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}
}
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.
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}
}
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.
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. |