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
|Author:||Afara, Isaac O.1; Sarin, Jaakko K.1,2; Ojanen, Simo1,3;|
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
|Online Access:||PDF Full Text (PDF, 3.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020070246722
|Publish Date:|| 2020-07-02
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.
Cellular and molecular bioengineering
|Pages:||219 - 228|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3126 Surgery, anesthesiology, intensive care, radiology
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 (Academy of Finland Funding decision)
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