Explainable Deep Learning and Biomechanical Modeling for TMJ Disorder Morphological Risk Factors.

ABSTRACT: Clarifying multifactorial musculoskeletal disorder etiologies supports risk analysis and development of targeted prevention and treatment modalities. Deep learning enables comprehensive risk factor identification through systematic analysis of disease datasets but does not provide sufficient context for mechanistic understanding, limiting clinical applicability for etiological investigations. Conversely, multiscale biomechanical modeling can evaluate mechanistic etiology within the relevant biomechanical and physiological context. We propose a hybrid approach combining 3D explainable deep learning and multiscale biomechanical modeling; we applied this approach to investigate temporomandibular joint (TMJ) disorder etiology by systematically identifying risk factors and elucidating mechanist

SUBMITTER: Sun S 

PROVIDER: S-EPMC11343598 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature