University of Oulu

Afara, I.O., Sarin, J.K., Ojanen, S. et al. Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy. Cel. Mol. Bioeng. 13, 219–228 (2020). https://doi.org/10.1007/s12195-020-00612-5

Machine learning classification of articular cartilage integrity using near infrared spectroscopy

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Author: Afara, Isaac O.1; Sarin, Jaakko K.1,2; Ojanen, Simo1,3;
Organizations: 1Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
2Diagnostic Imaging Centre, Kuopio University Hospital, Kuopio, Finland
3Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
4Human Performance Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Canada
5Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
6School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 3.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020070246722
Language: English
Published: Springer Nature, 2020
Publish Date: 2020-07-02
Description:

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 (nTOTAL = 313: nCNTRL = 111, nCL = 97, nACLT = 105). Machine and deep learning methods (support vector machines–SVM, logistic regression–LR, and deep neural networks–DNN) were then used to develop models for classifying the samples based solely on their NIR spectra.

Results: The results show that the model based on SVM is optimal of distinguishing between ACLT and CNTRL samples (ROC_AUC = 0.93, kappa = 0.86), LR is capable of distinguishing between CL and CNTRL samples (ROC_AUC = 0.91, kappa = 0.81), while DNN is optimal for discriminating between the different classes (multi-class classification, kappa = 0.48).

Conclusion: We show that NIR spectroscopy, when combined with machine learning techniques, is capable of holistic assessment of cartilage integrity, with potential for accurately distinguishing between healthy and diseased cartilage.

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Series: Cellular and molecular bioengineering
ISSN: 1865-5025
ISSN-E: 1865-5033
ISSN-L: 1865-5025
Volume: 13
Issue: 3
Pages: 219 - 228
DOI: 10.1007/s12195-020-00612-5
OADOI: https://oadoi.org/10.1007/s12195-020-00612-5
Type of Publication: A1 Journal article – refereed
Field of Science: 3126 Surgery, anesthesiology, intensive care, radiology
Subjects:
Funding: Open access funding provided by University of Eastern Finland (UEF) including Kuopio University Hospital. Dr. Afara acknowledges funding support from the Finnish Cultural Foundation (Suomen Kulttuurirahasto: 00160079 and 00171194) and Academy of Finland (Project 315820). This study was also supported by Academy of Finland projects of Professor Töyräs (267551), Professor Korhonen (286526, 324529), and Professor Saarakkala (303786). State research funding (Kuopio University Hospital VTR Projects 5041750 and 5041744: Professor Töyräs) and Sigrid Juselius Foundation (Professor Korhonen) are also acknowledged. Professor Herzog acknowledges the Canadian Institutes of Health Research, the Killam Foundation and the Canada Research Chair Program. Dr Finnilä acknowledges strategic funding from the University of Eastern Finland. Mr Ojanen acknowledges funding from Saastamoinen Foundation, Päivikki and Sakari Sohlberg Foundation, and Finnish Cultural Foundation (North Savo Regional Fund).
Academy of Finland Grant Number: 303786
Detailed Information: 303786 (Academy of Finland Funding decision)
Copyright information: © 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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