Publications
2022
N, Al Turkestani; L, Cai; L, Cevidanes; J, Bianchi; W, Zhang; M, Gurgel; M, Gillot; B, Baguero; K, Najarian; R, Soroushmehr
In: Research Square, 2022.
Abstract | Links | BibTeX | Tags: feature selection, machine learning, osteoarthritis, temporomandibular joint
@article{Bianchi2022e,
title = {Osteoarthritis Diagnosis Integrating Whole Joint Radiomics and Clinical Features for Robust Learning Models using Biological Privileged Information},
author = {Al Turkestani N and Cai L and Cevidanes L and Bianchi J and Zhang W and Gurgel M and Gillot M and Baguero B and Najarian K and Soroushmehr R },
url = {https://www.researchsquare.com/article/rs-1840348/v1},
doi = {https://doi.org/10.21203/rs.3.rs-1840348/v1},
year = {2022},
date = {2022-07-14},
journal = {Research Square},
abstract = {This paper proposes a machine learning model using privileged information (LUPI) and normalized mutual information feature selection method (NMIFS) to build a robust and accurate framework to diagnose patients with Temporomandibular Joint Osteoarthritis (TMJ OA). To build such a model, we employ clinical, quantitative imaging and additional biological markers as privileged information. We show that clinical features play a leading role in the TMJ OA diagnosis and quantitative imaging features, extracted from cone-beam computerized tomography (CBCT) scans, improve the model performance. As the proposed LUPI model employs biological data in the training phase (which boosted the model performance), this data is unnecessary for the testing stage, indicating the model can be widely used even when only clinical and imaging data are collected. The model was validated using 5-fold stratified cross-validation with hyperparameter tuning to avoid the bias of data splitting. Our method achieved an AUC, specificity and precision of 0.81, 0.79 and 0.77, respectively.},
keywords = {feature selection, machine learning, osteoarthritis, temporomandibular joint},
pubstate = {published},
tppubtype = {article}
}
2021
Bianchi, J; Goncalves, J R; de Oliveira Ruellas, A C; Ashman, L M; Vimort, J-B; Yatabe, M; Paniagua, B; Hernandez, P; Benavides, E; Soki, F N; Loshida, M; Cevidanes, L H S
Quantitative bone imaging biomarkers to diagnose temporomandibular joint osteoarthritis. Journal Article
In: International Journal of Oral and Maxillofacial Surgery, vol. 50, no. 2, pp. 227-235, 2021.
Abstract | Links | BibTeX | Tags: AAOF, Adolescents, biomarkers, Cone-beam computed tomography, Cranial base, osteoarthritis, temporomandibular joint
@article{Bianchi2021b,
title = {Quantitative bone imaging biomarkers to diagnose temporomandibular joint osteoarthritis.},
author = {J Bianchi and J R Goncalves and A C de Oliveira Ruellas and L M Ashman and J-B Vimort and M Yatabe and B Paniagua and P Hernandez and E Benavides and F N Soki and M Loshida and L H S Cevidanes},
url = {https://pubmed.ncbi.nlm.nih.gov/32605824/},
doi = {10.1016/j.ijom.2020.04.018},
year = {2021},
date = {2021-02-00},
journal = {International Journal of Oral and Maxillofacial Surgery},
volume = {50},
number = {2},
pages = {227-235},
abstract = {Bone degradation of the condylar surface is seen in temporomandibular joint osteoarthritis (TMJ OA); however, the initial changes occur in the subchondral bone. This cross-sectional study was performed to evaluate 23 subchondral bone imaging biomarkers for TMJ OA. The sample consisted of high-resolution cone beam computed tomography scans of 84 subjects, divided into two groups: TMJ OA (45 patients with TMJ OA) and control (39 asymptomatic subjects). Six regions of each mandibular condyle scan were extracted for computation of five bone morphometric and 18 grey-level texture-based variables. The groups were compared using the Mann-Whitney U-test, and the receiver operating characteristics (ROC) curve was determined for each variable that showed a statically significance difference. The results showed statistically significant differences in the subchondral bone microstructure in the lateral and central condylar regions between the control and TMJ OA groups (P< 0.05). The area under the ROC curve (AUC) for these variables was between 0.620 and 0.710. In conclusion, 13 imaging bone biomarkers presented an acceptable diagnostic performance for the diagnosis of TMJ OA, indicating that the texture and geometry of the subchondral bone microarchitecture may be useful for quantitative grading of the disease.},
keywords = {AAOF, Adolescents, biomarkers, Cone-beam computed tomography, Cranial base, osteoarthritis, temporomandibular joint},
pubstate = {published},
tppubtype = {article}
}
2019
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}
}
N, Al Turkestani; L, Cai; L, Cevidanes; J, Bianchi; W, Zhang; M, Gurgel; M, Gillot; B, Baguero; K, Najarian; R, Soroushmehr
Osteoarthritis Diagnosis Integrating Whole Joint Radiomics and Clinical Features for Robust Learning Models using Biological Privileged Information Journal Article
In: Research Square, 2022.
@article{Bianchi2022e,
title = {Osteoarthritis Diagnosis Integrating Whole Joint Radiomics and Clinical Features for Robust Learning Models using Biological Privileged Information},
author = {Al Turkestani N and Cai L and Cevidanes L and Bianchi J and Zhang W and Gurgel M and Gillot M and Baguero B and Najarian K and Soroushmehr R },
url = {https://www.researchsquare.com/article/rs-1840348/v1},
doi = {https://doi.org/10.21203/rs.3.rs-1840348/v1},
year = {2022},
date = {2022-07-14},
journal = {Research Square},
abstract = {This paper proposes a machine learning model using privileged information (LUPI) and normalized mutual information feature selection method (NMIFS) to build a robust and accurate framework to diagnose patients with Temporomandibular Joint Osteoarthritis (TMJ OA). To build such a model, we employ clinical, quantitative imaging and additional biological markers as privileged information. We show that clinical features play a leading role in the TMJ OA diagnosis and quantitative imaging features, extracted from cone-beam computerized tomography (CBCT) scans, improve the model performance. As the proposed LUPI model employs biological data in the training phase (which boosted the model performance), this data is unnecessary for the testing stage, indicating the model can be widely used even when only clinical and imaging data are collected. The model was validated using 5-fold stratified cross-validation with hyperparameter tuning to avoid the bias of data splitting. Our method achieved an AUC, specificity and precision of 0.81, 0.79 and 0.77, respectively.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bianchi, J; Goncalves, J R; de Oliveira Ruellas, A C; Ashman, L M; Vimort, J-B; Yatabe, M; Paniagua, B; Hernandez, P; Benavides, E; Soki, F N; Loshida, M; Cevidanes, L H S
Quantitative bone imaging biomarkers to diagnose temporomandibular joint osteoarthritis. Journal Article
In: International Journal of Oral and Maxillofacial Surgery, vol. 50, no. 2, pp. 227-235, 2021.
@article{Bianchi2021b,
title = {Quantitative bone imaging biomarkers to diagnose temporomandibular joint osteoarthritis.},
author = {J Bianchi and J R Goncalves and A C de Oliveira Ruellas and L M Ashman and J-B Vimort and M Yatabe and B Paniagua and P Hernandez and E Benavides and F N Soki and M Loshida and L H S Cevidanes},
url = {https://pubmed.ncbi.nlm.nih.gov/32605824/},
doi = {10.1016/j.ijom.2020.04.018},
year = {2021},
date = {2021-02-00},
journal = {International Journal of Oral and Maxillofacial Surgery},
volume = {50},
number = {2},
pages = {227-235},
abstract = {Bone degradation of the condylar surface is seen in temporomandibular joint osteoarthritis (TMJ OA); however, the initial changes occur in the subchondral bone. This cross-sectional study was performed to evaluate 23 subchondral bone imaging biomarkers for TMJ OA. The sample consisted of high-resolution cone beam computed tomography scans of 84 subjects, divided into two groups: TMJ OA (45 patients with TMJ OA) and control (39 asymptomatic subjects). Six regions of each mandibular condyle scan were extracted for computation of five bone morphometric and 18 grey-level texture-based variables. The groups were compared using the Mann-Whitney U-test, and the receiver operating characteristics (ROC) curve was determined for each variable that showed a statically significance difference. The results showed statistically significant differences in the subchondral bone microstructure in the lateral and central condylar regions between the control and TMJ OA groups (P< 0.05). The area under the ROC curve (AUC) for these variables was between 0.620 and 0.710. In conclusion, 13 imaging bone biomarkers presented an acceptable diagnostic performance for the diagnosis of TMJ OA, indicating that the texture and geometry of the subchondral bone microarchitecture may be useful for quantitative grading of the disease.},
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 |
N, Al Turkestani; L, Cai; L, Cevidanes; J, Bianchi; W, Zhang; M, Gurgel; M, Gillot; B, Baguero; K, Najarian; R, Soroushmehr: Osteoarthritis Diagnosis Integrating Whole Joint Radiomics and Clinical Features for Robust Learning Models using Biological Privileged Information. In: Research Square, 2022. (Type: Journal Article | Abstract | Links | BibTeX | Tags: feature selection, machine learning, osteoarthritis, temporomandibular joint)@article{Bianchi2022e, This paper proposes a machine learning model using privileged information (LUPI) and normalized mutual information feature selection method (NMIFS) to build a robust and accurate framework to diagnose patients with Temporomandibular Joint Osteoarthritis (TMJ OA). To build such a model, we employ clinical, quantitative imaging and additional biological markers as privileged information. We show that clinical features play a leading role in the TMJ OA diagnosis and quantitative imaging features, extracted from cone-beam computerized tomography (CBCT) scans, improve the model performance. As the proposed LUPI model employs biological data in the training phase (which boosted the model performance), this data is unnecessary for the testing stage, indicating the model can be widely used even when only clinical and imaging data are collected. The model was validated using 5-fold stratified cross-validation with hyperparameter tuning to avoid the bias of data splitting. Our method achieved an AUC, specificity and precision of 0.81, 0.79 and 0.77, respectively. |
2021 |
Bianchi, J; Goncalves, J R; de Oliveira Ruellas, A C; Ashman, L M; Vimort, J-B; Yatabe, M; Paniagua, B; Hernandez, P; Benavides, E; Soki, F N; Loshida, M; Cevidanes, L H S: Quantitative bone imaging biomarkers to diagnose temporomandibular joint osteoarthritis.. In: International Journal of Oral and Maxillofacial Surgery, vol. 50, no. 2, pp. 227-235, 2021. (Type: Journal Article | Abstract | Links | BibTeX | Tags: AAOF, Adolescents, biomarkers, Cone-beam computed tomography, Cranial base, osteoarthritis, temporomandibular joint)@article{Bianchi2021b, Bone degradation of the condylar surface is seen in temporomandibular joint osteoarthritis (TMJ OA); however, the initial changes occur in the subchondral bone. This cross-sectional study was performed to evaluate 23 subchondral bone imaging biomarkers for TMJ OA. The sample consisted of high-resolution cone beam computed tomography scans of 84 subjects, divided into two groups: TMJ OA (45 patients with TMJ OA) and control (39 asymptomatic subjects). Six regions of each mandibular condyle scan were extracted for computation of five bone morphometric and 18 grey-level texture-based variables. The groups were compared using the Mann-Whitney U-test, and the receiver operating characteristics (ROC) curve was determined for each variable that showed a statically significance difference. The results showed statistically significant differences in the subchondral bone microstructure in the lateral and central condylar regions between the control and TMJ OA groups (P< 0.05). The area under the ROC curve (AUC) for these variables was between 0.620 and 0.710. In conclusion, 13 imaging bone biomarkers presented an acceptable diagnostic performance for the diagnosis of TMJ OA, indicating that the texture and geometry of the subchondral bone microarchitecture may be useful for quantitative grading of the disease. |
2019 |
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. |