Publications
2022
FGGP, Lima; LGC, Rios; J, Bianchi; JR, Goncalves; LR, Paranhos; WA, Vieira; et al,
Complications of total temporomandibular joint replacement: a systematic review and meta-analysis. Journal Article
In: Int J Oral Maxillofac Surg, vol. 52, iss. 5, pp. 584-594, 2022.
Abstract | Links | BibTeX | Tags: Intraoperative complications, Joint prosthesis, Mandibular prosthesis, Postoperative complications, temporomandibular joint disorders
@article{Bianchi2023,
title = {Complications of total temporomandibular joint replacement: a systematic review and meta-analysis.},
author = {Lima FGGP and Rios LGC and Bianchi J and Goncalves JR and Paranhos LR and Vieira WA and et al},
url = {https://pubmed.ncbi.nlm.nih.gov/36494246/},
doi = {10.1016/j.ijom.2022.10.009},
year = {2022},
date = {2022-12-07},
journal = {Int J Oral Maxillofac Surg},
volume = {52},
issue = {5},
pages = {584-594},
abstract = {The aim of this systematic review was to determine the most prevalent complications resulting from total temporomandibular joint (TMJ) replacement. An electronic search was performed using the Embase, LILACS, MEDLINE (via PubMed), SciELO, Scopus, and Web of Science databases up to June 2022. Prospective and retrospective clinical studies on patients who underwent TMJ replacement were included. Two reviewers performed the study selection, data extraction, and individual risk of bias assessment using the Joanna Briggs Institute Critical Appraisal Tools. The pooled prevalence of each complication was calculated through a proportion meta-analysis using the random-effects model. Twenty-eight studies met the eligibility criteria and were included in the review. All of the eligible studies had a low risk of bias. The results of the meta-analysis revealed that the most prevalent complication was paresis or paralysis of the facial nerve branches (7.8%; 95% confidence interval (CI) 2.6-15.1%, I2 = 94.5%), followed by sensory alterations (1.8%; 95% CI 0.6-4.9%, I2 = 88.8%), heterotopic bone formation (1.0%; 95% CI 0.1-2.5%, I2 = 75.8%), and infection (0.7%; 95% CI 0.1-1.6%, I2 = 22.7%). In conclusion, TMJ replacement has a low prevalence of complications, and most of them can be managed successfully.},
keywords = {Intraoperative complications, Joint prosthesis, Mandibular prosthesis, Postoperative complications, temporomandibular joint disorders},
pubstate = {published},
tppubtype = {article}
}
2019
J, Bianchi; Joao, R C; Ruellas, A C De Oliveira; Vimort, J B; Yatabe, Marilia; Beatriz, P; Pablo, H; Erika, B; Fabiana, N S; Helena, S C Lucia
Software comparison to analyze bone radiomics from high resolution CBCT scans of mandibular condyles. Journal Article
In: Dento Maxillo Facial Radiology, vol. 48, no. 6, 2019.
Abstract | Links | BibTeX | Tags: AAOF, Cone-beam computed tomography, software validation, temporomandibular joint disorders, tomography, X-ray computed
@article{Bianchi2019,
title = {Software comparison to analyze bone radiomics from high resolution CBCT scans of mandibular condyles.},
author = {Bianchi J and R C Joao and A C De Oliveira Ruellas and J B Vimort and Marilia Yatabe and P Beatriz and H Pablo and B Erika and N S Fabiana and S C Lucia Helena },
url = {https://pubmed.ncbi.nlm.nih.gov/31075043/},
doi = {10.1259/dmfr.20190049},
year = {2019},
date = {2019-09-00},
urldate = {2019-09-00},
journal = {Dento Maxillo Facial Radiology},
volume = {48},
number = {6},
abstract = {Radiomics refers to the extraction and analysis of advanced quantitative imaging from medical images to diagnose and/or predict diseases. In the dentistry field, the bone data from mandibular condyles could be computationally analyzed using the voxel information provided by high-resolution CBCT scans to increase the diagnostic power of temporomandibular joint (TMJ) conditions. However, such quantitative information demands innovative computational software, algorithm implementation, and validation. Our study's aim was to compare a newly developed BoneTexture application to two-consolidated software with previous applications in the medical field, Ibex and BoneJ, to extract bone morphometric and textural features from mandibular condyles.},
keywords = {AAOF, Cone-beam computed tomography, software validation, temporomandibular joint disorders, tomography, X-ray computed},
pubstate = {published},
tppubtype = {article}
}
L, Michoud; C, Huang; M, Yatabe; J, Bianchi; et al,
A web-based system for statistical shape analysis in temporomandibular joint osteoarthritis. Journal Article
In: Proc SPIE-the Int Soc Opt Eng, vol. 10953, 2019.
Abstract | Links | BibTeX | Tags: biomarkers, Meshes, osteoarthritis, statistics, temporomandibular joint disorders, web-platform
@article{Michoud2019,
title = {A web-based system for statistical shape analysis in temporomandibular joint osteoarthritis.},
author = {Michoud L and Huang C and Yatabe M and Bianchi J and et al},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6494085/},
doi = {10.1117/12.2506032},
year = {2019},
date = {2019-05-15},
journal = {Proc SPIE-the Int Soc Opt Eng},
volume = {10953},
abstract = {This study presents a web-system repository: Data Storage for Computation and Integration (DSCI) for Osteoarthritis of the temporomandibular joint (TMJ OA). This environment aims to maintain and allow contributions to the database from multi-clinical centers and compute novel statistics for disease classification. For this purpose, imaging datasets stored in the DSCI consisted of three-dimensional (3D) surface meshes of condyles from CBCT, clinical markers and biological markers in healthy and TMJ OA subjects. A clusterpost package was included in the web platform to be able to execute the jobs in remote computing grids. The DSCI application allowed runs of statistical packages, such as the Multivariate Functional Shape Data Analysis to compute global correlations between covariates and the morphological variability, as well as local p-values in the 3D condylar morphology. In conclusion, the DSCI allows interactive advanced statistical tools for non-statistical experts.},
keywords = {biomarkers, Meshes, osteoarthritis, statistics, temporomandibular joint disorders, web-platform},
pubstate = {published},
tppubtype = {article}
}
Nina, T R; De, D Priscille; Marilia, Y; Antonio, R; Marcos, I; Beatriz, P; Martin, S; Joao, R G; Jonas, B; Lucia, C; Juan-Carlos, P
Shape variation analyzer: a classifier for temporomandibular joint damaged by osteoarthritis Journal Article
In: Dento Maxillo Facial Radiology, vol. 10950, 2019.
Abstract | Links | BibTeX | Tags: Classification, Deep Learning, Neural Network, osteoarthritis, temporomandibular joint disorders
@article{Ribera2019,
title = {Shape variation analyzer: a classifier for temporomandibular joint damaged by osteoarthritis},
author = {T R Nina and D Priscille De and Y Marilia and R Antonio and I Marcos and P Beatriz and S Martin and R G Joao and B Jonas and C Lucia and P Juan-Carlos },
url = {https://pubmed.ncbi.nlm.nih.gov/31359900/},
doi = {10.1117/12.2506018},
year = {2019},
date = {2019-02-00},
urldate = {2019-02-00},
journal = {Dento Maxillo Facial Radiology},
volume = {10950},
abstract = {We developed a deep learning neural network, the Shape Variation Analyzer (SVA), that allows disease staging of bony changes in temporomandibular joint (TMJ) osteoarthritis (OA). The sample was composed of 259 TMJ CBCT scans for the training set and 34 for the testing dataset. The 3D meshes had been previously classified in 6 groups by 2 expert clinicians. We improved the robustness of the training data using data augmentation, SMOTE, to alleviate over-fitting and to balance classes. We combined geometrical features and a shape descriptor, heat kernel signature, to describe every shape. The results were compared to nine different supervised machine learning algorithms. The deep learning neural network was the most accurate for classification of TMJ OA. In conclusion, SVA is a 3D Sheer extension that classifies pathology of the temporomandibular joint osteoarthritis cases based on 3D morphology.},
keywords = {Classification, Deep Learning, Neural Network, osteoarthritis, temporomandibular joint disorders},
pubstate = {published},
tppubtype = {article}
}
FGGP, Lima; LGC, Rios; J, Bianchi; JR, Goncalves; LR, Paranhos; WA, Vieira; et al,
Complications of total temporomandibular joint replacement: a systematic review and meta-analysis. Journal Article
In: Int J Oral Maxillofac Surg, vol. 52, iss. 5, pp. 584-594, 2022.
@article{Bianchi2023,
title = {Complications of total temporomandibular joint replacement: a systematic review and meta-analysis.},
author = {Lima FGGP and Rios LGC and Bianchi J and Goncalves JR and Paranhos LR and Vieira WA and et al},
url = {https://pubmed.ncbi.nlm.nih.gov/36494246/},
doi = {10.1016/j.ijom.2022.10.009},
year = {2022},
date = {2022-12-07},
journal = {Int J Oral Maxillofac Surg},
volume = {52},
issue = {5},
pages = {584-594},
abstract = {The aim of this systematic review was to determine the most prevalent complications resulting from total temporomandibular joint (TMJ) replacement. An electronic search was performed using the Embase, LILACS, MEDLINE (via PubMed), SciELO, Scopus, and Web of Science databases up to June 2022. Prospective and retrospective clinical studies on patients who underwent TMJ replacement were included. Two reviewers performed the study selection, data extraction, and individual risk of bias assessment using the Joanna Briggs Institute Critical Appraisal Tools. The pooled prevalence of each complication was calculated through a proportion meta-analysis using the random-effects model. Twenty-eight studies met the eligibility criteria and were included in the review. All of the eligible studies had a low risk of bias. The results of the meta-analysis revealed that the most prevalent complication was paresis or paralysis of the facial nerve branches (7.8%; 95% confidence interval (CI) 2.6-15.1%, I2 = 94.5%), followed by sensory alterations (1.8%; 95% CI 0.6-4.9%, I2 = 88.8%), heterotopic bone formation (1.0%; 95% CI 0.1-2.5%, I2 = 75.8%), and infection (0.7%; 95% CI 0.1-1.6%, I2 = 22.7%). In conclusion, TMJ replacement has a low prevalence of complications, and most of them can be managed successfully.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
J, Bianchi; Joao, R C; Ruellas, A C De Oliveira; Vimort, J B; Yatabe, Marilia; Beatriz, P; Pablo, H; Erika, B; Fabiana, N S; Helena, S C Lucia
Software comparison to analyze bone radiomics from high resolution CBCT scans of mandibular condyles. Journal Article
In: Dento Maxillo Facial Radiology, vol. 48, no. 6, 2019.
@article{Bianchi2019,
title = {Software comparison to analyze bone radiomics from high resolution CBCT scans of mandibular condyles.},
author = {Bianchi J and R C Joao and A C De Oliveira Ruellas and J B Vimort and Marilia Yatabe and P Beatriz and H Pablo and B Erika and N S Fabiana and S C Lucia Helena },
url = {https://pubmed.ncbi.nlm.nih.gov/31075043/},
doi = {10.1259/dmfr.20190049},
year = {2019},
date = {2019-09-00},
urldate = {2019-09-00},
journal = {Dento Maxillo Facial Radiology},
volume = {48},
number = {6},
abstract = {Radiomics refers to the extraction and analysis of advanced quantitative imaging from medical images to diagnose and/or predict diseases. In the dentistry field, the bone data from mandibular condyles could be computationally analyzed using the voxel information provided by high-resolution CBCT scans to increase the diagnostic power of temporomandibular joint (TMJ) conditions. However, such quantitative information demands innovative computational software, algorithm implementation, and validation. Our study's aim was to compare a newly developed BoneTexture application to two-consolidated software with previous applications in the medical field, Ibex and BoneJ, to extract bone morphometric and textural features from mandibular condyles.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
L, Michoud; C, Huang; M, Yatabe; J, Bianchi; et al,
A web-based system for statistical shape analysis in temporomandibular joint osteoarthritis. Journal Article
In: Proc SPIE-the Int Soc Opt Eng, vol. 10953, 2019.
@article{Michoud2019,
title = {A web-based system for statistical shape analysis in temporomandibular joint osteoarthritis.},
author = {Michoud L and Huang C and Yatabe M and Bianchi J and et al},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6494085/},
doi = {10.1117/12.2506032},
year = {2019},
date = {2019-05-15},
journal = {Proc SPIE-the Int Soc Opt Eng},
volume = {10953},
abstract = {This study presents a web-system repository: Data Storage for Computation and Integration (DSCI) for Osteoarthritis of the temporomandibular joint (TMJ OA). This environment aims to maintain and allow contributions to the database from multi-clinical centers and compute novel statistics for disease classification. For this purpose, imaging datasets stored in the DSCI consisted of three-dimensional (3D) surface meshes of condyles from CBCT, clinical markers and biological markers in healthy and TMJ OA subjects. A clusterpost package was included in the web platform to be able to execute the jobs in remote computing grids. The DSCI application allowed runs of statistical packages, such as the Multivariate Functional Shape Data Analysis to compute global correlations between covariates and the morphological variability, as well as local p-values in the 3D condylar morphology. In conclusion, the DSCI allows interactive advanced statistical tools for non-statistical experts.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Nina, T R; De, D Priscille; Marilia, Y; Antonio, R; Marcos, I; Beatriz, P; Martin, S; Joao, R G; Jonas, B; Lucia, C; Juan-Carlos, P
Shape variation analyzer: a classifier for temporomandibular joint damaged by osteoarthritis Journal Article
In: Dento Maxillo Facial Radiology, vol. 10950, 2019.
@article{Ribera2019,
title = {Shape variation analyzer: a classifier for temporomandibular joint damaged by osteoarthritis},
author = {T R Nina and D Priscille De and Y Marilia and R Antonio and I Marcos and P Beatriz and S Martin and R G Joao and B Jonas and C Lucia and P Juan-Carlos },
url = {https://pubmed.ncbi.nlm.nih.gov/31359900/},
doi = {10.1117/12.2506018},
year = {2019},
date = {2019-02-00},
urldate = {2019-02-00},
journal = {Dento Maxillo Facial Radiology},
volume = {10950},
abstract = {We developed a deep learning neural network, the Shape Variation Analyzer (SVA), that allows disease staging of bony changes in temporomandibular joint (TMJ) osteoarthritis (OA). The sample was composed of 259 TMJ CBCT scans for the training set and 34 for the testing dataset. The 3D meshes had been previously classified in 6 groups by 2 expert clinicians. We improved the robustness of the training data using data augmentation, SMOTE, to alleviate over-fitting and to balance classes. We combined geometrical features and a shape descriptor, heat kernel signature, to describe every shape. The results were compared to nine different supervised machine learning algorithms. The deep learning neural network was the most accurate for classification of TMJ OA. In conclusion, SVA is a 3D Sheer extension that classifies pathology of the temporomandibular joint osteoarthritis cases based on 3D morphology.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2022 |
FGGP, Lima; LGC, Rios; J, Bianchi; JR, Goncalves; LR, Paranhos; WA, Vieira; et al,: Complications of total temporomandibular joint replacement: a systematic review and meta-analysis.. In: Int J Oral Maxillofac Surg, vol. 52, iss. 5, pp. 584-594, 2022. (Type: Journal Article | Abstract | Links | BibTeX | Tags: Intraoperative complications, Joint prosthesis, Mandibular prosthesis, Postoperative complications, temporomandibular joint disorders)@article{Bianchi2023, The aim of this systematic review was to determine the most prevalent complications resulting from total temporomandibular joint (TMJ) replacement. An electronic search was performed using the Embase, LILACS, MEDLINE (via PubMed), SciELO, Scopus, and Web of Science databases up to June 2022. Prospective and retrospective clinical studies on patients who underwent TMJ replacement were included. Two reviewers performed the study selection, data extraction, and individual risk of bias assessment using the Joanna Briggs Institute Critical Appraisal Tools. The pooled prevalence of each complication was calculated through a proportion meta-analysis using the random-effects model. Twenty-eight studies met the eligibility criteria and were included in the review. All of the eligible studies had a low risk of bias. The results of the meta-analysis revealed that the most prevalent complication was paresis or paralysis of the facial nerve branches (7.8%; 95% confidence interval (CI) 2.6-15.1%, I2 = 94.5%), followed by sensory alterations (1.8%; 95% CI 0.6-4.9%, I2 = 88.8%), heterotopic bone formation (1.0%; 95% CI 0.1-2.5%, I2 = 75.8%), and infection (0.7%; 95% CI 0.1-1.6%, I2 = 22.7%). In conclusion, TMJ replacement has a low prevalence of complications, and most of them can be managed successfully. |
2019 |
J, Bianchi; Joao, R C; Ruellas, A C De Oliveira; Vimort, J B; Yatabe, Marilia; Beatriz, P; Pablo, H; Erika, B; Fabiana, N S; Helena, S C Lucia: Software comparison to analyze bone radiomics from high resolution CBCT scans of mandibular condyles.. In: Dento Maxillo Facial Radiology, vol. 48, no. 6, 2019. (Type: Journal Article | Abstract | Links | BibTeX | Tags: AAOF, Cone-beam computed tomography, software validation, temporomandibular joint disorders, tomography, X-ray computed)@article{Bianchi2019, Radiomics refers to the extraction and analysis of advanced quantitative imaging from medical images to diagnose and/or predict diseases. In the dentistry field, the bone data from mandibular condyles could be computationally analyzed using the voxel information provided by high-resolution CBCT scans to increase the diagnostic power of temporomandibular joint (TMJ) conditions. However, such quantitative information demands innovative computational software, algorithm implementation, and validation. Our study's aim was to compare a newly developed BoneTexture application to two-consolidated software with previous applications in the medical field, Ibex and BoneJ, to extract bone morphometric and textural features from mandibular condyles. |
L, Michoud; C, Huang; M, Yatabe; J, Bianchi; et al,: A web-based system for statistical shape analysis in temporomandibular joint osteoarthritis.. In: Proc SPIE-the Int Soc Opt Eng, vol. 10953, 2019. (Type: Journal Article | Abstract | Links | BibTeX | Tags: biomarkers, Meshes, osteoarthritis, statistics, temporomandibular joint disorders, web-platform)@article{Michoud2019, This study presents a web-system repository: Data Storage for Computation and Integration (DSCI) for Osteoarthritis of the temporomandibular joint (TMJ OA). This environment aims to maintain and allow contributions to the database from multi-clinical centers and compute novel statistics for disease classification. For this purpose, imaging datasets stored in the DSCI consisted of three-dimensional (3D) surface meshes of condyles from CBCT, clinical markers and biological markers in healthy and TMJ OA subjects. A clusterpost package was included in the web platform to be able to execute the jobs in remote computing grids. The DSCI application allowed runs of statistical packages, such as the Multivariate Functional Shape Data Analysis to compute global correlations between covariates and the morphological variability, as well as local p-values in the 3D condylar morphology. In conclusion, the DSCI allows interactive advanced statistical tools for non-statistical experts. |
Nina, T R; De, D Priscille; Marilia, Y; Antonio, R; Marcos, I; Beatriz, P; Martin, S; Joao, R G; Jonas, B; Lucia, C; Juan-Carlos, P: Shape variation analyzer: a classifier for temporomandibular joint damaged by osteoarthritis. In: Dento Maxillo Facial Radiology, vol. 10950, 2019. (Type: Journal Article | Abstract | Links | BibTeX | Tags: Classification, Deep Learning, Neural Network, osteoarthritis, temporomandibular joint disorders)@article{Ribera2019, We developed a deep learning neural network, the Shape Variation Analyzer (SVA), that allows disease staging of bony changes in temporomandibular joint (TMJ) osteoarthritis (OA). The sample was composed of 259 TMJ CBCT scans for the training set and 34 for the testing dataset. The 3D meshes had been previously classified in 6 groups by 2 expert clinicians. We improved the robustness of the training data using data augmentation, SMOTE, to alleviate over-fitting and to balance classes. We combined geometrical features and a shape descriptor, heat kernel signature, to describe every shape. The results were compared to nine different supervised machine learning algorithms. The deep learning neural network was the most accurate for classification of TMJ OA. In conclusion, SVA is a 3D Sheer extension that classifies pathology of the temporomandibular joint osteoarthritis cases based on 3D morphology. |