Multi-tasking deep network for tinnitus classification and severity prediction from multimodal structural MR images.

Autor:	Lin CT; Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, San Francisco, CA 94143, United States of America., Ghosh S; Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, San Francisco, CA 94143, United States of America., Hinkley LB; Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, San Francisco, CA 94143, United States of America., Dale CL; Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, San Francisco, CA 94143, United States of America., Souza ACS; Department of Telecommunication and Mechatronics Engineering, Federal University of Sao Joao del-Rei, Praca Frei Orlando, 170, Sao Joao del Rei 36307, MG, Brazil., Sabes JH; Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, 2380 Sutter St., San Francisco, CA 94115, United States of America., Hess CP; Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, San Francisco, CA 94143, United States of America., Adams ME; Department of Otolaryngology-Head and Neck Surgery, University of Minnesota, Phillips Wangensteen Building, 516 Delaware St., Minneapolis, MN 55455, United States of America., Cheung SW; Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, 2380 Sutter St., San Francisco, CA 94115, United States of America.; Surgical Services, Veterans Affairs, 4150 Clement St., San Francisco, CA 94121, United States of America., Nagarajan SS; Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, San Francisco, CA 94143, United States of America.; Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, 2380 Sutter St., San Francisco, CA 94115, United States of America.; Surgical Services, Veterans Affairs, 4150 Clement St., San Francisco, CA 94121, United States of America.
Jazyk:	angličtina
Zdroj:	Journal of neural engineering [J Neural Eng] 2023 Jan 18; Vol. 20 (1). Date of Electronic Publication: 2023 Jan 18.
DOI:	10.1088/1741-2552/acab33
Abstrakt:	Objective: Subjective tinnitus is an auditory phantom perceptual disorder without an objective biomarker. Fast and efficient diagnostic tools will advance clinical practice by detecting or confirming the condition, tracking change in severity, and monitoring treatment response. Motivated by evidence of subtle anatomical, morphological, or functional information in magnetic resonance images of the brain, we examine data-driven machine learning methods for joint tinnitus classification (tinnitus or no tinnitus) and tinnitus severity prediction. Approach: We propose a deep multi-task multimodal framework for tinnitus classification and severity prediction using structural MRI (sMRI) data. To leverage complementary information multimodal neuroimaging data, we integrate two modalities of three-dimensional sMRI-T1 weighted (T1w) and T2 weighted (T2w) images. To explore the key components in the MR images that drove task performance, we segment both T1w and T2w images into three different components-cerebrospinal fluid, grey matter and white matter, and evaluate performance of each segmented image. Main results: Results demonstrate that our multimodal framework capitalizes on the information across both modalities (T1w and T2w) for the joint task of tinnitus classification and severity prediction. Significance: Our model outperforms existing learning-based and conventional methods in terms of accuracy, sensitivity, specificity, and negative predictive value. (Creative Commons Attribution license.)
Databáze:	MEDLINE
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