@article{nokey,

title = {Enhancing airway obstruction diagnosis with multimodal 3D shape analysis},

author = {Lucie Dole and Claudia Trindade Mattos and Jonas Bianchi and Heesoo Oh and Karine Evangelista and José Valladares Neto and Sergio Luiz Mota-Júnior and Lucia Cevidanes and Juan Carlos Prieto},

url = {https://link.springer.com/content/pdf/10.1007/s11548-025-03527-6.pdf?utm_source=scopus&getft_integrator=scopus},

doi = {10.1007/s11548-025-03527-6},

year  = {2025},

date = {2025-09-23},

journal = {International Journal of Computer Assisted Radiology and Surgery},

abstract = {Purpose: Enlarged adenoids that obstruct nasal breathing can cause significant health complications, including cognitive deficits, cardiovascular risks, and developmental delays. Early and accurate diagnosis is critical for effective treatment planning, but current diagnostic methods—such as polysomnography and clinical visual inspection—are either time-consuming, expensive, or lack sufficient accuracy. As cone-beam computed tomography (CBCT) scans are frequently available for these patients and may complement diagnosis, we propose an open-source, automated deep learning tool for quantitative airway obstruction assessment. Our method leverages CBCT scans, which are automatically segmented and processed to extract 3D airway morphology. Methods: Our approach combines two advanced techniques for 3D shape analysis: multi-view and point cloud representations to capture both global and local airway features, enhancing classification and regression performance. Results: Our model achieves an accuracy of 81.88% in classifying the presence or absence of adenoid hypertrophy and demonstrates improved performance in predicting the nasopharynx airway obstruction ratio. While the model performs well in detecting severe cases, further refinement is needed to improve classification and regression across all severity levels. Conclusion: This tool has the potential to enhance clinical workflows by providing rapid, quantitative, and reproducible assessments of airway obstruction, offering a promising solution for improving diagnostic efficiency and patient outcomes in clinical practice.},

keywords = {airway obstruction, Classification, regression, shape analysis},

pubstate = {published},

tppubtype = {article}

}

@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}

}