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Scientific reports2018; 8(1); 13409; doi: 10.1038/s41598-018-31670-5

Arthroscopic near infrared spectroscopy enables simultaneous quantitative evaluation of articular cartilage and subchondral bone in vivo.

Abstract: Arthroscopic assessment of articular tissues is highly subjective and poorly reproducible. To ensure optimal patient care, quantitative techniques (e.g., near infrared spectroscopy (NIRS)) could substantially enhance arthroscopic diagnosis of initial signs of post-traumatic osteoarthritis (PTOA). Here, we demonstrate, for the first time, the potential of arthroscopic NIRS to simultaneously monitor progressive degeneration of cartilage and subchondral bone in vivo in Shetland ponies undergoing different experimental cartilage repair procedures. Osteochondral tissues adjacent to the repair sites were evaluated using an arthroscopic NIRS probe and significant (p < 0.05) degenerative changes were observed in the tissue properties when compared with tissues from healthy joints. Artificial neural networks (ANN) enabled reliable (ρ = 0.63-0.87, NMRSE = 8.5-17.2%, RPIQ = 1.93-3.03) estimation of articular cartilage biomechanical properties, subchondral bone plate thickness and bone mineral density (BMD), and subchondral trabecular bone thickness, bone volume fraction (BV), BMD, and structure model index (SMI) from in vitro spectral data. The trained ANNs also reliably predicted the properties of an independent in vitro test group (ρ = 0.54-0.91, NMRSE = 5.9-17.6%, RPIQ = 1.68-3.36). However, predictions based on arthroscopic NIR spectra were less reliable (ρ = 0.27-0.74, NMRSE = 14.5-24.0%, RPIQ = 1.35-1.70), possibly due to errors introduced during arthroscopic spectral acquisition. Adaptation of NIRS could address the limitations of conventional arthroscopy through quantitative assessment of lesion severity and extent, thereby enhancing detection of initial signs of PTOA. This would be of high clinical significance, for example, when conducting orthopaedic repair surgeries.
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Publication Date: 2018-09-07 PubMed ID: 30194446PubMed Central: PMC6128946DOI: 10.1038/s41598-018-31670-5Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The examined research brings forward a method to analyze the degradation of cartilage and subchondral bone in joints, which are signs of post-traumatic osteoarthritis, by using near infrared spectroscopy (NIRS) during arthroscopy. The technique seems capable of providing quantitative data about these tissues, enhancing detection of early signs of osteoarthritic changes, and could potentially improve outcomes in orthopedic surgery.

Introduction to the Research

  • The study was motivated by the limitations of the traditional method of arthroscopic assessment, a procedure that allows surgeons to visually inspect, diagnose, and repair problems inside a joint. The current method is subjective and has low reproducibility.
  • The researchers sought to improve this process with the addition of near-infrared spectroscopy (NIRS), a non-destructive technique that can determine the biochemical composition of a sample.

Methodology

  • In this experiment, Shetland ponies were used as a model to test how effective arthroscopic NIRS is at detecting degenerative changes in articular tissues, specifically the cartilage and subchondral bone, which are initial signs of post-traumatic osteoarthritis (PTOA).
  • The ponies underwent different types of experimental cartilage repair procedures. The tissues next to the repair sites were then evaluated using a NIR spectroscopic probe that was inserted arthroscopically.
  • Artificial neural networks (ANNs) were used for the evaluation and prediction of the results. ANNs are computing systems that mimic the neural networks in brains, enabling them to ‘learn’ from observational data.

Findings

  • Significant degenerative changes were found in the cartilage and subchondral bone tissues when compared to tissues from healthy joints.
  • The ANNs helped estimate various properties of articular cartilage, subchondral bone plate thickness and bone mineral density (BMD), and subchondral trabecular bone thickness, bone volume fraction (BV), BMD, and structure model index (SMI) from the in vitro spectral data.
  • The technique also demonstrated promising results when tested on an independent in vitro group.

Implications and Conclusions

  • While the results showed many positive outcomes, the predictions based on arthroscopic NIR spectra were less reliable due to possible errors during the procedure itself. This implies that there might need to be adjustments and improvements to the NIRS technique and its integration into arthroscopy.
  • The study suggests that the adaptation and utilization of NIRS in arthroscopic procedures could provide quantitative assessment of lesion severity and extent, improving the detection of initial signs of PTOA. This innovation could be particularly significant in conducting orthopedic repair surgeries and potentially improving patient outcomes.

Cite This Article

APA
Sarin JK, Te Moller NCR, Mancini IAD, Brommer H, Visser J, Malda J, van Weeren PR, Afara IO, Töyräs J. (2018). Arthroscopic near infrared spectroscopy enables simultaneous quantitative evaluation of articular cartilage and subchondral bone in vivo. Sci Rep, 8(1), 13409. https://doi.org/10.1038/s41598-018-31670-5

Publication

Scientific reports
ISSN: 2045-2322
NlmUniqueID: 101563288
Country: England
Language: English
Volume: 8
Issue: 1
Pages: 13409
PII: 13409

Researcher Affiliations

Sarin, Jaakko K
  • Department of Applied Physics, University of Eastern Finland, Kuopio, Finland. jaakko.sarin@uef.fi.
  • Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland. jaakko.sarin@uef.fi.
Te Moller, Nikae C R
  • Department of Equine Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Mancini, Irina A D
  • Department of Equine Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Brommer, Harold
  • Department of Equine Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Visser, Jetze
  • Department of Orthopaedics, University Medical Center Utrecht, Utrecht, The Netherlands.
Malda, Jos
  • Department of Equine Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
  • Department of Orthopaedics, University Medical Center Utrecht, Utrecht, The Netherlands.
van Weeren, P René
  • Department of Equine Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Afara, Isaac O
  • Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
Töyräs, Juha
  • Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
  • Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.

MeSH Terms

  • Animals
  • Arthroscopy / methods
  • Bone Density
  • Cancellous Bone / diagnostic imaging
  • Cancellous Bone / pathology
  • Cartilage, Articular / diagnostic imaging
  • Cartilage, Articular / pathology
  • Horses
  • Neural Networks, Computer
  • Osteochondrosis / diagnostic imaging
  • Osteochondrosis / pathology
  • Spectroscopy, Near-Infrared / methods

Grant Funding

  • 647426 / European Research Council

Conflict of Interest Statement

The authors declare no competing interests.

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