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Min CK, Kim KA, Lee KE, Suh BJ, Jung W. A study on volumetric change of mandibular condyles with osteoarthritis using cone-beam computed tomography. Sci Rep 2024; 14:10232. [PMID: 38702404 PMCID: PMC11068749 DOI: 10.1038/s41598-024-60404-z] [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: 11/02/2023] [Accepted: 04/23/2024] [Indexed: 05/06/2024] Open
Abstract
This study aimed to quantitatively assess three-dimensional changes in the mandibular condyle with osteoarthritis using cone-beam computed tomography (CBCT). Pre- and post-treatment CBCT images of temporomandibular joints (TMJs) from 66 patients were used to assess longitudinal changes in condylar volume within individual patients using 3D slicer software. Total volume difference (dV), net increase (dV + , bone deposition), and net decrease (dV- , bone resorption) after treatment were analyzed based on clinical and radiological factors. Condyles with surface erosion at their first visit showed significantly decreased volume after treatment compared to condyles without erosion (p < 0.05). Amounts of bone resorption and deposition were higher in condyles with surface erosion (both p < 0.01). In patients with condylar erosion, the presence of joint pain was associated with a decrease in condylar volume and an increase in net resorption (both p < 0.01). When both joint pain and condylar erosion were present, patients with parafunctional habits showed reduced condylar volume after treatment (p < 0.05). Condylar volume change after treatment was negatively correlated with the duration of pain relief (R = - 0.501, p < 0.05). These results indicate that condylar erosion and TMJ pain could be significant variables affecting TMJ volume changes after treatment. Establishing appropriate treatment strategies is crucial for managing condylar erosion and TMJ pain.
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Affiliation(s)
- Chang-Ki Min
- Department of Oral and Maxillofacial Radiology, Institute of Oral Bioscience, School of Dentistry, Jeonbuk National University, Jeonju, South Korea
- Research Institute of Clinical Medicine of Jeonbuk National University - Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea
| | - Kyoung-A Kim
- Department of Oral and Maxillofacial Radiology, Institute of Oral Bioscience, School of Dentistry, Jeonbuk National University, Jeonju, South Korea
- Research Institute of Clinical Medicine of Jeonbuk National University - Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea
| | - Kyung-Eun Lee
- Department of Oral Medicine, Institute of Oral Bioscience, School of Dentistry, Jeonbuk National University, Jeonju, South Korea
- Research Institute of Clinical Medicine of Jeonbuk National University - Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea
| | - Bong-Jik Suh
- Department of Oral Medicine, Institute of Oral Bioscience, School of Dentistry, Jeonbuk National University, Jeonju, South Korea
- Research Institute of Clinical Medicine of Jeonbuk National University - Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea
| | - Won Jung
- Department of Oral Medicine, Institute of Oral Bioscience, School of Dentistry, Jeonbuk National University, Jeonju, South Korea.
- Research Institute of Clinical Medicine of Jeonbuk National University - Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea.
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Jiang T, Lau SH, Zhang J, Chan LC, Wang W, Chan PK, Cai J, Wen C. Radiomics signature of osteoarthritis: Current status and perspective. J Orthop Translat 2024; 45:100-106. [PMID: 38524869 PMCID: PMC10958157 DOI: 10.1016/j.jot.2023.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 10/05/2023] [Accepted: 10/10/2023] [Indexed: 03/26/2024] Open
Abstract
Osteoarthritis (OA) is one of the fast-growing disability-related diseases worldwide, which has significantly affected the quality of patients' lives and brings about substantial socioeconomic burdens in medical expenditure. There is currently no cure for OA once the bone damage is established. Unfortunately, the existing radiological examination is limited to grading the disease's severity and is insufficient to precisely diagnose OA, detect early OA or predict OA progression. Therefore, there is a pressing need to develop novel approaches in medical image analysis to detect subtle changes for identifying early OA development and rapid progressors. Recently, radiomics has emerged as a unique approach to extracting high-dimensional imaging features that quantitatively characterise visible or hidden information from routine medical images. Radiomics data mining via machine learning has empowered precise diagnoses and prognoses of disease, mainly in oncology. Mounting evidence has shown its great potential in aiding the diagnosis and contributing to the study of musculoskeletal diseases. This paper will summarise the current development of radiomics at the crossroads between engineering and medicine and discuss the application and perspectives of radiomics analysis for OA diagnosis and prognosis. The translational potential of this article Radiomics is a novel approach used in oncology, and it may also play an essential role in the diagnosis and prognosis of OA. By transforming medical images from qualitative interpretation to quantitative data, radiomics could be the solution for precise early OA detection, progression tracking, and treatment efficacy prediction. Since the application of radiomics in OA is still in the early stages and primarily focuses on fundamental studies, this review may inspire more explorations and bring more promising diagnoses, prognoses, and management results of OA.
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Affiliation(s)
- Tianshu Jiang
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Sing-Hin Lau
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Lok-Chun Chan
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Wei Wang
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Ping-Keung Chan
- Department of Orthopaedics and Traumatology, Queen Mary Hospital, The University of Hong Kong, Hong Kong SAR, China
| | - Jing Cai
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Chunyi Wen
- Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
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Song HJ, Choi HM, Shin BM, Kim YJ, Park MS, Kim C. Age-stratified analysis of temporomandibular joint osteoarthritis using cone-beam computed tomography. Imaging Sci Dent 2024; 54:71-80. [PMID: 38571783 PMCID: PMC10985520 DOI: 10.5624/isd.20230229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/26/2023] [Accepted: 01/08/2024] [Indexed: 04/05/2024] Open
Abstract
Purpose This study aimed to evaluate age-stratified radiographic features in temporomandibular joint osteoarthritis using cone-beam computed tomography. Materials and Methods In total, 210 joints from 183 patients (144 females, 39 males, ranging from 12 to 88 years old with a mean age of 44.75±19.97 years) diagnosed with temporomandibular joint osteoarthritis were stratified by age. Mandibular condyle position and bony changes (flattening, erosion, osteophytes, subchondral sclerosis, and subchondral pseudocysts in both the condyle and articular eminence, thickening of the glenoid fossa, joint space narrowing, and joint loose bodies) were evaluated through cone-beam computed tomography. After adjusting for sex, the association between age groups and radiographic findings was analyzed using both a multiple regression model and a multinomial logistic regression model (α=0.05). Results The prevalence of joint space narrowing and protruded condyle position in the glenoid fossa significantly increased with age (P<0.05). The risks of bony changes, including osteophytes and subchondral pseudocysts in the condyle; flattening, erosion, osteophyte, and subchondral sclerosis in the articular eminence; joint loose bodies; and thickening of the glenoid fossa, also significantly rose with increasing age (P<0.05). The number of radiographic findings increased with age; in particular, the increase was more pronounced in the temporal bone than in the mandibular condyle (P<0.05). Conclusion Increasing age was associated with a higher frequency and greater diversity of bony changes in the temporal bone, as well as a protruded condyle position in the glenoid fossa, resulting in noticeable joint space narrowing in temporomandibular joint osteoarthritis.
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Affiliation(s)
- Hee-Jeong Song
- Department of Oral Medicine and Diagnosis, Research Institute of Oral Science, College of Dentistry, Gangneung-Wonju National University, Gangneung, Korea
| | - Hang-Moon Choi
- Department of Oral and Maxillofacial Radiology, Research Institute of Oral Science, College of Dentistry, Gangneung-Wonju National University, Gangneung, Korea
| | - Bo-Mi Shin
- Department of Dental Hygiene, Research Institute of Oral Science, College of Dentistry, Gangneung-Wonju National University, Gangneung, Korea
| | - Young-Jun Kim
- Department of Oral Medicine and Diagnosis, Research Institute of Oral Science, College of Dentistry, Gangneung-Wonju National University, Gangneung, Korea
| | - Moon-Soo Park
- Department of Oral Medicine and Diagnosis, Research Institute of Oral Science, College of Dentistry, Gangneung-Wonju National University, Gangneung, Korea
| | - Cheul Kim
- Department of Oral Medicine and Diagnosis, Research Institute of Oral Science, College of Dentistry, Gangneung-Wonju National University, Gangneung, Korea
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Schadow JE, Maxey D, Smith TO, Finnilä MAJ, Manske SL, Segal NA, Wong AKO, Davey RA, Turmezei T, Stok KS. Systematic review of computed tomography parameters used for the assessment of subchondral bone in osteoarthritis. Bone 2024; 178:116948. [PMID: 37926204 DOI: 10.1016/j.bone.2023.116948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/04/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023]
Abstract
OBJECTIVE To systematically review the published parameters for the assessment of subchondral bone in human osteoarthritis (OA) using computed tomography (CT) and gain an overview of current practices and standards. DESIGN A literature search of Medline, Embase and Cochrane Library databases was performed with search strategies tailored to each database (search from 2010 to January 2023). The search results were screened independently by two reviewers against pre-determined inclusion and exclusion criteria. Studies were deemed eligible if conducted in vivo/ex vivo in human adults (>18 years) using any type of CT to assess subchondral bone in OA. Extracted data from eligible studies were compiled in a qualitative summary and formal narrative synthesis. RESULTS This analysis included 202 studies. Four groups of CT modalities were identified to have been used for subchondral bone assessment in OA across nine anatomical locations. Subchondral bone parameters measuring similar features of OA were combined in six categories: (i) microstructure, (ii) bone adaptation, (iii) gross morphology (iv) mineralisation, (v) joint space, and (vi) mechanical properties. CONCLUSIONS Clinically meaningful parameter categories were identified as well as categories with the potential to become relevant in the clinical field. Furthermore, we stress the importance of quantification of parameters to improve their sensitivity and reliability for the evaluation of OA disease progression and the need for standardised measurement methods to improve their clinical value.
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Affiliation(s)
- Jemima E Schadow
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia.
| | - David Maxey
- Department of Radiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, United Kingdom.
| | - Toby O Smith
- Warwick Medical School, University of Warwick, United Kingdom.
| | - Mikko A J Finnilä
- Research Unit of Health Science and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.
| | - Sarah L Manske
- Department of Radiology, McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Canada.
| | - Neil A Segal
- Department of Rehabilitation Medicine, The University of Kansas Medical Center, Kansas City, United States.
| | - Andy Kin On Wong
- Joint Department of Medical Imaging, University Health Network, Toronto, Canada; Schroeder's Arthritis Institute, Toronto General Hospital Research Institute, University Health Network, Toronto, Canada.
| | - Rachel A Davey
- Department of Medicine, Austin Health, University of Melbourne, Melbourne, Australia.
| | - Tom Turmezei
- Department of Radiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Norwich, United Kingdom; Norwich Medical School, University of East Anglia, Norwich, United Kingdom.
| | - Kathryn S Stok
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia.
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Shrivastava M, Ye L. Neuroimaging and artificial intelligence for assessment of chronic painful temporomandibular disorders-a comprehensive review. Int J Oral Sci 2023; 15:58. [PMID: 38155153 PMCID: PMC10754947 DOI: 10.1038/s41368-023-00254-z] [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: 08/01/2023] [Revised: 10/19/2023] [Accepted: 10/20/2023] [Indexed: 12/30/2023] Open
Abstract
Chronic Painful Temporomandibular Disorders (TMD) are challenging to diagnose and manage due to their complexity and lack of understanding of brain mechanism. In the past few decades' neural mechanisms of pain regulation and perception have been clarified by neuroimaging research. Advances in the neuroimaging have bridged the gap between brain activity and the subjective experience of pain. Neuroimaging has also made strides toward separating the neural mechanisms underlying the chronic painful TMD. Recently, Artificial Intelligence (AI) is transforming various sectors by automating tasks that previously required humans' intelligence to complete. AI has started to contribute to the recognition, assessment, and understanding of painful TMD. The application of AI and neuroimaging in understanding the pathophysiology and diagnosis of chronic painful TMD are still in its early stages. The objective of the present review is to identify the contemporary neuroimaging approaches such as structural, functional, and molecular techniques that have been used to investigate the brain of chronic painful TMD individuals. Furthermore, this review guides practitioners on relevant aspects of AI and how AI and neuroimaging methods can revolutionize our understanding on the mechanisms of painful TMD and aid in both diagnosis and management to enhance patient outcomes.
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Affiliation(s)
- Mayank Shrivastava
- Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, USA
| | - Liang Ye
- Department of Rehabilitation Medicine, University of Minnesota Medical School, Minneapolis, MN, USA.
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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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Xu L, Chen J, Qiu K, Yang F, Wu W. Artificial intelligence for detecting temporomandibular joint osteoarthritis using radiographic image data: A systematic review and meta-analysis of diagnostic test accuracy. PLoS One 2023; 18:e0288631. [PMID: 37450501 PMCID: PMC10348514 DOI: 10.1371/journal.pone.0288631] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 07/02/2023] [Indexed: 07/18/2023] Open
Abstract
In this review, we assessed the diagnostic efficiency of artificial intelligence (AI) models in detecting temporomandibular joint osteoarthritis (TMJOA) using radiographic imaging data. Based upon the PRISMA guidelines, a systematic review of studies published between January 2010 and January 2023 was conducted using PubMed, Web of Science, Scopus, and Embase. Articles on the accuracy of AI to detect TMJOA or degenerative changes by radiographic imaging were selected. The characteristics and diagnostic information of each article were extracted. The quality of studies was assessed by the QUADAS-2 tool. Pooled data for sensitivity, specificity, and summary receiver operating characteristic curve (SROC) were calculated. Of 513 records identified through a database search, six met the inclusion criteria and were collected. The pooled sensitivity, specificity, and area under the curve (AUC) were 80%, 90%, and 92%, respectively. Substantial heterogeneity between AI models mainly arose from imaging modality, ethnicity, sex, techniques of AI, and sample size. This article confirmed AI models have enormous potential for diagnosing TMJOA automatically through radiographic imaging. Therefore, AI models appear to have enormous potential to diagnose TMJOA automatically using radiographic images. However, further studies are needed to evaluate AI more thoroughly.
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Affiliation(s)
- Liang Xu
- The School of Stomatology, Fujian Medical University, Fuzhou, Fujian, China
- Department of Stomatology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Jiang Chen
- The School of Stomatology, Fujian Medical University, Fuzhou, Fujian, China
- School and Hospital of Stomatology, Fujian Medical University, Fuzhou, Fujian, China
| | - Kaixi Qiu
- Fuzhou No. 1 Hospital Affiliated with Fujian Medical University, Fuzhou, Fujian, China
| | - Feng Yang
- School and Hospital of Stomatology, Fujian Medical University, Fuzhou, Fujian, China
| | - Weiliang Wu
- The School of Stomatology, Fujian Medical University, Fuzhou, Fujian, China
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Ricardo ALF, da Silva GA, Ogawa CM, Nussi AD, De Rosa CS, Martins JS, de Castro Lopes SLP, Appenzeller S, Braz-Silva PH, Costa ALF. Magnetic resonance imaging texture analysis for quantitative evaluation of the mandibular condyle in juvenile idiopathic arthritis. Oral Radiol 2023; 39:329-340. [PMID: 35948783 DOI: 10.1007/s11282-022-00641-y] [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: 05/04/2022] [Accepted: 07/13/2022] [Indexed: 10/15/2022]
Abstract
OBJECTIVES Juvenile idiopathic arthritis (JIA) is a chronic inflammatory disease that affects the joints and other organs, including the development of the former in a growing child. This study aimed to evaluate the feasibility of texture analysis (TA) based on magnetic resonance imaging (MRI) to provide biomarkers that serve to identify patients likely to progress to temporomandibular joint damage by associating JIA with age, gender and disease onset age. METHODS The radiological database was retrospectively reviewed. A total of 45 patients were first divided into control group (23) and JIA group (22). TA was performed using grey-level co-occurrence matrix (GLCM) parameters, in which 11 textural parameters were calculated using MaZda software. These 11 parameters were ranked based on the p value obtained with ANOVA and then correlated with age, gender and disease onset age. RESULTS Significant differences in texture parameters of condyle were demonstrated between JIA group and control group (p < 0.05). There was a progressive loss of uniformity in the grayscale pixels of MRI with an increasing age in JIA group. CONCLUSIONS MRI TA of the condyle can make it possible to detect the alterations in bone marrow of patients with JIA and promising tool which may help the image analysis.
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Affiliation(s)
- Ana Lúcia Franco Ricardo
- Postgraduate Program in Dentistry, Cruzeiro Do Sul University (UNICSUL), São Paulo, 01506-000, Brazil
| | - Gabriel Araújo da Silva
- Division of Oral Radiology, Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), Campinas, Brazil
| | - Celso Massahiro Ogawa
- Postgraduate Program in Dentistry, Cruzeiro Do Sul University (UNICSUL), São Paulo, 01506-000, Brazil
| | - Amanda D Nussi
- Postgraduate Program in Dentistry, Cruzeiro Do Sul University (UNICSUL), São Paulo, 01506-000, Brazil
| | | | - Jaqueline Serra Martins
- Rheumatology Department, Faculty of Medical Sciences, University of Campinas (UNICAMP), Campinas, SP, Brazil
| | - Sérgio Lúcio Pereira de Castro Lopes
- Department of Diagnosis and Surgery, São José Dos Campos School of Dentistry, São Paulo State University (UNESP), São José dos Campos, SP, Brazil
| | - Simone Appenzeller
- Rheumatology Department, Faculty of Medical Sciences, University of Campinas (UNICAMP), Campinas, SP, Brazil
| | | | - Andre Luiz Ferreira Costa
- Postgraduate Program in Dentistry, Cruzeiro Do Sul University (UNICSUL), São Paulo, 01506-000, Brazil.
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Radiomics approach to the condylar head for legal age classification using cone-beam computed tomography: A pilot study. PLoS One 2023; 18:e0280523. [PMID: 36656878 PMCID: PMC9851527 DOI: 10.1371/journal.pone.0280523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 01/02/2023] [Indexed: 01/20/2023] Open
Abstract
Legal age estimation of living individuals is a critically important issue, and radiomics is an emerging research field that extracts quantitative data from medical images. However, no reports have proposed age-related radiomics features of the condylar head or an age classification model using those features. This study aimed to introduce a radiomics approach for various classifications of legal age (18, 19, 20, and 21 years old) based on cone-beam computed tomography (CBCT) images of the mandibular condylar head, and to evaluate the usefulness of the radiomics features selected by machine learning models as imaging biomarkers. CBCT images from 85 subjects were divided into eight age groups for four legal age classifications: ≤17 and ≥18 years old groups (18-year age classification), ≤18 and ≥19 years old groups (19-year age classification), ≤19 and ≥20 years old groups (20-year age classification) and ≤20 and ≥21 years old groups (21-year age classification). The condylar heads were manually segmented by an expert. In total, 127 radiomics features were extracted from the segmented area of each condylar head. The random forest (RF) method was utilized to select features and develop the age classification model for four legal ages. After sorting features in descending order of importance, the top 10 extracted features were used. The 21-year age classification model showed the best performance, with an accuracy of 91.18%, sensitivity of 80%, and specificity of 95.83%. Radiomics features of the condylar head using CBCT showed the possibility of age estimation, and the selected features were useful as imaging biomarkers.
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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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Mackie T, Al Turkestani N, Bianchi J, Li T, Ruellas A, Gurgel M, Benavides E, Soki F, Cevidanes L. Quantitative bone imaging biomarkers and joint space analysis of the articular Fossa in temporomandibular joint osteoarthritis using artificial intelligence models. FRONTIERS IN DENTAL MEDICINE 2022; 3:1007011. [PMID: 36404987 PMCID: PMC9673279 DOI: 10.3389/fdmed.2022.1007011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2024] Open
Abstract
Temporomandibular joint osteoarthritis (TMJ OA) is a disease with a multifactorial etiology, involving many pathophysiological processes, and requiring comprehensive assessments to characterize progressive cartilage degradation, subchondral bone remodeling, and chronic pain. This study aimed to integrate quantitative biomarkers of bone texture and morphometry of the articular fossa and joint space to advance the role of imaging phenotypes for diagnosis of Temporomandibular Joint Osteoarthritis (TMJ OA) in early to moderate stages by improving the performance of machine-learning algorithms to detect TMJ OA status. Ninety-two patients were prospectively enrolled (184 h-CBCT scans of the right and left mandibular condyles), divided into two groups: 46 control and 46 TMJ OA subjects. No significant difference in the articular fossa radiomic biomarkers was found between TMJ OA and control patients. The superior condyle-to-fossa distance (p < 0.05) was significantly smaller in diseased patients. The interaction effects of the articular fossa radiomic biomarkers enhanced the performance of machine-learning algorithms to detect TMJ OA status. The LightGBM model achieved an AUC 0.842 to diagnose the TMJ OA status with Headaches and Range of Mouth Opening Without Pain ranked as top features, and top interactions of VE-cadherin in Serum and Angiogenin in Saliva, TGF-β1 in Saliva and Headaches, Gender and Muscle Soreness, PA1 in Saliva and Range of Mouth Opening Without Pain, Lateral Condyle Grey Level Non-Uniformity and Lateral Fossa Short Run Emphasis, TGF-β1 in Serum and Lateral Fossa Trabeculae number, MMP3 in Serum and VEGF in Serum, Headaches and Lateral Fossa Trabecular spacing, Headaches and PA1 in Saliva, and Headaches and BDNF in Saliva. Our preliminary results indicate that condyle imaging features may be more important in regards to main effects, but the fossa imaging features may have a larger contribution in terms of interaction effects. More studies are needed to optimize and further enhance machine-learning algorithms to detect early markers of disease, improve prediction of disease progression and severity to ultimately better serve clinical decision support systems in the treatment of patients with TMJ OA.
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Affiliation(s)
- Tamara Mackie
- Department of Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, MI, United States
| | - Najla Al Turkestani
- Department of Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, MI, United States
- Department of Restorative and Aesthetic Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Jonas Bianchi
- Department of Orthodontics, University of the Pacific, Arthur Dugoni School of Dentistry, San Francisco, CA, United States
| | - Tengfei Li
- Department of Radiology and Biomedical Research Imaging Center, University of North, Chapel Hill, NC, United States
| | - Antonio Ruellas
- Department of Orthodontics, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Marcela Gurgel
- Department of Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, MI, United States
| | - Erika Benavides
- Department of Periodontics and Oral Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Fabiana Soki
- Department of Periodontics and Oral Medicine, University of Michigan, Ann Arbor, MI, United States
| | - Lucia Cevidanes
- Department of Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, MI, United States
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12
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Jha N, Lee KS, Kim YJ. Diagnosis of temporomandibular disorders using artificial intelligence technologies: A systematic review and meta-analysis. PLoS One 2022; 17:e0272715. [PMID: 35980894 PMCID: PMC9387829 DOI: 10.1371/journal.pone.0272715] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 07/25/2022] [Indexed: 11/21/2022] Open
Abstract
Background Artificial intelligence (AI) algorithms have been applied to diagnose temporomandibular disorders (TMDs). However, studies have used different patient selection criteria, disease subtypes, input data, and outcome measures. Resultantly, the performance of the AI models varies. Objective This study aimed to systematically summarize the current literature on the application of AI technologies for diagnosis of different TMD subtypes, evaluate the quality of these studies, and assess the diagnostic accuracy of existing AI models. Materials and methods The study protocol was carried out based on the preferred reporting items for systematic review and meta-analysis protocols (PRISMA). The PubMed, Embase, and Web of Science databases were searched to find relevant articles from database inception to June 2022. Studies that used AI algorithms to diagnose at least one subtype of TMD and those that assessed the performance of AI algorithms were included. We excluded studies on orofacial pain that were not directly related to the TMD, such as studies on atypical facial pain and neuropathic pain, editorials, book chapters, and excerpts without detailed empirical data. The risk of bias was assessed using the QUADAS-2 tool. We used Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) to provide certainty of evidence. Results A total of 17 articles for automated diagnosis of masticatory muscle disorders, TMJ osteoarthrosis, internal derangement, and disc perforation were included; they were retrospective studies, case-control studies, cohort studies, and a pilot study. Seven studies were subjected to a meta-analysis for diagnostic accuracy. According to the GRADE, the certainty of evidence was very low. The performance of the AI models had accuracy and specificity ranging from 84% to 99.9% and 73% to 100%, respectively. The pooled accuracy was 0.91 (95% CI 0.76–0.99), I2 = 97% (95% CI 0.96–0.98), p < 0.001. Conclusions Various AI algorithms developed for diagnosing TMDs may provide additional clinical expertise to increase diagnostic accuracy. However, it should be noted that a high risk of bias was present in the included studies. Also, certainty of evidence was very low. Future research of higher quality is strongly recommended.
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Affiliation(s)
- Nayansi Jha
- University of Ulsan College of Medicine, Seoul, Korea
| | - Kwang-sig Lee
- AI Center, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Yoon-Ji Kim
- Department of Orthodontics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
- * E-mail:
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13
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Gladkova EV, Ulyanov VY, Agafonova NY. Features of osseous regeneration and informative value of subchondral remodeling markers in early signs of primary gonarthrosis. Klin Lab Diagn 2022; 67:433-439. [PMID: 36095078 DOI: 10.51620/0869-2084-2022-67-8-433-439] [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: 06/15/2023]
Abstract
Subchondral remodeling is an important pathogenic mechanism in primary gonarthrosis progress. The role of altered metabolism of osseous tissue in early signs of articular pathology remains vague, and the informative value of biochemical markers is discussible. Our research involved 103 patients (64 women and 39 men) with 0-I gonarthrosis stages and 103 healthy individuals (72 women and 28 men) of 36 to 50 years old. We measured osteocalcin, pyridinoline, type I collagen telopeptides, vitamin B metabolites, cartilage oligomeric matrix protein concentrations, determined the activity of bone alkaline phosphatase, and diagnostic significance of the markers with the ROC curve. We found the increase (p<0.0001) in bone alkaline phosphatase, type I collagen telopeptides, cartilage oligomeric matrix protein concentrations as well as osteocalcin (p<0.0002) in 0-I gonarthrosis stages as compared to the controls. The ROC curve featured 98.1 and 79.6 percent sensitivity and specificity of type I collagen telopeptides; 80.6 and 52.4 percent of osteocalcin; 99.0 and 78.6 percent of pyridinoline, respectively. These findings suggest the significant role of subchondral remodeling in the pathogenesis of early gonarthrosis stages. Pyridinoline and type I collagen telopeptides are the most informative osseous markers detectable in the serum of patients with early gonarthrosis.
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Affiliation(s)
- E V Gladkova
- Federal State Budgetary Educational Institution of Higher Education V.I. Razumovsky Saratov State Medical University of the Ministry of Healthcare of the Russian Federation
| | - V Yu Ulyanov
- Federal State Budgetary Educational Institution of Higher Education V.I. Razumovsky Saratov State Medical University of the Ministry of Healthcare of the Russian Federation
| | - N Yu Agafonova
- Federal State Budgetary Educational Institution of Higher Education V.I. Razumovsky Saratov State Medical University of the Ministry of Healthcare of the Russian Federation
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14
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Oei EHG, Hirvasniemi J, van Zadelhoff TA, van der Heijden RA. Osteoarthritis year in review 2021: imaging. Osteoarthritis Cartilage 2022; 30:226-236. [PMID: 34838670 DOI: 10.1016/j.joca.2021.11.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 10/16/2021] [Accepted: 11/11/2021] [Indexed: 02/02/2023]
Abstract
PURPOSE To provide a narrative review of original articles on imaging of osteoarthritis (OA) published between January 1, 2020 and March 31, 2021, with a special focus on imaging of inflammation, imaging of bone, cartilage and bone-cartilage interactions, imaging of peri-articular tissues, imaging scoring methods for OA, and artificial intelligence (AI) applied to OA imaging. METHODS The Embase, Pubmed, Medline, Cochrane databases were searched for original research articles in the English language on human, in vivo, imaging of OA published between January 1, 2020 and March 31, 2021. Search terms related to osteoarthritis combined with all imaging modalities and artificial intelligence were applied. A selection of articles reporting on one of the focus topics was discussed further. RESULTS The search resulted in 651 articles, of which 214 were deemed relevant to human OA imaging. Among the articles included, the knee joint (69%) and magnetic resonance imaging (MRI) (52%) were the predominant anatomical area and imaging modality studied. There were also a substantial number of papers (n = 46) reporting on AI applications in the field of OA imaging. CONCLUSION Imaging continues to play an important role in the assessment of OA. Recent advances in OA imaging include quantitative, non-contrast, and hybrid imaging techniques for improved characterization of multiple tissue processes in OA. In addition, an increasing effort in AI techniques is undertaken to enhance OA imaging acquisition and analysis.
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Affiliation(s)
- E H G Oei
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, the Netherlands.
| | - J Hirvasniemi
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, the Netherlands.
| | - T A van Zadelhoff
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, the Netherlands.
| | - R A van der Heijden
- Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD Rotterdam, the Netherlands.
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15
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Ajami S, Rodriguez-Florez N, Ong J, Jeelani NUO, Dunaway D, James G, Angullia F, Budden C, Bozkurt S, Ibrahim A, Ferretti P, Schievano S, Borghi A. Mechanical and morphological properties of parietal bone in patients with sagittal craniosynostosis. J Mech Behav Biomed Mater 2021; 125:104929. [PMID: 34773914 DOI: 10.1016/j.jmbbm.2021.104929] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 10/15/2021] [Accepted: 10/23/2021] [Indexed: 12/13/2022]
Abstract
Limited information is available on the effect of sagittal craniosynostosis (CS) on morphological and material properties of the parietal bone. Understanding these properties would not only provide an insight into bone response to surgical procedures but also improve the accuracy of computational models simulating these surgeries. The aim of the present study was to characterise the mechanical and microstructural properties of the cortical table and diploe in parietal bone of patients affected by sagittal CS. Twelve samples were collected from pediatric patients (11 males, and 1 female; age 5.2 ± 1.3 months) surgically treated for sagittal CS. Samples were imaged using micro-computed tomography (micro-CT); and mechanical properties were extracted by means of micro-CT based finite element modelling (micro-FE) of three-point bending test, calibrated using sample-specific experimental data. Reference point indentation (RPI) was used to validate the micro-FE output. Bone samples were classified based on their macrostructure as unilaminar or trilaminar (sandwich) structure. The elastic moduli obtained using RPI and micro-FE approaches for cortical tables (ERPI 3973.33 ± 268.45 MPa and Emicro-FE 3438.11 ± 387.38 MPa) in the sandwich structure and diploe (ERPI1958.17 ± 563.79 MPa and Emicro-FE 1960.66 ± 492.44 MPa) in unilaminar samples were in strong agreement (r = 0.86, p < .01). We found that the elastic modulus of cortical tables and diploe were correlated with bone mineral density. Changes in the microstructure and mechanical properties of bone specimens were found to be irrespective of patients' age. Although younger patients are reported to benefit more from surgical intervention as skull is more malleable, understanding the material properties is critical to better predict the surgical outcome in patients <1 year old since age-related changes were minimal.
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Affiliation(s)
- Sara Ajami
- UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, United Kingdom.
| | - Naiara Rodriguez-Florez
- Universidad de Navarra, TECNUN Escuela de Ingenieros, Spain; Ikerbasque, Basque Foundation of Science, Spain
| | - Juling Ong
- Craniofacial Unit, Great Ormond Street Hospital, London WC1N 3JH, United Kingdom
| | | | - David Dunaway
- Craniofacial Unit, Great Ormond Street Hospital, London WC1N 3JH, United Kingdom
| | - Greg James
- Craniofacial Unit, Great Ormond Street Hospital, London WC1N 3JH, United Kingdom
| | - Freida Angullia
- Craniofacial Unit, Great Ormond Street Hospital, London WC1N 3JH, United Kingdom
| | - Curtis Budden
- Craniofacial Unit, Great Ormond Street Hospital, London WC1N 3JH, United Kingdom
| | - Selim Bozkurt
- UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, United Kingdom; UCL Institute of Cardiovascular Science, London WC1E 6BT, United Kingdom
| | - Amel Ibrahim
- Biomaterials and Biomimetics, NYU College of Dentistry, United States
| | - Patrizia Ferretti
- UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, United Kingdom
| | - Silvia Schievano
- UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, United Kingdom
| | - Alessandro Borghi
- UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, United Kingdom
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16
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Le C, Deleat-Besson R, Prieto J, Brosset S, Dumont M, Zhang W, Cevidanes L, Bianchi J, Ruellas A, Gomes L, Gurgel M, Massaro C, Aliaga-Del Castillo A, Yatabe M, Benavides E, Soki F, Al Turkestani N, Evangelista K, Goncalves J, Valladares-Neto J, Alves Garcia Silva M, Chaves C, Costa F, Garib D, Oh H, Gryak J, Styner M, Fillion-Robin JC, Paniagua B, Najarian K, Soroushmehr R. Automatic Segmentation of Mandibular Ramus and Condyles. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2952-2955. [PMID: 34891864 PMCID: PMC8994041 DOI: 10.1109/embc46164.2021.9630727] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In order to diagnose TMJ pathologies, we developed and tested a novel algorithm, MandSeg, that combines image processing and machine learning approaches for automatically segmenting the mandibular condyles and ramus. A deep neural network based on the U-Net architecture was trained for this task, using 109 cone-beam computed tomography (CBCT) scans. The ground truth label maps were manually segmented by clinicians. The U-Net takes 2D slices extracted from the 3D volumetric images. All the 3D scans were cropped depending on their size in order to keep only the mandibular region of interest. The same anatomic cropping region was used for every scan in the dataset. The scans were acquired at different centers with different resolutions. Therefore, we resized all scans to 512×512 in the pre-processing step where we also performed contrast adjustment as the original scans had low contrast. After the pre-processing, around 350 slices were extracted from each scan, and used to train the U-Net model. For the cross-validation, the dataset was divided into 10 folds. The training was performed with 60 epochs, a batch size of 8 and a learning rate of 2×10-5. The average performance of the models on the test set presented 0.95 ± 0.05 AUC, 0.93 ± 0.06 sensitivity, 0.9998 ± 0.0001 specificity, 0.9996 ± 0.0003 accuracy, and 0.91 ± 0.03 F1 score. This study findings suggest that fast and efficient CBCT image segmentation of the mandibular condyles and ramus from different clinical data sets and centers can be analyzed effectively. Future studies can now extract radiomic and imaging features as potentially relevant objective diagnostic criteria for TMJ pathologies, such as osteoarthritis (OA). The proposed segmentation will allow large datasets to be analyzed more efficiently for disease classification.
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17
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Bianchi J, Ruellas A, Prieto JC, Li T, Soroushmehr R, Najarian K, Gryak J, Deleat-Besson R, Le C, Yatabe M, Gurgel M, Turkestani NA, Paniagua B, Cevidanes L. Decision Support Systems in Temporomandibular Joint Osteoarthritis: A review of Data Science and Artificial Intelligence Applications. Semin Orthod 2021; 27:78-86. [PMID: 34305383 DOI: 10.1053/j.sodo.2021.05.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
With the exponential growth of computational systems and increased patient data acquisition, dental research faces new challenges to manage a large quantity of information. For this reason, data science approaches are needed for the integrative diagnosis of multifactorial diseases, such as Temporomandibular joint (TMJ) Osteoarthritis (OA). The Data science spectrum includes data capture/acquisition, data processing with optimized web-based storage and management, data analytics involving in-depth statistical analysis, machine learning (ML) approaches, and data communication. Artificial intelligence (AI) plays a crucial role in this process. It consists of developing computational systems that can perform human intelligence tasks, such as disease diagnosis, using many features to help in the decision-making support. Patient's clinical parameters, imaging exams, and molecular data are used as the input in cross-validation tasks, and human annotation/diagnosis is also used as the gold standard to train computational learning models and automatic disease classifiers. This paper aims to review and describe AI and ML techniques to diagnose TMJ OA and data science approaches for imaging processing. We used a web-based system for multi-center data communication, algorithms integration, statistics deployment, and process the computational machine learning models. We successfully show AI and data-science applications using patients' data to improve the TMJ OA diagnosis decision-making towards personalized medicine.
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Affiliation(s)
- Jonas Bianchi
- Department of Orthodontics, University of the Pacific, Arthur A. Dugoni School of Dentistry, San Francisco, CA, USA
| | - Antonio Ruellas
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | | | - Tengfei Li
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC
| | - Reza Soroushmehr
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
| | - Romain Deleat-Besson
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - Celia Le
- Department of Orthodontics and Pediatric Dentistry, University of Michigan, Ann Arbor, MI
| | - Marilia Yatabe
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - Marcela Gurgel
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - Najla Al Turkestani
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | | | - Lucia Cevidanes
- Department of Orthodontics and Pediatric Dentistry, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
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