Temporomandibular Joint Osteoarthritis Diagnosis Using Privileged Learning of Protein Markers

• Autor(a) principal: Zhang, Winston
• Data de Publicação: 2021
• Outros Autores: Bianchi, Jonas [UNESP], Turkestani, Najla Al, Le, Celia, Deleat-Besson, Romain, Ruellas, Antonio, Cevidanes, Lucia, Yatabe, Marilia, Goncalves, Joao [UNESP], Benavides, Erika, Soki, Fabiana, Prieto, Juan, Paniagua, Beatriz, Najarian, Kayvan, Gryak, Jonathan, Soroushmehr, Reza
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/EMBC46164.2021.9629990; http://hdl.handle.net/11449/223211

Diagnosis of temporomandibular joint (TMJ) Osteoarthritis (OA) before serious degradation of cartilage and subchondral bone occurs can help prevent chronic pain and disability. Clinical, radiomic, and protein markers collected from TMJ OA patients have been shown to be predictive of OA onset. Since protein data can often be unavailable for clinical diagnosis, we harnessed the learning using privileged information (LUPI) paradigm to make use of protein markers only during classifier training. Three different LUPI algorithms are compared with traditional machine learning models on a dataset extracted from 92 unique OA patients and controls. The best classifier performance of 0.80 AUC and 75.6 accuracy was obtained from the KRVFL+ model using privileged protein features. Results show that LUPI-based algorithms using privileged protein data can improve final diagnostic performance of TMJ OA classifiers without needing protein microarray data during classifier diagnosis.