@article{Bianchi2023c,

title = {Automatic landmark identification in cone‐beam computed tomography. },

author = {Gillot M and Miranda F and Baquero B and Ruellas A and Gurgel M and Al Turkestani N and et al},

url = {https://pubmed.ncbi.nlm.nih.gov/36811276/},

doi = {10.1111/ocr.12642},

year  = {2023},

date = {2023-11-26},

journal = {Orthod Craniofac Res},

volume = {26},

issue = {4},

pages = {560-567},

abstract = {Objective: To present and validate an open-source fully automated landmark placement (ALICBCT) tool for cone-beam computed tomography scans.



Materials and methods: One hundred and forty-three large and medium field of view cone-beam computed tomography (CBCT) were used to train and test a novel approach, called ALICBCT that reformulates landmark detection as a classification problem through a virtual agent placed inside volumetric images. The landmark agents were trained to navigate in a multi-scale volumetric space to reach the estimated landmark position. The agent movements decision relies on a combination of DenseNet feature network and fully connected layers. For each CBCT, 32 ground truth landmark positions were identified by 2 clinician experts. After validation of the 32 landmarks, new models were trained to identify a total of 119 landmarks that are commonly used in clinical studies for the quantification of changes in bone morphology and tooth position.



Results: Our method achieved a high accuracy with an average of 1.54 ± 0.87 mm error for the 32 landmark positions with rare failures, taking an average of 4.2 second computation time to identify each landmark in one large 3D-CBCT scan using a conventional GPU.



Conclusion: The ALICBCT algorithm is a robust automatic identification tool that has been deployed for clinical and research use as an extension in the 3D Slicer platform allowing continuous updates for increased precision.},

keywords = {anatomic landmarks, fiducial markers, machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{Bianchi2023e,

title = {Integrative Risk Predictors of Temporomandibular Joint Osteoarthritis Progression.},

author = {Cai L and Al Turkestani N and Cevidanes L and Bianchi J and Gurgel M and Najarian K and et al},

url = {https://doi.org/10.1117/12.2651940},

doi = {10.1117/12.2651940},

year  = {2023},

date = {2023-04-03},

journal = {Semantic Scholar},

abstract = {In this paper we propose feature selection and machine learning approaches to identify a combination of features for risk prediction of Temporomandibular Joint (TMJ) disease progression. In a sample of 32 TMJ osteoarthritis and 38 controls, feature selection of 5 clinical comorbidities, 43 quantitative imaging, 28 biological features and was performed using Maximum Relevance Minimum Redundancy, Chi-Square and Least Absolute Shrinkage and Selection Operator (LASSO) and Recursive Feature Elimination. We compared the performance of learning using concave and convex kernels (LUCCK), Support Vector Machine (SVM) and Random Forest (RF) approaches to predict disease cure/improvement or persistence/worsening. We show that the SVM model using LASSO achieves area under the curve (AUC), sensitivity and precision of 0.92±0.08, 0.85±0.19 and 0.76 ±0.18, respectively. Baseline levels of headaches, lower back pain, restless sleep, muscle soreness, articular fossa bone surface/bone volume and trabecular separation, condylar High Gray Level Run Emphasis and Short Run High Gray Level Emphasis, saliva levels of 6Ckine, Osteoprotegerin (OPG) and Angiogenin, and serum levels of 6ckine and Brain Derived Neurotrophic Factor (BDNF) were the most frequently occurring features to predict more severe TMJ osteoarthritis prognosis.

},

keywords = {machine learning, temporomandibular joint},

pubstate = {published},

tppubtype = {article}

}

@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}

}