/2024] [Accepted: 05/27/2024] [Indexed: 06/10/2024] Open
Abstract
OBJECTIVES Dentists' diagnostic accuracy in detecting periapical radiolucency varies considerably. This systematic review and meta-analysis aimed to investigate the accuracy of artificial intelligence (AI) for detecting periapical radiolucency. DATA Studies reporting diagnostic accuracy and utilizing AI for periapical radiolucency detection, published until November 2023, were eligible for inclusion. Meta-analysis was conducted using the online MetaDTA Tool to calculate pooled sensitivity and specificity. Risk of bias was evaluated using QUADAS-2. SOURCES A comprehensive search was conducted in PubMed/MEDLINE, ScienceDirect, and Institute of Electrical and Electronics Engineers (IEEE) Xplore databases. Studies reporting diagnostic accuracy and utilizing AI tools for periapical radiolucency detection, published until November 2023, were eligible for inclusion. STUDY SELECTION We identified 210 articles, of which 24 met the criteria for inclusion in the review. All but one study used one type of convolutional neural network. The body of evidence comes with an overall unclear to high risk of bias and several applicability concerns. Four of the twenty-four studies were included in a meta-analysis. AI showed a pooled sensitivity and specificity of 0.94 (95 % CI = 0.90-0.96) and 0.96 (95 % CI = 0.91-0.98), respectively. CONCLUSIONS AI demonstrated high specificity and sensitivity for detecting periapical radiolucencies. However, the current landscape suggests a need for diverse study designs beyond traditional diagnostic accuracy studies. Prospective real-life randomized controlled trials using heterogeneous data are needed to demonstrate the true value of AI. CLINICAL SIGNIFICANCE Artificial intelligence tools seem to have the potential to support detecting periapical radiolucencies on imagery. Notably, nearly all studies did not test fully fledged software systems but measured the mere accuracy of AI models in diagnostic accuracy studies. The true value of currently available AI-based software for lesion detection on both 2D and 3D radiographs remains uncertain.
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Affiliation(s)
- Utku Pul
- University for Digital Technologies in Medicine and Dentistry, Wiltz, Luxembourg
| | - Falk Schwendicke
- Conservative Dentistry and Periodontology, LMU Klinikum, Goethestr. 70, Munich 80336, Germany.
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Kim P, Seo B, De Silva H. Concordance of clinician, Chat-GPT4, and ORAD diagnoses against histopathology in Odontogenic Keratocysts and tumours: a 15-Year New Zealand retrospective study. Oral Maxillofac Surg 2024:10.1007/s10006-024-01284-5. [PMID: 39060850 DOI: 10.1007/s10006-024-01284-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 07/19/2024] [Indexed: 07/28/2024]
Abstract
BACKGROUND This research aimed to investigate the concordance between clinical impressions and histopathologic diagnoses made by clinicians and artificial intelligence tools for odontogenic keratocyst (OKC) and Odontogenic tumours (OT) in a New Zealand population from 2008 to 2023. METHODS Histopathological records from the Oral Pathology Centre, University of Otago (2008-2023) were examined to identify OKCs and OT. Specimen referral details, histopathologic reports, and clinician differential diagnoses, as well as those provided by ORAD and Chat-GPT4, were documented. Data were analyzed using SPSS, and concordance between provisional and histopathologic diagnoses was ascertained. RESULTS Of the 34,225 biopsies, 302 and 321 samples were identified as OTs and OKCs. Concordance rates were 43.2% for clinicians, 45.6% for ORAD, and 41.4% for Chat-GPT4. Corresponding Kappa value against histological diagnosis were 0.23, 0.13 and 0.14. Surgeons achieved a higher concordance rate (47.7%) compared to non-surgeons (29.82%). Odds ratio of having concordant diagnosis using Chat-GPT4 and ORAD were between 1.4 and 2.8 (p < 0.05). ROC-AUC and PR-AUC were similar between the groups (Clinician 0.62/0.42, ORAD 0.58/0.28, Char-GPT4 0.63/0.37) for ameloblastoma and for OKC (Clinician 0.64/0.78, ORAD 0.66/0.77, Char-GPT4 0.60/0.71). CONCLUSION Clinicians with surgical training achieved higher concordance rate when it comes to OT and OKC. Chat-GPT4 and Bayesian approach (ORAD) have shown potential in enhancing diagnostic capabilities.
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Affiliation(s)
- Paul Kim
- Oral and Maxillofacial Surgery Registrar, Dunedin Hospital, Dunedin, New Zealand.
| | - Benedict Seo
- Department of Oral Diagnostic and Surgical Sciences, University of Otago, Dunedin, New Zealand
| | - Harsha De Silva
- Department of Oral Diagnostic and Surgical Sciences, University of Otago, Dunedin, New Zealand
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Shrivastava PK, Hasan S, Abid L, Injety R, Shrivastav AK, Sybil D. Accuracy of machine learning in the diagnosis of odontogenic cysts and tumors: a systematic review and meta-analysis. Oral Radiol 2024; 40:342-356. [PMID: 38530559 DOI: 10.1007/s11282-024-00745-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/06/2024] [Indexed: 03/28/2024]
Abstract
BACKGROUND The recent impact of artificial intelligence in diagnostic services has been enormous. Machine learning tools offer an innovative alternative to diagnose cysts and tumors radiographically that pose certain challenges due to the near similar presentation, anatomical variations, and superimposition. It is crucial that the performance of these models is evaluated for their clinical applicability in diagnosing cysts and tumors. METHODS A comprehensive literature search was carried out on eminent databases for published studies between January 2015 and December 2022. Studies utilizing machine learning models in the diagnosis of odontogenic cysts or tumors using Orthopantomograms (OPG) or Cone Beam Computed Tomographic images (CBCT) were included. QUADAS-2 tool was used for the assessment of the risk of bias and applicability concerns. Meta-analysis was performed for studies reporting sufficient performance metrics, separately for OPG and CBCT. RESULTS 16 studies were included for qualitative synthesis including a total of 10,872 odontogenic cysts and tumors. The sensitivity and specificity of machine learning in diagnosing cysts and tumors through OPG were 0.83 (95% CI 0.81-0.85) and 0.82 (95% CI 0.81-0.83) respectively. Studies utilizing CBCT noted a sensitivity of 0.88 (95% CI 0.87-0.88) and specificity of 0.88 (95% CI 0.87-0.89). Highest classification accuracy was 100%, noted for Support Vector Machine classifier. CONCLUSION The results from the present review favoured machine learning models to be used as a clinical adjunct in the radiographic diagnosis of odontogenic cysts and tumors, provided they undergo robust training with a huge dataset. However, the arduous process, investment, and certain ethical concerns associated with the total dependence on technology must be taken into account. Standardized reporting of outcomes for diagnostic studies utilizing machine learning methods is recommended to ensure homogeneity in assessment criteria, facilitate comparison between different studies, and promote transparency in research findings.
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Affiliation(s)
| | - Shamimul Hasan
- Department of Oral Medicine and Radiology, Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India
| | - Laraib Abid
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India
| | - Ranjit Injety
- Department of Neurology, Christian Medical College & Hospital, Ludhiana, Punjab, India
| | - Ayush Kumar Shrivastav
- Computer Science and Engineering, Centre for Development of Advanced Computing, Noida, Uttar Pradesh, India
| | - Deborah Sybil
- Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
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Shi YJ, Li JP, Wang Y, Ma RH, Wang YL, Guo Y, Li G. Deep learning in the diagnosis for cystic lesions of the jaws: a review of recent progress. Dentomaxillofac Radiol 2024; 53:271-280. [PMID: 38814810 PMCID: PMC11211683 DOI: 10.1093/dmfr/twae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 05/06/2024] [Accepted: 05/09/2024] [Indexed: 06/01/2024] Open
Abstract
Cystic lesions of the gnathic bones present challenges in differential diagnosis. In recent years, artificial intelligence (AI) represented by deep learning (DL) has rapidly developed and emerged in the field of dental and maxillofacial radiology (DMFR). Dental radiography provides a rich resource for the study of diagnostic analysis methods for cystic lesions of the jaws and has attracted many researchers. The aim of the current study was to investigate the diagnostic performance of DL for cystic lesions of the jaws. Online searches were done on Google Scholar, PubMed, and IEEE Xplore databases, up to September 2023, with subsequent manual screening for confirmation. The initial search yielded 1862 titles, and 44 studies were ultimately included. All studies used DL methods or tools for the identification of a variable number of maxillofacial cysts. The performance of algorithms with different models varies. Although most of the reviewed studies demonstrated that DL methods have better discriminative performance than clinicians, further development is still needed before routine clinical implementation due to several challenges and limitations such as lack of model interpretability, multicentre data validation, etc. Considering the current limitations and challenges, future studies for the differential diagnosis of cystic lesions of the jaws should follow actual clinical diagnostic scenarios to coordinate study design and enhance the impact of AI in the diagnosis of oral and maxillofacial diseases.
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Affiliation(s)
- Yu-Jie Shi
- School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
| | - Ju-Peng Li
- School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
| | - Yue Wang
- School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
| | - Ruo-Han Ma
- Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, Beijing, 100081, China
| | - Yan-Lin Wang
- Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, Beijing, 100081, China
| | - Yong Guo
- School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China
| | - Gang Li
- Department of Oral and Maxillofacial Radiology, Peking University School and Hospital of Stomatology, Beijing, 100081, China
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Bayrakdar IS, Elfayome NS, Hussien RA, Gulsen IT, Kuran A, Gunes I, Al-Badr A, Celik O, Orhan K. Artificial intelligence system for automatic maxillary sinus segmentation on cone beam computed tomography images. Dentomaxillofac Radiol 2024; 53:256-266. [PMID: 38502963 PMCID: PMC11056744 DOI: 10.1093/dmfr/twae012] [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: 01/17/2024] [Revised: 02/29/2024] [Accepted: 03/14/2024] [Indexed: 03/21/2024] Open
Abstract
OBJECTIVES The study aims to develop an artificial intelligence (AI) model based on nnU-Net v2 for automatic maxillary sinus (MS) segmentation in cone beam computed tomography (CBCT) volumes and to evaluate the performance of this model. METHODS In 101 CBCT scans, MS were annotated using the CranioCatch labelling software (Eskisehir, Turkey) The dataset was divided into 3 parts: 80 CBCT scans for training the model, 11 CBCT scans for model validation, and 10 CBCT scans for testing the model. The model training was conducted using the nnU-Net v2 deep learning model with a learning rate of 0.00001 for 1000 epochs. The performance of the model to automatically segment the MS on CBCT scans was assessed by several parameters, including F1-score, accuracy, sensitivity, precision, area under curve (AUC), Dice coefficient (DC), 95% Hausdorff distance (95% HD), and Intersection over Union (IoU) values. RESULTS F1-score, accuracy, sensitivity, precision values were found to be 0.96, 0.99, 0.96, 0.96, respectively for the successful segmentation of maxillary sinus in CBCT images. AUC, DC, 95% HD, IoU values were 0.97, 0.96, 1.19, 0.93, respectively. CONCLUSIONS Models based on nnU-Net v2 demonstrate the ability to segment the MS autonomously and accurately in CBCT images.
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Affiliation(s)
- Ibrahim Sevki Bayrakdar
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, 26040, Turkey
| | - Nermin Sameh Elfayome
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Cairo University, Cairo, 12613, Egypt
| | - Reham Ashraf Hussien
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Cairo University, Cairo, 12613, Egypt
| | - Ibrahim Tevfik Gulsen
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Alanya Alaaddin Keykubat University, Antalya, 07425, Turkey
| | - Alican Kuran
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Kocaeli University, Kocaeli 41190, Turkey
| | - Ihsan Gunes
- Open and Distance Education Application and Research Center, Eskisehir Technical University, Eskisehir, 26555, Turkey
| | - Alwaleed Al-Badr
- Restorative Dentistry, Riyadh Elm University, Riyadh, 13244, Saudi Arabia
| | - Ozer Celik
- Department of Mathematics-Computer, Eskisehir Osmangazi University Faculty of Science, Eskisehir, 26040, Turkey
| | - Kaan Orhan
- Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, 06560, Turkey
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Hartoonian S, Hosseini M, Yousefi I, Mahdian M, Ghazizadeh Ahsaie M. Applications of artificial intelligence in dentomaxillofacial imaging-a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol 2024:S2212-4403(23)01566-3. [PMID: 38637235 DOI: 10.1016/j.oooo.2023.12.790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/02/2023] [Accepted: 12/22/2023] [Indexed: 04/20/2024]
Abstract
BACKGROUND Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities. STUDY DESIGN A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another. RESULTS The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection. CONCLUSION Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.
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Affiliation(s)
- Serlie Hartoonian
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Matine Hosseini
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Iman Yousefi
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mina Mahdian
- Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Mitra Ghazizadeh Ahsaie
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Yeshua T, Ladyzhensky S, Abu-Nasser A, Abdalla-Aslan R, Boharon T, Itzhak-Pur A, Alexander A, Chaurasia A, Cohen A, Sosna J, Leichter I, Nadler C. Deep learning for detection and 3D segmentation of maxillofacial bone lesions in cone beam CT. Eur Radiol 2023; 33:7507-7518. [PMID: 37191921 DOI: 10.1007/s00330-023-09726-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/30/2023] [Accepted: 04/21/2023] [Indexed: 05/17/2023]
Abstract
OBJECTIVES To develop an automated deep-learning algorithm for detection and 3D segmentation of incidental bone lesions in maxillofacial CBCT scans. METHODS The dataset included 82 cone beam CT (CBCT) scans, 41 with histologically confirmed benign bone lesions (BL) and 41 control scans (without lesions), obtained using three CBCT devices with diverse imaging protocols. Lesions were marked in all axial slices by experienced maxillofacial radiologists. All cases were divided into sub-datasets: training (20,214 axial images), validation (4530 axial images), and testing (6795 axial images). A Mask-RCNN algorithm segmented the bone lesions in each axial slice. Analysis of sequential slices was used for improving the Mask-RCNN performance and classifying each CBCT scan as containing bone lesions or not. Finally, the algorithm generated 3D segmentations of the lesions and calculated their volumes. RESULTS The algorithm correctly classified all CBCT cases as containing bone lesions or not, with an accuracy of 100%. The algorithm detected the bone lesion in axial images with high sensitivity (95.9%) and high precision (98.9%) with an average dice coefficient of 83.5%. CONCLUSIONS The developed algorithm detected and segmented bone lesions in CBCT scans with high accuracy and may serve as a computerized tool for detecting incidental bone lesions in CBCT imaging. CLINICAL RELEVANCE Our novel deep-learning algorithm detects incidental hypodense bone lesions in cone beam CT scans, using various imaging devices and protocols. This algorithm may reduce patients' morbidity and mortality, particularly since currently, cone beam CT interpretation is not always preformed. KEY POINTS • A deep learning algorithm was developed for automatic detection and 3D segmentation of various maxillofacial bone lesions in CBCT scans, irrespective of the CBCT device or the scanning protocol. • The developed algorithm can detect incidental jaw lesions with high accuracy, generates a 3D segmentation of the lesion, and calculates the lesion volume.
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Affiliation(s)
- Talia Yeshua
- Department of Applied Physics, The Jerusalem College of Technology, Jerusalem, Israel
| | - Shmuel Ladyzhensky
- Department of Applied Physics, The Jerusalem College of Technology, Jerusalem, Israel
| | - Amal Abu-Nasser
- Oral Maxillofacial Imaging, Department of Oral Medicine, Sedation and Imaging, Faculty of Dental Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ragda Abdalla-Aslan
- Department of Oral Medicine, Sedation and Imaging, Faculty of Dental Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel
- Department of Oral and Maxillofacial Surgery, Rambam Health Care Campus, Haifa, Israel
| | - Tami Boharon
- Department of Software Engineering, The Jerusalem College of Technology, Jerusalem, Israel
| | - Avital Itzhak-Pur
- Department of Software Engineering, The Jerusalem College of Technology, Jerusalem, Israel
| | - Asher Alexander
- Department of Software Engineering, The Jerusalem College of Technology, Jerusalem, Israel
| | - Akhilanand Chaurasia
- Department of Oral Medicine and Radiology, King George's Medical University, Lucknow, India
| | - Adir Cohen
- Department of Oral and Maxillofacial Surgery, Faculty of Dental Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Jacob Sosna
- Department of Radiology, Faculty of Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Isaac Leichter
- Department of Applied Physics, The Jerusalem College of Technology, Jerusalem, Israel
- Department of Radiology, Faculty of Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Chen Nadler
- Department of Oral Medicine, Sedation and Imaging, Faculty of Dental Medicine, Hadassah Medical Center, Hebrew University of Jerusalem, Jerusalem, Israel.
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He X, Chen Z, Gao Y, Wang W, You M. Reproducibility and location-stability of radiomic features derived from cone-beam computed tomography: a phantom study. Dentomaxillofac Radiol 2023; 52:20230180. [PMID: 37664997 DOI: 10.1259/dmfr.20230180] [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] [Indexed: 09/05/2023] Open
Abstract
OBJECTIVES This study aims to determine the reproducibility and location-stability of cone-beam computed tomography (CBCT) radiomic features. METHODS Centrifugal tubes with six concentrations of K2HPO4 solutions (50, 100, 200, 400, 600, and 800 mg ml-1) were imaged within a customized phantom. For each concentration, images were captured twice as test and retest sets. Totally, 69 radiomic features were extracted by LIFEx. The reproducibility was assessed between the test and retest sets. We used the concordance correlation coefficient (CCC) to screen qualified features and then compared the differences in the numbers of them under 24 series (four locations groups * six concentrations). The location-stability was assessed using the Kruskal-Wallis test under different concentration sets; likewise, the numbers of qualified features under six test sets were analyzed. RESULTS There were 20 and 23 qualified features in the reproducibility and location-stability experiments, respectively. In the reproducibility experiment, the performance of the peripheral groups and high-concentration sets was significantly better than the center groups and low-concentration sets. The effect of concentration on the location-stability of features was not monotonic, and the number of qualified features in the low-concentration sets was greater than that in the high-concentration sets. No features were qualified in both experiments. CONCLUSIONS The density and location of the target object can affect the number of reproducible radiomic features, and its density can also affect the number of location-stable radiomic features. The problem of feature reliability should be treated cautiously in radiomic research on CBCT.
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Affiliation(s)
- Xian He
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China School of Stomatology, Sichuan University, Chengdu, China
| | - Zhi Chen
- School of Communication and Electronic Engineering, East China Normal University, Shanghai, China
| | - Yutao Gao
- School of Computer Science, Sichuan University, Chengdu, China
| | - Wanjing Wang
- Faculty of Mathematics, Sichuan University, Chengdu, China
| | - Meng You
- Department of Oral Medical Imaging, State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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Fawaz P, Sayegh PE, Vannet BV. What is the current state of artificial intelligence applications in dentistry and orthodontics? JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2023; 124:101524. [PMID: 37270174 DOI: 10.1016/j.jormas.2023.101524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/08/2023] [Accepted: 05/31/2023] [Indexed: 06/05/2023]
Abstract
BACKGROUND The use of Artificial Intelligence (AI) in the medical field has the potential to bring about significant improvements in patient care and outcomes. AI is being used in dentistry and more specifically in orthodontics through the development of diagnostic imaging tools, the development of treatment planning tools, and the development of robotic surgery. The aim of this study is to present the latest emerging AI softwares and applications in dental field to benefit from. TYPES OF STUDIES REVIEWED Search strategies were conducted in three electronic databases, with no date limits in the following databases up to April 30, 2023: MEDLINE, PUBMED, and GOOGLE® SCHOLAR for articles related to AI in dentistry & orthodontics. No inclusion and exclusion criteria were used for the selection of the articles. Most of the articles included (n = 79) are reviews of the literature, retro/prospective studies, systematic reviews and meta-analyses, and observational studies. RESULTS The use of AI in dentistry and orthodontics is a rapidly growing area of research and development, with the potential to revolutionize the field and bring about significant improvements in patient care and outcomes; this can save clinicians' chair-time and push for more individualized treatment plans. Results from the various studies reported in this review are suggestive that the accuracy of AI-based systems is quite promising and reliable. PRACTICAL IMPLICATIONS AI application in the healthcare field has proven to be efficient and helpful for the dentist to be more precise in diagnosis and clinical decision-making. These systems can simplify the tasks and provide results in quick time which can save dentists time and help them perform their duties more efficiently. These systems can be of greater aid and can be used as auxiliary support for dentists with lesser experience.
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Affiliation(s)
- Paul Fawaz
- Academic Lecturer & Researcher at the Orthodontic department Université de Lorraine, Nancy, France.
| | | | - Bart Vande Vannet
- Clinical and Academical responsable of the Orthodontic department at Université de Lorraine, Nancy, France.
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Kolarkodi SH, Alotaibi KZ. Artificial Intelligence in Diagnosis of Oral Diseases: A Systematic Review. J Contemp Dent Pract 2023; 24:61-68. [PMID: 37189014 DOI: 10.5005/jp-journals-10024-3465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
AIM To understand the role of Artificial intelligence (AI) in oral radiology and its applications. BACKGROUND Over the last two decades, the field of AI has undergone phenomenal progression and expansion. Artificial intelligence applications have taken up new roles in dentistry like digitized data acquisition and machine learning and diagnostic applications. MATERIALS AND METHODS All research papers outlining the population, intervention, control, and outcomes (PICO) questions were searched for in PubMed, ERIC, Embase, CINAHL, database from the last 10 years on first January 2023. Two authors independently reviewed the titles and abstracts of the selected studies, and any discrepancy between the two review authors was handled by a third reviewer. Two independent investigators evaluated all the included studies for the quality assessment using the modified tool for the quality assessment of diagnostic accuracy studies (QUADAS- 2). REVIEW RESULTS After the removal of duplicates and screening of titles and abstracts, 18 full texts were agreed upon for further evaluation, of which 14 that met the inclusion criteria were included in this review. The application of artificial intelligence models has primarily been reported on osteoporosis diagnosis, classification/segmentation of maxillofacial cysts and/or tumors, and alveolar bone resorption. Overall study quality was deemed to be high for two (14%) studies, moderate for six (43%) studies, and low for another six (43%) studies. CONCLUSION The use of AI for patient diagnosis and clinical decision-making can be accomplished with relative ease, and the technology should be regarded as a reliable modality for potential future applications in oral diagnosis.
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Affiliation(s)
- Shaul Hameed Kolarkodi
- Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, Qassim University, Buraydah, Saudi Arabia, Phone: +96 6533653299, e-mail:
| | - Khalid Zabin Alotaibi
- Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, Qassim University, Buraydah, Saudi Arabia
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Santos GNM, da Silva HEC, Ossege FEL, Figueiredo PTDS, Melo NDS, Stefani CM, Leite AF. Radiomics in bone pathology of the jaws. Dentomaxillofac Radiol 2023; 52:20220225. [PMID: 36416666 PMCID: PMC9793454 DOI: 10.1259/dmfr.20220225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 09/02/2022] [Accepted: 10/02/2022] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVE To define which are and how the radiomics features of jawbone pathologies are extracted for diagnosis, predicting prognosis and therapeutic response. METHODS A comprehensive literature search was conducted using eight databases and gray literature. Two independent observers rated these articles according to exclusion and inclusion criteria. 23 papers were included to assess the radiomics features related to jawbone pathologies. Included studies were evaluated by using JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies. RESULTS Agnostic features were mined from periapical, dental panoramic radiographs, cone beam CT, CT and MRI images of six different jawbone alterations. The most frequent features mined were texture-, shape- and intensity-based features. Only 13 studies described the machine learning step, and the best results were obtained with Support Vector Machine and random forest classifier. For osteoporosis diagnosis and classification, filtering, shape-based and Tamura texture features showed the best performance. For temporomandibular joint pathology, gray-level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), first-order statistics analysis and shape-based analysis showed the best results. Considering odontogenic and non-odontogenic cysts and tumors, contourlet and SPHARM features, first-order statistical features, GLRLM, GLCM had better indexes. For odontogenic cysts and granulomas, first-order statistical analysis showed better classification results. CONCLUSIONS GLCM was the most frequent feature, followed by first-order statistics, and GLRLM features. No study reported predicting response, prognosis or therapeutic response, but instead diseases diagnosis or classification. Although the lack of standardization in the radiomics workflow of the included studies, texture analysis showed potential to contribute to radiologists' reports, decreasing the subjectivity and leading to personalized healthcare.
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Affiliation(s)
| | | | | | | | - Nilce de Santos Melo
- Dentistry Department, Faculty of Health Science, University of Brasília, Brasilia, Brazil
| | - Cristine Miron Stefani
- Dentistry Department, Faculty of Health Science, University of Brasília, Brasilia, Brazil
| | - André Ferreira Leite
- Dentistry Department, Faculty of Health Science, University of Brasília, Brasilia, Brazil
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12
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Artificial intelligence models for clinical usage in dentistry with a focus on dentomaxillofacial CBCT: a systematic review. Oral Radiol 2023; 39:18-40. [PMID: 36269515 DOI: 10.1007/s11282-022-00660-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 09/29/2022] [Indexed: 01/05/2023]
Abstract
This study aimed at performing a systematic review of the literature on the application of artificial intelligence (AI) in dental and maxillofacial cone beam computed tomography (CBCT) and providing comprehensive descriptions of current technical innovations to assist future researchers and dental professionals. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA) Statement was followed. The study's protocol was prospectively registered. Following databases were searched, based on MeSH and Emtree terms: PubMed/MEDLINE, Embase and Web of Science. The search strategy enrolled 1473 articles. 59 publications were included, which assessed the use of AI on CBCT images in dentistry. According to the PROBAST guidelines for study design, seven papers reported only external validation and 11 reported both model building and validation on an external dataset. 40 studies focused exclusively on model development. The AI models employed mainly used deep learning models (42 studies), while other 17 papers used conventional approaches, such as statistical-shape and active shape models, and traditional machine learning methods, such as thresholding-based methods, support vector machines, k-nearest neighbors, decision trees, and random forests. Supervised or semi-supervised learning was utilized in the majority (96.62%) of studies, and unsupervised learning was used in two (3.38%). 52 publications included studies had a high risk of bias (ROB), two papers had a low ROB, and four papers had an unclear rating. Applications based on AI have the potential to improve oral healthcare quality, promote personalized, predictive, preventative, and participatory dentistry, and expedite dental procedures.
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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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14
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Rasteau S, Ernenwein D, Savoldelli C, Bouletreau P. Artificial intelligence for oral and maxillo-facial surgery: A narrative review. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2022; 123:276-282. [PMID: 35091121 DOI: 10.1016/j.jormas.2022.01.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 01/23/2022] [Indexed: 12/24/2022]
Abstract
Artificial Intelligence (AI) is a set of technologies that simulate human cognition in order to address a specific problem. The improvement in computing speed, the exponential production and the routine collection of data have led to the rapid development of AI in the health sector. In this review, we propose to provide surgeons with the essential technical elements to help them understand the possibilities offered by AI and to review the current applications of AI for oral and maxillofacial surgery (OMFS). The review of the literature reveals a real research boom of AI in all fields in OMFS. The algorithms used are related to machine learning, with a strong representation of the convolutional neural networks specific to deep learning. The complex architecture of these networks gives them the capacity to extract and process the elementary characteristics of an image, and they are therefore particularly used for diagnostic purposes on medical imagery or facial photography. We identified representative articles dealing with AI algorithms providing assistance in diagnosis, therapeutic decision, preoperative planning, or prediction and evaluation of the outcomes. Thanks to their learning, classification, prediction and detection capabilities, AI algorithms complement human skills while limiting their imperfections. However, these algorithms should be subject to rigorous clinical evaluation, and ethical reflection on data protection should be systematically conducted.
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Affiliation(s)
- Simon Rasteau
- Maxillo-Facial Surgery, Facial Plastic Surgery, Stomatology and Oral Surgery, Hospices Civils de Lyon, Lyon-Sud Hospital - Claude-Bernard Lyon 1 University, 165 Chemin du Grand-Revoyet, Pierre-Bénite 69310, France.
| | - Didier Ernenwein
- Department of Pediatric Oral & Maxillofacial & Plastic Surgery, Children's Hospital Robert-Debré, Paris-Diderot University, Paris, France
| | - Charles Savoldelli
- University Institute of the Face and Neck, Côte d'Azur University, Nice University Hospital, 31 Avenue de Valombrose, Nice 06100, France
| | - Pierre Bouletreau
- Maxillo-Facial Surgery, Facial Plastic Surgery, Stomatology and Oral Surgery, Hospices Civils de Lyon, Lyon-Sud Hospital - Claude-Bernard Lyon 1 University, 165 Chemin du Grand-Revoyet, Pierre-Bénite 69310, France
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15
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Potential and impact of artificial intelligence algorithms in dento-maxillofacial radiology. Clin Oral Investig 2022; 26:5535-5555. [PMID: 35438326 DOI: 10.1007/s00784-022-04477-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 03/25/2022] [Indexed: 12/20/2022]
Abstract
OBJECTIVES Novel artificial intelligence (AI) learning algorithms in dento-maxillofacial radiology (DMFR) are continuously being developed and improved using advanced convolutional neural networks. This review provides an overview of the potential and impact of AI algorithms in DMFR. MATERIALS AND METHODS A narrative review was conducted on the literature on AI algorithms in DMFR. RESULTS In the field of DMFR, AI algorithms were mainly proposed for (1) automated detection of dental caries, periapical pathologies, root fracture, periodontal/peri-implant bone loss, and maxillofacial cysts/tumors; (2) classification of mandibular third molars, skeletal malocclusion, and dental implant systems; (3) localization of cephalometric landmarks; and (4) improvement of image quality. Data insufficiency, overfitting, and the lack of interpretability are the main issues in the development and use of image-based AI algorithms. Several strategies have been suggested to address these issues, such as data augmentation, transfer learning, semi-supervised training, few-shot learning, and gradient-weighted class activation mapping. CONCLUSIONS Further integration of relevant AI algorithms into one fully automatic end-to-end intelligent system for possible multi-disciplinary applications is very likely to be a field of increased interest in the future. CLINICAL RELEVANCE This review provides dental practitioners and researchers with a comprehensive understanding of the current development, performance, issues, and prospects of image-based AI algorithms in DMFR.
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Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K. Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofac Radiol 2022; 51:20210197. [PMID: 34233515 PMCID: PMC8693331 DOI: 10.1259/dmfr.20210197] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
In the last few years, artificial intelligence (AI) research has been rapidly developing and emerging in the field of dental and maxillofacial radiology. Dental radiography, which is commonly used in daily practices, provides an incredibly rich resource for AI development and attracted many researchers to develop its application for various purposes. This study reviewed the applicability of AI for dental radiography from the current studies. Online searches on PubMed and IEEE Xplore databases, up to December 2020, and subsequent manual searches were performed. Then, we categorized the application of AI according to similarity of the following purposes: diagnosis of dental caries, periapical pathologies, and periodontal bone loss; cyst and tumor classification; cephalometric analysis; screening of osteoporosis; tooth recognition and forensic odontology; dental implant system recognition; and image quality enhancement. Current development of AI methodology in each aforementioned application were subsequently discussed. Although most of the reviewed studies demonstrated a great potential of AI application for dental radiography, further development is still needed before implementation in clinical routine due to several challenges and limitations, such as lack of datasets size justification and unstandardized reporting format. Considering the current limitations and challenges, future AI research in dental radiography should follow standardized reporting formats in order to align the research designs and enhance the impact of AI development globally.
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Affiliation(s)
| | - Chiaki Doi
- Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, 4–1 Seiryo-machi, Sendai, Japan
| | - Nobuhiro Yoda
- Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, 4–1 Seiryo-machi, Sendai, Japan
| | - Eha Renwi Astuti
- Department of Dentomaxillofacial Radiology, Faculty of Dental Medicine, Universitas Airlangga, Jl. Mayjen Prof. Dr. Moestopo no 47, Surabaya, Indonesia
| | - Keiichi Sasaki
- Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, 4–1 Seiryo-machi, Sendai, Japan
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Kuralt M, Gašperšič R, Fidler A. The precision of gingival recession measurements is increased by an automated curvature analysis method. BMC Oral Health 2021; 21:505. [PMID: 34620155 PMCID: PMC8499415 DOI: 10.1186/s12903-021-01858-9] [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: 07/23/2021] [Accepted: 09/27/2021] [Indexed: 12/03/2022] Open
Abstract
Background
The extent of gingival recession represents one of the most important measures determining outcome of periodontal plastic surgery. The accurate measurements are, thus, critical for optimal treatment planning and outcome evaluation. Present study aimed to introduce automated curvature-based digital gingival recession measurements, evaluate the agreement and reliability of manual measurements, and identify sources of manual variability. Methods Measurement of gingival recessions was performed manually by three examiners and automatically using curvature analysis on representative cross-sections (n = 60). Cemento-enamel junction (CEJ) and gingival margin (GM) measurement points selection was the only variable. Agreement and reliability of measurements were analysed using intra- and inter-examiner correlations and Bland–Altman plots. Measurement point selection variability was evaluated with manual point distance deviation from an automatic point. The effect of curvature on manual point selection was evaluated with scatter plots. Results Bland–Altman plots revealed a high variability of examiner’s recession measurements indicated by high 95% limits of agreement range of approximately 1 mm and several outliers beyond the limits of agreement. CEJ point selection was the main source of examiner’s variability due to smaller curvature values than GM, i.e., median values of − 0.98 mm− 1 and − 4.39 mm− 1, respectively, indicating straighter profile for CEJ point. Scatter plots revealed inverse relationship between curvature and examiner deviation for CEJ point, indicating a threshold curvature value around 1 mm− 1. Conclusions Automated curvature-based approach increases the precision of recession measurements by reproducible measurement point selection. Proposed approach allows evaluation of teeth with indistinguishable CEJ that could be not be included in the previous studies. Supplementary Information The online version contains supplementary material available at 10.1186/s12903-021-01858-9.
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Affiliation(s)
- Marko Kuralt
- Department of Restorative Dentistry and Endodontics, University Medical Centre Ljubljana, Hrvatski trg 6, 1000, Ljubljana, Slovenia. .,Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.
| | - Rok Gašperšič
- Department of Oral Medicine and Periodontology, University Medical Centre Ljubljana, Ljubljana, Slovenia.,Department of Oral Medicine and Periodontology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Aleš Fidler
- Department of Restorative Dentistry and Endodontics, University Medical Centre Ljubljana, Hrvatski trg 6, 1000, Ljubljana, Slovenia.,Department of Endodontics and Operative Dentistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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18
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Bispo MS, Pierre Júnior MLGDQ, Apolinário Jr AL, dos Santos JN, Junior BC, Neves FS, Crusoé-Rebello I. Computer tomographic differential diagnosis of ameloblastoma and odontogenic keratocyst: classification using a convolutional neural network. Dentomaxillofac Radiol 2021; 50:20210002. [PMID: 33882255 PMCID: PMC8474127 DOI: 10.1259/dmfr.20210002] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Revised: 04/11/2021] [Accepted: 04/12/2021] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE To analyse the automatic classification performance of a convolutional neural network (CNN), Google Inception v3, using tomographic images of odontogenic keratocysts (OKCs) and ameloblastomas (AMs). METHODS For construction of the database, we selected axial multidetector CT images from patients with confirmed AM (n = 22) and OKC (n = 18) based on a conclusive histopathological report. The images (n = 350) were segmented manually and data augmentation algorithms were applied, totalling 2500 images. The k-fold × five cross-validation method (k = 2) was used to estimate the accuracy of the CNN model. RESULTS The accuracy and standard deviation (%) of cross-validation for the five iterations performed were 90.16 ± 0.95, 91.37 ± 0.57, 91.62 ± 0.19, 92.48 ± 0.16 and 91.21 ± 0.87, respectively. A higher error rate was observed for the classification of AM images. CONCLUSION This study demonstrated a high classification accuracy of Google Inception v3 for tomographic images of OKCs and AMs. However, AMs images presented the higher error rate.
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Affiliation(s)
- Mayara Simões Bispo
- Postgraduate Program in Dentistry and Health, Federal University of Bahia, Salvador, Brazil
| | | | | | | | - Braulio Carneiro Junior
- Division of Oral and Maxillofacial Surgery, Southwest Bahia State University, Vitória da Conquista, Brazil
| | | | - Iêda Crusoé-Rebello
- Division of Oral and Maxillofacial Radiology, Federal University of Bahia, Salvador, Brazil
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19
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Computed tomography image representation using the Legendre polynomial and spherical harmonics functions. Radiol Phys Technol 2021; 14:113-121. [PMID: 33428117 DOI: 10.1007/s12194-020-00604-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 12/11/2020] [Accepted: 12/15/2020] [Indexed: 10/22/2022]
Abstract
The representation of computed tomography (CT) images using the Legendre polynomial (LPF) and spherical harmonics (SHF) functions was investigated. We selected 100 two-dimensional (2D) CT images of 10 lung cancer patients and 33 three-dimensional (3D) CT images of head and neck cancer patients. The reproducibility of these special functions was evaluated in terms of the normalized cross-correlation (NCC). For the 2D images, the NCC was 0.990 ± 0.002 (1sd) with an LPF of order 70, whereas for the 3D images, the NCC was 0.971 ± 0.004 (1sd) with an SHF of degree 70. The results showed that the LPF was more efficient than the Fourier series. As the thoracic and head areas are cylindrical and spherical, respectively, expansions with the LPF and SHF achieved an efficient representation of the human body. CT image representation with analytical functions can be potentially beneficial, such as in X-ray scattering estimation.
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20
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Mei HX, Cheng JH, Li YZ, Ma HS, Zhang KW, Shou YK, Li Y. [Advances in the application of machine learning in maxillofacial cysts and tumors]. HUA XI KOU QIANG YI XUE ZA ZHI = HUAXI KOUQIANG YIXUE ZAZHI = WEST CHINA JOURNAL OF STOMATOLOGY 2020; 38:687-691. [PMID: 33377348 PMCID: PMC7738924 DOI: 10.7518/hxkq.2020.06.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 01/19/2020] [Indexed: 02/05/2023]
Abstract
The application of artificial intelligence in medicine has gradually received attention along with its development. Many studies have shown that machine learning has a wide range of applications in stomatology, especially in the clinical diagnosis and treatment of maxillofacial cysts and tumors. This article reviews the application of machine learning in maxillofacial cyst and tumor to provide a new method for the diagnosis of oral and maxillofacial diseases.
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Affiliation(s)
- Hong-Xiang Mei
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Jun-Hao Cheng
- College of Computer Science, Sichuan University, Chengdu 610041, China
| | - Yi-Zhou Li
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Huang-Shui Ma
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Kai-Wen Zhang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Yu-Ke Shou
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - Yang Li
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
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Hung K, Yeung AWK, Tanaka R, Bornstein MM. Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17124424. [PMID: 32575560 PMCID: PMC7345758 DOI: 10.3390/ijerph17124424] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 06/12/2020] [Accepted: 06/16/2020] [Indexed: 12/15/2022]
Abstract
The increasing use of three-dimensional (3D) imaging techniques in dental medicine has boosted the development and use of artificial intelligence (AI) systems for various clinical problems. Cone beam computed tomography (CBCT) and intraoral/facial scans are potential sources of image data to develop 3D image-based AI systems for automated diagnosis, treatment planning, and prediction of treatment outcome. This review focuses on current developments and performance of AI for 3D imaging in dentomaxillofacial radiology (DMFR) as well as intraoral and facial scanning. In DMFR, machine learning-based algorithms proposed in the literature focus on three main applications, including automated diagnosis of dental and maxillofacial diseases, localization of anatomical landmarks for orthodontic and orthognathic treatment planning, and general improvement of image quality. Automatic recognition of teeth and diagnosis of facial deformations using AI systems based on intraoral and facial scanning will very likely be a field of increased interest in the future. The review is aimed at providing dental practitioners and interested colleagues in healthcare with a comprehensive understanding of the current trend of AI developments in the field of 3D imaging in dental medicine.
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Affiliation(s)
- Kuofeng Hung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong 999077, China; (K.H.); (A.W.K.Y.); (R.T.)
| | - Andy Wai Kan Yeung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong 999077, China; (K.H.); (A.W.K.Y.); (R.T.)
| | - Ray Tanaka
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong 999077, China; (K.H.); (A.W.K.Y.); (R.T.)
| | - Michael M. Bornstein
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong 999077, China; (K.H.); (A.W.K.Y.); (R.T.)
- Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, 4058 Basel, Switzerland
- Correspondence: ; Tel.: +41-(0)61-267-25-45
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22
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Silva VKS, Vieira WA, Bernardino ÍM, Travençolo BAN, Bittencourt MAV, Blumenberg C, Paranhos LR, Galvão HC. Accuracy of computer-assisted image analysis in the diagnosis of maxillofacial radiolucent lesions: A systematic review and meta-analysis. Dentomaxillofac Radiol 2019; 49:20190204. [PMID: 31709811 DOI: 10.1259/dmfr.20190204] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES This study aimed to search for scientific evidence concerning the accuracy of computer-assisted analysis for diagnosing maxillofacial radiolucent lesions. METHODS A systematic review was conducted according to the statements of Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols and considering 10 databases, including the gray literature. Protocol was registered at the International Prospective Register of Systematic Reviews (CRD42018089945). The population, intervention, comparison and outcome strategy was used to define the eligibility criteria and only diagnostic test studies were included. Their risk of bias was assessed by the Joanna Briggs Institute Critical Appraisal tool. Random-effects model meta-analysis was performed and heterogeneity among the included studies was estimated using the I2 statistic. The grade of recommendation, assessment, development, and evaluation (GRADE) tool assessed the quality of evidence and strength of recommendation across included studies. RESULTS Out of 715 identified citations, four papers, published between 2009 and 2017, fulfilled the criteria and were included in this systematic review. A total of 191 lesions, classified as periapical granuloma and cyst, dentigerous cyst or keratocystic odontogenic tumor, were analyzed. All selected articles scored low risk of bias. The pooled accuracy estimation, regardless of the classification method used, was 88.75% (95% CI = 85.19-92.30). Heterogeneity test reached moderate values (I2 = 57.89%). According to the GRADE tool, the analyzed outcome was classified as having low level of certainty. CONCLUSIONS The overall evaluation showed all studies presented high accuracy rates of computer-aided diagnosis systems in classifying radiolucent maxillofacial lesions compared to histopathological biopsy. However, due to the moderate heterogeneity found among the studies included in this meta-analysis, a pragmatic recommendation about the use of computer-assisted analysis is not possible.
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Affiliation(s)
- Virginia K S Silva
- Department of Dentistry, Postgraduate Program in Health Sciences, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
| | - Walbert A Vieira
- Postgraduate Program in Dentistry, Endodontics Division, Piracicaba Dental School, State University of Campinas, Piracicaba, São Paulo, Brazil
| | - Ítalo M Bernardino
- Department of Dentistry, Postgraduate Program in Dentistry, State University of Paraíba, Campina Grande, Paraíba, Brazil
| | - Bruno A N Travençolo
- Center for Exact Sciences and Technology, School of Computing, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil
| | - Marcos A V Bittencourt
- Department of Pediatric and Community Dentistry, School of Dentistry, Federal University of Bahia, Salvador, Bahia, Brazil
| | - Cauane Blumenberg
- Department of Social Medicine, Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Rio Grande do Sul, Brazil
| | - Luiz R Paranhos
- Department of Preventive and Community Dentistry, School of Dentistry, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil
| | - Hebel C Galvão
- Department of Dentistry, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil
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Oda M, Staziaki PV, Qureshi MM, Andreu-Arasa VC, Li B, Takumi K, Chapman MN, Wang A, Salama AR, Sakai O. Using CT texture analysis to differentiate cystic and cystic-appearing odontogenic lesions. Eur J Radiol 2019; 120:108654. [PMID: 31539792 DOI: 10.1016/j.ejrad.2019.108654] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 08/16/2019] [Accepted: 08/26/2019] [Indexed: 12/19/2022]
Abstract
PURPOSE Cystic and cystic-appearing odontogenic lesions of the jaw may appear similar on CT imaging. Accurate diagnosis is often difficult although the relationship of the lesion to the tooth root or crown may offer a clue to the etiology. The purpose of this study was to evaluate CT texture analysis as an aid in differentiating cystic and cystic-appearing odontogenic lesions of the jaw. METHODS This was an IRB-approved retrospective study including 42 pathology-proven dentigerous cysts, 37 odontogenic keratocysts, and 19 ameloblastomas. Each lesion was manually segmented on axial CT images, and textural features were analyzed using an in-house-developed Matlab-based texture analysis program that extracted 47 texture features from each segmented volume. Statistical analysis was performed comparing all pairs of the three types of lesions. RESULTS Pairwise analysis revealed that nine histogram features, one GLCM feature, three GLRL features, two Laws features, four GLGM features and two Chi-square features showed significant differences between dentigerous cysts and odontogenic keratocysts. Four histogram features and one Chi-square feature showed significant differences between odontogenic keratocysts and ameloblastomas. Two histogram features showed significant differences between dentigerous cysts and ameloblastomas. CONCLUSIONS CT texture analysis may be useful as a noninvasive method to obtain additional quantitative information to differentiate cystic and cystic-appearing odontogenic lesions of the jaw.
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Affiliation(s)
- Masafumi Oda
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States; Division of Oral and Maxillofacial Radiology, Kyushu Dental University, Kitakyushu, Fukuoka, Japan
| | - Pedro V Staziaki
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Muhammad M Qureshi
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States; Deparment of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, United States
| | - V Carlota Andreu-Arasa
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Baojun Li
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Koji Takumi
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States; Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Margaret N Chapman
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Albert Wang
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States
| | - Andrew R Salama
- Deparment of Otolaryngology - Head and Neck Surgery, Boston Medical Center, Boston University School of Medicine, United States; Department of Oral & Maxillofacial Surgery, Boston Medical Center, Boston University Henry M. Goldman School of Dental Medicine, United States
| | - Osamu Sakai
- Department of Radiology, Boston Medical Center, Boston University School of Medicine, United States; Deparment of Radiation Oncology, Boston Medical Center, Boston University School of Medicine, United States; Deparment of Otolaryngology - Head and Neck Surgery, Boston Medical Center, Boston University School of Medicine, United States.
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Hung K, Montalvao C, Tanaka R, Kawai T, Bornstein MM. The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review. Dentomaxillofac Radiol 2019; 49:20190107. [PMID: 31386555 DOI: 10.1259/dmfr.20190107] [Citation(s) in RCA: 134] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVES To investigate the current clinical applications and diagnostic performance of artificial intelligence (AI) in dental and maxillofacial radiology (DMFR). METHODS Studies using applications related to DMFR to develop or implement AI models were sought by searching five electronic databases and four selected core journals in the field of DMFR. The customized assessment criteria based on QUADAS-2 were adapted for quality analysis of the studies included. RESULTS The initial electronic search yielded 1862 titles, and 50 studies were eventually included. Most studies focused on AI applications for an automated localization of cephalometric landmarks, diagnosis of osteoporosis, classification/segmentation of maxillofacial cysts and/or tumors, and identification of periodontitis/periapical disease. The performance of AI models varies among different algorithms. CONCLUSION The AI models proposed in the studies included exhibited wide clinical applications in DMFR. Nevertheless, it is still necessary to further verify the reliability and applicability of the AI models prior to transferring these models into clinical practice.
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Affiliation(s)
- Kuofeng Hung
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Carla Montalvao
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Ray Tanaka
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Taisuke Kawai
- Department of Oral and Maxillofacial Radiology, School of Life Dentistry at Tokyo, Nippon Dental University, Tokyo, Japan
| | - Michael M Bornstein
- Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
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Slusarenko da Silva Y, Stoelinga PJW, Naclério-Homem MDG. The presentation of odontogenic keratocysts in the jaws with an emphasis on the tooth-bearing area: a systematic review and meta-analysis. Oral Maxillofac Surg 2019; 23:133-147. [PMID: 30825057 DOI: 10.1007/s10006-019-00754-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Accepted: 02/21/2019] [Indexed: 06/09/2023]
Abstract
PURPOSE This study was conducted in order to gain insight in the actual ratio of odontogenic keratocysts occurring in the tooth-bearing area as compared to the posterior region of the jaws in order to come up with reliable data to base upon a rational treatment policy. METHODS We searched MEDLINE, Web of Science, Scopus, and Cochrane databases for studies reporting on the location of mandibular and maxillary odontogenic keratocysts. All records were independently assessed and a meta-analysis was performed. Risk difference with a confidence interval of 95% of having the lesion in the posterior region versus the tooth-bearing area was the effect measure. P value for the summary effect of < 0.05 was considered statistically significant. RESULTS The 2615 records retrieved were reduced to 34 studies to be qualitatively/quantitatively assessed. The pooled values showed that the difference in the clinical risk of having keratocysts in the posterior region of the mandible and in the tooth-bearing area of the maxilla is 21 and 43%, respectively (P < 0.02 and P < 0.00001). CONCLUSIONS A substantial amount of keratocysts occur in the tooth-bearing area of the jaws, requiring attention.
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Affiliation(s)
- Yuri Slusarenko da Silva
- Department of Oral & Maxillofacial Surgery, Traumatology and Prosthesis, Faculty of Dentistry of the University of São Paulo, Av Prof. Lineu Prestes 2227, Butantã, São Paulo, 05508-000, Brazil.
| | - Paul J W Stoelinga
- Department of Oral and Maxillofacial Surgery, Radboud University, Nijmegen, The Netherlands
| | - Maria da Graça Naclério-Homem
- Department of Oral & Maxillofacial Surgery, Traumatology and Prosthesis, Faculty of Dentistry of the University of São Paulo, Av Prof. Lineu Prestes 2227, Butantã, São Paulo, 05508-000, Brazil
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26
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Yilmaz E, Kayikcioglu T, Kayipmaz S. Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 146:91-100. [PMID: 28688493 DOI: 10.1016/j.cmpb.2017.05.012] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2016] [Revised: 04/15/2017] [Accepted: 05/26/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVES In this article, we propose a decision support system for effective classification of dental periapical cyst and keratocystic odontogenic tumor (KCOT) lesions obtained via cone beam computed tomography (CBCT). CBCT has been effectively used in recent years for diagnosing dental pathologies and determining their boundaries and content. Unlike other imaging techniques, CBCT provides detailed and distinctive information about the pathologies by enabling a three-dimensional (3D) image of the region to be displayed. METHODS We employed 50 CBCT 3D image dataset files as the full dataset of our study. These datasets were identified by experts as periapical cyst and KCOT lesions according to the clinical, radiographic and histopathologic features. Segmentation operations were performed on the CBCT images using viewer software that we developed. Using the tools of this software, we marked the lesional volume of interest and calculated and applied the order statistics and 3D gray-level co-occurrence matrix for each CBCT dataset. A feature vector of the lesional region, including 636 different feature items, was created from those statistics. Six classifiers were used for the classification experiments. RESULTS The Support Vector Machine (SVM) classifier achieved the best classification performance with 100% accuracy, and 100% F-score (F1) scores as a result of the experiments in which a ten-fold cross validation method was used with a forward feature selection algorithm. SVM achieved the best classification performance with 96.00% accuracy, and 96.00% F1 scores in the experiments in which a split sample validation method was used with a forward feature selection algorithm. SVM additionally achieved the best performance of 94.00% accuracy, and 93.88% F1 in which a leave-one-out (LOOCV) method was used with a forward feature selection algorithm. CONCLUSIONS Based on the results, we determined that periapical cyst and KCOT lesions can be classified with a high accuracy with the models that we built using the new dataset selected for this study. The studies mentioned in this article, along with the selected 3D dataset, 3D statistics calculated from the dataset, and performance results of the different classifiers, comprise an important contribution to the field of computer-aided diagnosis of dental apical lesions.
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Affiliation(s)
- E Yilmaz
- Department of Computer Engineering, Karadeniz Technical University, Trabzon 61080, Turkey.
| | - T Kayikcioglu
- Department of Electrical and Electronics Engineering, Karadeniz Technical University, Trabzon 61080, Turkey
| | - S Kayipmaz
- Department of Oral Diagnosis and Radiology, Karadeniz Technical University Faculty of Dentistry, Trabzon 61080, Turkey
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