Mid-infrared fiber spectroscopy for detection of cartilage degeneration in osteoarthritis (Conference Presentation)

Bibikova, Olga; Mironovich, Valentin; Usenov, Iskander; Feliksberger, Elena; Bocharnikov, Alexey; Surkova, Anastasia; Belikova, Valeria; Nippolainen, Ervin; Afara, Isaac O.; Haas, Julian; Virtanen, Vesa; Rieppo, Lassi; Brommer, Harold; Weeren, René van; Pleyer, Michael; Koeth, Johannes; Nägele, M.; Töyräs, Juha; Mizaikoff, Boris; Sakharova, Tatiana; Artyushenko, Viacheslav

doi:10.1117/12.2544893

Cited by 3 publications

(4 citation statements)

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“…This makes MIR spectroscopy ideal for detecting and characterizing biochemical changes in AC during injury, providing valuable information that is comparable to results obtained from methods such as DD. More so, the sensitivity of MIR spectroscopy to AC biomechanical properties is likely due to the structure-function coupling of its matrix, where changes in the biochemical composition and structure of the AC extracellular matrix are reflected in its biomechanical properties [24,27,46]. Thus, the features embedded in the MIR spectrum, which contains information on important physicochemical [46,47] and morphological properties of the tissue [48,49], are the likely reason for the capacity of MIR spectroscopy for discriminating normal from damaged AC, as well as estimating the tissue's composition and functional properties.…”

Section: Discussionmentioning

confidence: 99%

“…Optical methodologies, such as optical coherence tomography [19,20], near-infrared (NIR) [21,22], MIR [23,24], and Raman spectroscopy [25,26], have emerged as promising minimally invasive tools to assess the biochemical composition and quality of AC. Of these methods, MIR spectroscopy has been increasingly used due to its sensitivity to changes in collagen and PG content [27], two important components of the cartilage ECM that contribute to its mechanical properties [1].…”

Section: Introductionmentioning

confidence: 99%

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Infrared Spectroscopy Can Differentiate Between Cartilage Injury Models: Implication for Assessment of Cartilage Integrity

Shahini,

Oskouei,

Nippolainen

et al. 2024

Ann Biomed Eng

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In order to improve the ability of clinical diagnosis to differentiate articular cartilage (AC) injury of different origins, this study explores the sensitivity of mid-infrared (MIR) spectroscopy for detecting structural, compositional, and functional changes in AC resulting from two injury types. Three grooves (two in parallel in the palmar-dorsal direction and one in the mediolateral direction) were made via arthrotomy in the AC of the radial facet of the third carpal bone (middle carpal joint) and of the intermediate carpal bone (the radiocarpal joint) of nine healthy adult female Shetland ponies (age = 6.8 ± 2.6 years; range 4–13 years) using blunt and sharp tools. The defects were randomly assigned to each of the two joints. Ponies underwent a 3-week box rest followed by 8 weeks of treadmill training and 26 weeks of free pasture exercise before being euthanized for osteochondral sample collection. The osteochondral samples underwent biomechanical indentation testing, followed by MIR spectroscopic assessment. Digital densitometry was conducted afterward to estimate the tissue's proteoglycan (PG) content. Subsequently, machine learning models were developed to classify the samples to estimate their biomechanical properties and PG content based on the MIR spectra according to injury type. Results show that MIR is able to discriminate healthy from injured AC (91%) and between injury types (88%). The method can also estimate AC properties with relatively low error (thickness = 12.7% mm, equilibrium modulus = 10.7% MPa, instantaneous modulus = 11.8% MPa). These findings demonstrate the potential of MIR spectroscopy as a tool for assessment of AC integrity changes that result from injury.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Infrared Spectroscopy Can Differentiate Between Cartilage Injury Models: Implication for Assessment of Cartilage Integrity

Shahini,

Oskouei,

Nippolainen

et al. 2024

Ann Biomed Eng

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“…Similarly, near-infrared (NIR) spectroscopy (spectral range (4000–10,000 cm

) has been successfully used in diagnosis for different kinds of cartilage and bone disease, such as assessment of the matrix constituents of cartilage [ 17 , 18 ], in vivo arthroscopic monitoring of cartilage defects [ 19 ], and non-destructive evaluation of articular cartilage [ 20 , 21 , 22 ]. Furthermore, infrared fiber optic probes coupled with FTIR and NIR spectrometers have shown great success in clinical studies [ 11 , 23 , 24 , 25 , 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach

et al. 2022

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Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.

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“…Although chalcogenide glass optical fiber and polycrystalline optical fiber are commercially available as types of mid-infrared optical fiber, the latter is advantageous because it does not contain toxic material prohibited from being used in biomedical applications. Various proposals for spectroscopic systems using a QCL and AgCl/AgBr polycrystalline optical fiber have been made [ 6 , 7 ]. Moreover, because of its simpler structure, a hollow optical fiber is advantageous in terms of cost and durability as an optical transmission medium for mid-infrared spectroscopy systems.…”

Section: Introductionmentioning

confidence: 99%

A Compact Mid-Infrared Spectroscopy System for Healthcare Applications Based on a Wavelength-Swept, Pulsed Quantum Cascade Laser

Kõyama

Shibata

Kino

et al. 2020

Sensors

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A mid-infrared spectroscopic system using a high-speed wavelength-swept and pulsed quantum cascade laser (QCL) for healthcare applications such as blood glucose measurement is proposed. We developed an attenuated total reflection measurement system comprising the QCL with a micro-electromechanical system (MEMS)-scanning grating, hollow optical fibers, and InAsSb detector and tested its feasibility for healthcare applications. A continuous spectrum was obtained by integrating comb-shaped spectra, the timing of which was slightly shifted. As this method does not require complex calculations, absorption spectra are obtained in real-time. We found that the signal-to-noise ratio of the obtained spectrum had been improved by increasing the number of spectra that were integrated into the spectrum calculation. Accordingly, we succeeded in measuring the absorption spectrum of a 0.1% aqueous glucose solution. Furthermore, the absorption spectra of human lips were measured, and it was shown that estimation of blood glucose levels were possible using a model equation derived using a partial least squares regression analysis of the measured absorption spectra. The spectroscopic system based on the QCL with MEMS-scanning grating has the advantages of compactness and low cost over conventional Fourier transform infrared-based systems and common spectroscopic systems with a tunable QCL that has a relatively large, movable grating.

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Mid-infrared fiber spectroscopy for detection of cartilage degeneration in osteoarthritis (Conference Presentation)

Cited by 3 publications

References 0 publications

Infrared Spectroscopy Can Differentiate Between Cartilage Injury Models: Implication for Assessment of Cartilage Integrity

Infrared Spectroscopy Can Differentiate Between Cartilage Injury Models: Implication for Assessment of Cartilage Integrity

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach

A Compact Mid-Infrared Spectroscopy System for Healthcare Applications Based on a Wavelength-Swept, Pulsed Quantum Cascade Laser

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