Machine learning classification of articular cartilage integrity using near infrared spectroscopy
Afara, Isaac O.; Sarin, Jaakko K.; Ojanen, Simo; Finnilä, Mikko A. J.; Herzog, Walter; Saarakkala, Simo; Korhonen, Rami K.; Töyräs, Juha (2020-03-09)
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
© 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/.
https://creativecommons.org/licenses/by/4.0/
https://urn.fi/URN:NBN:fi-fe2020070246722
Tiivistelmä
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.
Kokoelmat
- Avoin saatavuus [32041]