组织光学特性与机器学习相结合能够估计关节软骨的组成和功能完整性。
Tissue optical properties combined with machine learning enables estimation of articular cartilage composition and functional integrity.
作者信息
Kafian-Attari Iman, Nippolainen Ervin, Semenov Dmitry, Hauta-Kasari Markku, Töyräs Juha, Afara Isaac O
机构信息
University of Eastern Finland, Department of Applied Physics, Yliopistonranta 1, Kuopio 70120, Finland.
University of Eastern Finland, School of Computing, Lämsikatu 15, Joensuu 80110, Finland.
出版信息
Biomed Opt Express. 2020 Oct 19;11(11):6480-6494. doi: 10.1364/BOE.402929. eCollection 2020 Nov 1.
Abstract
Absorption and reduced scattering coefficients ( ) 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 ( ) for estimating articular cartilage composition and biomechanical properties using multivariate and machine learning techniques. The results suggest that μ could optimally estimate cartilage proteoglycan content in the superficial zone, in addition to its equilibrium modulus. While could effectively estimate the proteoglycan content of the middle and deep zones in addition to the instantaneous and dynamic moduli of articular cartilage.
摘要
生物组织的吸收系数和约化散射系数（ ）在生物医学应用中已显示出巨大潜力。因此，它们是表征组织完整性的有效参数，并提供有关生物组织健康状况的重要信息。本研究使用多变量和机器学习技术，探讨光学特性（ ）在估计关节软骨组成和生物力学特性方面的潜力。结果表明，除了平衡模量外，μ还能最佳地估计表层区域的软骨蛋白聚糖含量。而 除了能有效估计关节软骨的瞬时和动态模量外，还能有效估计中层和深层区域的蛋白聚糖含量。
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