TY - JOUR
T1 - Arthroscopic Determination of Cartilage Proteoglycan Content and Collagen Network Structure with Near-Infrared Spectroscopy
AU - Sarin, Jaakko K
AU - Nykänen, Olli
AU - Tiitu, Virpi
AU - Mancini, Irina A D
AU - Brommer, Harold
AU - Visser, Jetze
AU - Malda, Jos
AU - van Weeren, P René
AU - Afara, Isaac O
AU - Töyräs, Juha
N1 - Funding Information:
Open access funding provided by University of Eastern Finland (UEF) and Kuopio University Hospital. The Doctoral Programme in Science, Technology and Computing (SCITECO) of University of Eastern Finland, Kuopio University Hospital (VTR Projects 5041750 and 5041744, PY210 Clinical Neurophysiology), the Academy of Finland (Project 267551), the Orion Research Foundation sr, and the Finnish Foundation of Technology Promotion financially supported this study. The European Community’s Seventh Framework Programme (FP7/2007–2013) under Grant Agreement 309962 (HydroZONES), and the Dutch Arthritis Society (LLP-12 and LLP-22) supported this research. Nikae CR te Moller is acknowledged for assistance during arthroscopic measurements.
Funding Information:
Open access funding provided by University of Eastern Finland (UEF) and Kuopio University Hospital. The Doctoral Programme in Science, Technology and Computing (SCITECO) of University of Eastern Finland, Kuopio University Hospital (VTR Projects 5041750 and 5041744, PY210 Clinical Neurophysiology), the Academy of Finland (Project 267551), the Orion Research Foundation sr, and the Finnish Foundation of Technology Promotion financially supported this study. The European Community?s Seventh Framework Programme (FP7/2007?2013) under Grant Agreement 309962 (HydroZONES), and the Dutch Arthritis Society (LLP-12 and LLP-22) supported this research. Nikae CR te Moller is acknowledged for assistance during arthroscopic measurements. The authors have no conflicts of interest regarding the study or preparation of the manuscript.
Publisher Copyright:
© 2019, The Author(s).
PY - 2019/8
Y1 - 2019/8
N2 - Conventional arthroscopic evaluation of articular cartilage is subjective and insufficient for assessing early compositional and structural changes during the progression of post-traumatic osteoarthritis. Therefore, in this study, arthroscopic near-infrared (NIR) spectroscopy is introduced, for the first time, for in vivo evaluation of articular cartilage thickness, proteoglycan (PG) content, and collagen orientation angle. NIR spectra were acquired in vivo and in vitro from equine cartilage adjacent to experimental cartilage repair sites. As reference, digital densitometry and polarized light microscopy were used to evaluate superficial and full-thickness PG content and collagen orientation angle. To relate NIR spectra and cartilage properties, ensemble neural networks, each with two different architectures, were trained and evaluated by using Spearman's correlation analysis (ρ). The ensemble networks enabled accurate predictions for full-thickness reference properties (PG content: ρin vitro, Val= 0.691, ρin vivo= 0.676; collagen orientation angle: ρin vitro, Val= 0.626, ρin vivo= 0.574) from NIR spectral data. In addition, the networks enabled reliable prediction of PG content in superficial (25%) cartilage (ρin vitro, Val= 0.650, ρin vivo= 0.613) and cartilage thickness (ρin vitro, Val= 0.797, ρin vivo= 0.596). To conclude, NIR spectroscopy could enhance the detection of initial cartilage degeneration and thus enable demarcation of the boundary between healthy and compromised cartilage tissue during arthroscopic surgery.
AB - Conventional arthroscopic evaluation of articular cartilage is subjective and insufficient for assessing early compositional and structural changes during the progression of post-traumatic osteoarthritis. Therefore, in this study, arthroscopic near-infrared (NIR) spectroscopy is introduced, for the first time, for in vivo evaluation of articular cartilage thickness, proteoglycan (PG) content, and collagen orientation angle. NIR spectra were acquired in vivo and in vitro from equine cartilage adjacent to experimental cartilage repair sites. As reference, digital densitometry and polarized light microscopy were used to evaluate superficial and full-thickness PG content and collagen orientation angle. To relate NIR spectra and cartilage properties, ensemble neural networks, each with two different architectures, were trained and evaluated by using Spearman's correlation analysis (ρ). The ensemble networks enabled accurate predictions for full-thickness reference properties (PG content: ρin vitro, Val= 0.691, ρin vivo= 0.676; collagen orientation angle: ρin vitro, Val= 0.626, ρin vivo= 0.574) from NIR spectral data. In addition, the networks enabled reliable prediction of PG content in superficial (25%) cartilage (ρin vitro, Val= 0.650, ρin vivo= 0.613) and cartilage thickness (ρin vitro, Val= 0.797, ρin vivo= 0.596). To conclude, NIR spectroscopy could enhance the detection of initial cartilage degeneration and thus enable demarcation of the boundary between healthy and compromised cartilage tissue during arthroscopic surgery.
KW - Arthroscopy
KW - Deep learning
KW - Equine
KW - Mosaicplasty
KW - Neural networks
KW - Osteoarthritis
KW - Post-traumatic osteoarthritis
KW - Neural Networks, Computer
KW - Cartilage, Articular/chemistry
KW - Male
KW - Deep Learning
KW - Animals
KW - Proteoglycans/analysis
KW - Spectroscopy, Near-Infrared
KW - Horses
KW - Female
KW - Collagen/chemistry
UR - http://www.scopus.com/inward/record.url?scp=85065424622&partnerID=8YFLogxK
U2 - 10.1007/s10439-019-02280-7
DO - 10.1007/s10439-019-02280-7
M3 - Article
C2 - 31062256
SN - 0090-6964
VL - 47
SP - 1815
EP - 1826
JO - Annals of Biomedical Engineering
JF - Annals of Biomedical Engineering
IS - 8
ER -