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Tissue optical properties combined with machine learning enables estimation of articular cartilage composition and functional integrity
Author(s) -
Iman Kafian-Attari,
Ervin Nippolainen,
Dmitry V. Semenov,
Markku Hauta-Kasari,
Juha Töyräs,
Isaac O. Afara
Publication year - 2020
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.402929
Subject(s) - artificial intelligence , algorithm , computer science
Absorption and reduced scattering coefficients (μ a, μ s ' ) of biological tissues have shown significant potential in biomedical applications. Thus, they are effective parameters for the characterization of tissue integrity and provide vital information on the health of biological tissues. This study investigates the potential of optical properties (μ a, μ s ' ) for estimating articular cartilage composition and biomechanical properties using multivariate and machine learning techniques. The results suggest that μ a could optimally estimate cartilage proteoglycan content in the superficial zone, in addition to its equilibrium modulus. While μ s ' could effectively estimate the proteoglycan content of the middle and deep zones in addition to the instantaneous and dynamic moduli of articular cartilage.