Temporomandibular Joint Osteoarthritis Diagnosis Using Privileged Learning of Protein Markers - Citegraph

Paper Info

Title
Temporomandibular Joint Osteoarthritis Diagnosis Using Privileged Learning of Protein Markers

Abstract
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.

Year	DOI	Venue
2021	10.1109/EMBC46164.2021.9629990	2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)
DocType	Volume	ISSN
Conference	2021	1557-170X
Citations	PageRank	References
0	0.34	0
Authors
16

Authors (16 rows)

Cited by (0 rows)

References (0 rows)

Name	Order	Citations	PageRank
Winston Zhang	1	0	0.68
Jonas Bianchi	2	0	2.70
Najla Al Turkestani	3	0	1.69
Celia Le	4	0	0.34
Romain Deleat-Besson	5	0	1.69
Antonio Ruellas	6	0	3.04
Lucia Cevidanes	7	2	3.43
Marilia Yatabe	8	0	0.68
Joao Goncalves	9	0	1.35
Erika Benavides	10	0	0.34
Fabiana Soki	11	0	2.37
Juan Prieto	12	1	2.39
Beatriz Paniagua	13	0	0.34
Kayvan Najarian	14	0	1.35
Jonathan Gryak	15	3	8.64
Reza Soroushmehr	16	0	2.70

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