Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy.

ABSTRACT:

Introduction

Assessment of cartilage integrity during arthroscopy is limited by the subjective visual nature of the technique. To address this shortcoming in diagnostic evaluation of articular cartilage, near infrared spectroscopy (NIRS) has been proposed. In this study, we evaluated the capacity of NIRS, combined with machine learning techniques, to classify cartilage integrity.

Methods

Rabbit (n = 14) knee joints with artificial injury, induced via unilateral anterior cruciate ligament transection (ACLT), and the corresponding contra-lateral (CL) joints, including joints from separate non-operated control (CNTRL) animals (n = 8), were used. After sacrifice, NIR spectra (1000-2500 nm) were acquired from different anatomical locations of the joints (n

SUBMITTER: Afara IO 

PROVIDER: S-EPMC7225230 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature