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Cook H, Crisford A, Bourdakos K, Dunlop D, Oreffo ROC, Mahajan S. Holistic vibrational spectromics assessment of human cartilage for osteoarthritis diagnosis. BIOMEDICAL OPTICS EXPRESS 2024; 15:4264-4280. [PMID: 39022535 PMCID: PMC11249685 DOI: 10.1364/boe.520171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 05/29/2024] [Accepted: 05/31/2024] [Indexed: 07/20/2024]
Abstract
Osteoarthritis (OA) is the most common degenerative joint disease, presented as wearing down of articular cartilage and resulting in pain and limited mobility for 1 in 10 adults in the UK [Osteoarthr. Cartil.28(6), 792 (2020)10.1016/j.joca.2020.03.004]. There is an unmet need for patient friendly paradigms for clinical assessment that do not use ionizing radiation (CT), exogenous contrast enhancing dyes (MRI), and biopsy. Hence, techniques that use non-destructive, near- and shortwave infrared light (NIR, SWIR) may be ideal for providing label-free, deep tissue interrogation. This study demonstrates multimodal "spectromics", low-level abstraction data fusion of non-destructive NIR Raman scattering spectroscopy and NIR-SWIR absorption spectroscopy, providing an enhanced, interpretable "fingerprint" for diagnosis of OA in human cartilage. This is proposed as method level innovation applicable to both arthro- or endoscopic (minimally invasive) or potential exoscopic (non-invasive) optical approaches. Samples were excised from femoral heads post hip arthroplasty from OA patients (n = 13) and age-matched control (osteoporosis) patients (n = 14). Under multivariate statistical analysis and supervised machine learning, tissue was classified to high precision: 100% segregation of tissue classes (using 10 principal components), and a classification accuracy of 95% (control) and 80% (OA), using the combined vibrational data. There was a marked performance improvement (5 to 6-fold for multivariate analysis) using the spectromics fingerprint compared to results obtained from solely Raman or NIR-SWIR data. Furthermore, clinically relevant tissue components were identified through discriminatory spectral features - spectromics biomarkers - allowing interpretable feedback from the enhanced fingerprint. In summary, spectromics provides comprehensive information for early OA detection and disease stratification, imperative for effective intervention in treating the degenerative onset disease for an aging demographic. This novel and elegant approach for data fusion is compatible with various NIR-SWIR optical devices that will allow deep non-destructive penetration.
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Affiliation(s)
- Hiroki Cook
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK
- Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK
| | - Anna Crisford
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK
- Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK
- Human Development Health, Faculty of Medicine, Southampton SO16 6YD, UK
| | - Konstantinos Bourdakos
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK
- Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK
| | - Douglas Dunlop
- University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
| | - Richard O. C. Oreffo
- Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK
- Human Development Health, Faculty of Medicine, Southampton SO16 6YD, UK
| | - Sumeet Mahajan
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK
- Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK
- Department of Biotechnology, Inland Norway University of Applied Sciences, N-2317 Hamar, Norway
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Berni M, Marchiori G, Baleani M, Giavaresi G, Lopomo NF. Biomechanics of the Human Osteochondral Unit: A Systematic Review. MATERIALS (BASEL, SWITZERLAND) 2024; 17:1698. [PMID: 38612211 PMCID: PMC11012636 DOI: 10.3390/ma17071698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/17/2024] [Accepted: 03/26/2024] [Indexed: 04/14/2024]
Abstract
The damping system ensured by the osteochondral (OC) unit is essential to deploy the forces generated within load-bearing joints during locomotion, allowing furthermore low-friction sliding motion between bone segments. The OC unit is a multi-layer structure including articular cartilage, as well as subchondral and trabecular bone. The interplay between the OC tissues is essential in maintaining the joint functionality; altered loading patterns can trigger biological processes that could lead to degenerative joint diseases like osteoarthritis. Currently, no effective treatments are available to avoid degeneration beyond tissues' recovery capabilities. A thorough comprehension on the mechanical behaviour of the OC unit is essential to (i) soundly elucidate its overall response to intra-articular loads for developing diagnostic tools capable of detecting non-physiological strain levels, (ii) properly evaluate the efficacy of innovative treatments in restoring physiological strain levels, and (iii) optimize regenerative medicine approaches as potential and less-invasive alternatives to arthroplasty when irreversible damage has occurred. Therefore, the leading aim of this review was to provide an overview of the state-of-the-art-up to 2022-about the mechanical behaviour of the OC unit. A systematic search is performed, according to PRISMA standards, by focusing on studies that experimentally assess the human lower-limb joints' OC tissues. A multi-criteria decision-making method is proposed to quantitatively evaluate eligible studies, in order to highlight only the insights retrieved through sound and robust approaches. This review revealed that studies on human lower limbs are focusing on the knee and articular cartilage, while hip and trabecular bone studies are declining, and the ankle and subchondral bone are poorly investigated. Compression and indentation are the most common experimental techniques studying the mechanical behaviour of the OC tissues, with indentation also being able to provide information at the micro- and nanoscales. While a certain comparability among studies was highlighted, none of the identified testing protocols are currently recognised as standard for any of the OC tissues. The fibril-network-reinforced poro-viscoelastic constitutive model has become common for describing the response of the articular cartilage, while the models describing the mechanical behaviour of mineralised tissues are usually simpler (i.e., linear elastic, elasto-plastic). Most advanced studies have tested and modelled multiple tissues of the same OC unit but have done so individually rather than through integrated approaches. Therefore, efforts should be made in simultaneously evaluating the comprehensive response of the OC unit to intra-articular loads and the interplay between the OC tissues. In this regard, a multidisciplinary approach combining complementary techniques, e.g., full-field imaging, mechanical testing, and computational approaches, should be implemented and validated. Furthermore, the next challenge entails transferring this assessment to a non-invasive approach, allowing its application in vivo, in order to increase its diagnostic and prognostic potential.
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Affiliation(s)
- Matteo Berni
- Laboratorio di Tecnologia Medica, IRCCS Istituto Ortopedico Rizzoli, Via di Barbiano 1/10, 40136 Bologna, Italy; (M.B.); (M.B.)
| | - Gregorio Marchiori
- Scienze e Tecnologie Chirurgiche, IRCCS Istituto Ortopedico Rizzoli, Via di Barbiano 1/10, 40136 Bologna, Italy;
| | - Massimiliano Baleani
- Laboratorio di Tecnologia Medica, IRCCS Istituto Ortopedico Rizzoli, Via di Barbiano 1/10, 40136 Bologna, Italy; (M.B.); (M.B.)
| | - Gianluca Giavaresi
- Scienze e Tecnologie Chirurgiche, IRCCS Istituto Ortopedico Rizzoli, Via di Barbiano 1/10, 40136 Bologna, Italy;
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Chen YC, Huang HP. Ultraviolet-Visible-Near Infrared Spectroscopy May Aid in the Qualitative Assessment of Early-Stage Cartilage Degradation. Arthrosc Sports Med Rehabil 2024; 6:100842. [PMID: 38414840 PMCID: PMC10897593 DOI: 10.1016/j.asmr.2023.100842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 11/07/2023] [Indexed: 02/29/2024] Open
Abstract
Purpose To assess the potential of ultraviolet-visible near-infrared spectroscopy to provide quantitative information on the cartilage surface at early osteoarthritis. Methods We used a similar source and optical path to a standard arthroscope and constraining input to the range available to a standard detector/camera, further capturing and analyzing spectral information quantitatively in terms of specific electronic absorbance bands and scattering from the cartilage surface, with a focus on the early stages of degradation. Results The ratio of the 320-nm and longer than 500-nm absorbances produced a distinct change from the normal to diseased states. The slopes between the wavelengths of 600 and 980 nm may show the transition of the single fibril to fibril bundles that occurs during early stages disease. Conclusions Ultraviolet-visible near-infrared spectroscopy has good potential for use in integrated arthroscopic assessment. Clinical Relevance This raises the possibility of advancing arthroscopy from a qualitative to a quantitative tool, without requiring modification of either the radiation (the light source and path) or instrumentation (the arthroscope itself) delivered to the patient, thus allowing a low-cost yet potentially high-value technology.
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Affiliation(s)
- Ying-chun Chen
- Botnar Research Centre, NDORMS, University of Oxford, Oxford, United Kingdom
- Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Hsing-Po Huang
- Department of Mechanical Engineering, National Taipei University of Technology. Taipei, Taiwan
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Rovnyagina NR, Budylin GS, Dyakonov PV, Efremov YM, Lipina MM, Goncharuk YR, Murdalov EE, Pogosyan DA, Davydov DA, Korneev AA, Serejnikova NB, Mikaelyan KA, Evlashin SA, Lazarev VA, Lychagin AV, Timashev PS, Shirshin EA. Grading cartilage damage with diffuse reflectance spectroscopy: Optical markers and mechanical properties. JOURNAL OF BIOPHOTONICS 2023; 16:e202200149. [PMID: 36066126 DOI: 10.1002/jbio.202200149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/01/2022] [Accepted: 08/29/2022] [Indexed: 06/15/2023]
Abstract
Osteoarthritis (OA) is one of the most common joint diseases worldwide. Unfortunately, clinical methods lack the ability to detect OA in the early stages. Timely detection of the knee joint degradation at the level of tissue changes can prevent its progressive damage. Here, diffuse reflectance spectroscopy (DRS) in the NIR range was used to obtain optical markers of the cartilage damage grades and to assess its mechanical properties. It was observed that the water content obtained by DRS strongly correlates with the cartilage thickness (R = .82) and viscoelastic relaxation time (R = .7). Moreover, the spectral parameters, including water content (OH-band), protein content (CH-band), and scattering parameters allowed for discrimination between the cartilage damage grades (10-4 < P ≤ 10-3 ). The developed approach may become a valuable addition to arthroscopy, helping to identify lesions at the microscopic level in the early stages of OA and complement the surgical analysis.
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Affiliation(s)
- Nataliya R Rovnyagina
- Laboratory of Clinical Biophotonics, Biomedical Science and Technology Park, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Gleb S Budylin
- Laboratory of Clinical Biophotonics, Biomedical Science and Technology Park, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Pavel V Dyakonov
- Laboratory of Clinical Biophotonics, Biomedical Science and Technology Park, Sechenov First Moscow State Medical University, Moscow, Russia
- Center for Materials Technologies, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Yuri M Efremov
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University, Moscow, Russia
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Marina M Lipina
- Department of Trauma, Orthopedics and Disaster Surgery, Sechenov First Moscow State Medical University, Moscow, Russia
- Laboratory of Clinical Smart Nanotechnologies, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Yuliya R Goncharuk
- Department of Trauma, Orthopedics and Disaster Surgery, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Emirkhan E Murdalov
- Department of Trauma, Orthopedics and Disaster Surgery, Sechenov First Moscow State Medical University, Moscow, Russia
| | - David A Pogosyan
- Department of Trauma, Orthopedics and Disaster Surgery, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Denis A Davydov
- Laboratory of Clinical Biophotonics, Biomedical Science and Technology Park, Sechenov First Moscow State Medical University, Moscow, Russia
- Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow, Russia
| | - Alexander A Korneev
- N.V. Sklifosovskiy Institute of Clinical Medicine, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Natalia B Serejnikova
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Karen A Mikaelyan
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Stanislav A Evlashin
- Center for Materials Technologies, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Vladimir A Lazarev
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University, Moscow, Russia
- Bauman Moscow State Technical University, Moscow, Russia
| | - Alexey V Lychagin
- Department of Trauma, Orthopedics and Disaster Surgery, Sechenov First Moscow State Medical University, Moscow, Russia
- Laboratory of Clinical Smart Nanotechnologies, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Peter S Timashev
- Institute for Regenerative Medicine, Sechenov First Moscow State Medical University, Moscow, Russia
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
- Chemistry Department, Lomonosov Moscow State University, Moscow, Russia
| | - Evgeny A Shirshin
- Laboratory of Clinical Biophotonics, Biomedical Science and Technology Park, Sechenov First Moscow State Medical University, Moscow, Russia
- Faculty of Physics, M.V. Lomonosov Moscow State University, Moscow, Russia
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Sarin JK, Prakash M, Shaikh R, Torniainen J, Joukainen A, Kröger H, Afara IO, Töyräs J. Near-Infrared Spectroscopy Enables Arthroscopic Histologic Grading of Human Knee Articular Cartilage. Arthrosc Sports Med Rehabil 2022; 4:e1767-e1775. [PMID: 36312728 PMCID: PMC9596902 DOI: 10.1016/j.asmr.2022.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 07/01/2022] [Indexed: 11/03/2022] Open
Abstract
Purpose To develop the means to estimate cartilage histologic grades and proteoglycan content in ex vivo arthroscopy using near-infrared spectroscopy (NIRS). Methods In this experimental study, arthroscopic NIR spectral measurements were performed on both knees of 9 human cadavers, followed by osteochondral block extraction and in vitro measurements: reacquisition of spectra and reference measurements (proteoglycan content, and three histologic scores). A hybrid model, combining principal component analysis and linear mixed-effects model (PCA-LME), was trained for each reference to investigate its relationship with in vitro NIR spectra. The performance of the PCA-LME model was validated with ex vivo spectra before and after the exclusion of outlying spectra. Model performance was evaluated based on Spearman rank correlation (ρ) and root-mean-square error (RMSE). Results The PCA-LME models performed well (independent test: average ρ = 0.668, RMSE = 0.892, P < .001) in the prediction of the reference measurements based on in vitro data. The performance on ex vivo arthroscopic data was poorer but improved substantially after outlier exclusion (independent test: average ρ = 0.462 to 0.614, RMSE = 1.078 to 0.950, P = .019 to .008). Conclusions NIRS is capable of nondestructive evaluation of cartilage integrity (i.e., histologic scores and proteoglycan content) under similar conditions as in clinical arthroscopy. Clinical Relevance There are clear clinical benefits to the accurate assessment of cartilage lesions in arthroscopy. Visual grading is the current standard of care. However, optical techniques, such as NIRS, may provide a more objective assessment of cartilage damage.
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Afara IO, Oloyede A. Resolving the Near-Infrared Spectrum of Articular Cartilage. Cartilage 2021; 13:729S-737S. [PMID: 34643470 PMCID: PMC8808936 DOI: 10.1177/19476035211035417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE Spectroscopic techniques, such as near-infrared (NIR) spectroscopy, are gaining significant research interest for characterizing connective tissues, particularly articular cartilage, because there is still a largely unmet need for rapid, accurate and objective methods for assessing tissue integrity in real-time during arthroscopic surgery. This study aims to identify the NIR spectral range that is optimal for characterizing cartilage integrity by (a) identifying the contribution of its major constituents (collagen and proteoglycans) to its overall spectrum using proxy constituent models and (b) determining constituent-specific spectral contributions that can be used for assessment of cartilage in its physiological state. DESIGN The NIR spectra of cartilage matrix constituent models were measured and compared with specific molecular components of organic compounds in the NIR spectral range in order to identify their bands and molecular assignments. To verify the identified bands, spectra of the model compounds were compared with those of native cartilage. Since water obscures some bands in the NIR range, spectral measurements of the native cartilage were conducted under conditions of decreasing water content to amplify features of the solid matrix components. The identified spectral bands were then compared and examined in the resulting spectra of the intact cartilage samples. RESULTS As water was progressively eliminated from cartilage, the specific contribution of the different matrix components was observed to correspond with those identified from the proxy cartilage component models. CONCLUSION Spectral peaks in the regions 5500 to 6250 cm-1 and 8100 to 8600 cm-1 were identified to be effective for characterizing cartilage proteoglycan and collagen contents, respectively.
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Affiliation(s)
- Isaac O. Afara
- Department of Applied Physics,
University of Eastern Finland, Kuopio, Finland
- School of Information Technology and
Electrical Engineering, The University of Queensland, Brisbane, Queensland,
Australia
| | - Adekunle Oloyede
- School of Chemistry, Physics, and
Mechanical Engineering, Science and Engineering Faculty, Queensland University of
Technology, Brisbane, Queensland, Australia
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Khan B, Kafian-Attari I, Nippolainen E, Shaikh R, Semenov D, Hauta-Kasari M, Töyräs J, Afara IO. Articular cartilage optical properties in the near-infrared (NIR) spectral range vary with depth and tissue integrity. BIOMEDICAL OPTICS EXPRESS 2021; 12:6066-6080. [PMID: 34745722 PMCID: PMC8548021 DOI: 10.1364/boe.430053] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/27/2021] [Accepted: 08/09/2021] [Indexed: 05/02/2023]
Abstract
Optical properties of biological tissues in the NIR spectral range have demonstrated significant potential for in vivo diagnostic applications and are critical parameters for modelling light interaction in biological tissues. This study aims to investigate the optical properties of articular cartilage as a function of tissue depth and integrity. The results suggest consistent wavelength-dependent variation in optical properties between cartilage depth-wise zones, as well as between healthy and degenerated tissue. Also, statistically significant differences (p<0.05) in both optical properties were observed between the different cartilage depth-wise zones and as a result of tissue degeneration. When taken into account, the outcome of this study could enable accurate modelling of light interaction in cartilage matrix and could provide useful diagnostic information on cartilage integrity.
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Affiliation(s)
- Bilour Khan
- University of Eastern Finland, Department of Applied Physics, Yliopistonranta 1, Kuopio, Finland, 70120, Finland
| | - Iman Kafian-Attari
- University of Eastern Finland, Department of Applied Physics, Yliopistonranta 1, Kuopio, Finland, 70120, Finland
| | - Ervin Nippolainen
- University of Eastern Finland, Department of Applied Physics, Yliopistonranta 1, Kuopio, Finland, 70120, Finland
| | - Rubina Shaikh
- University of Eastern Finland, Department of Applied Physics, Yliopistonranta 1, Kuopio, Finland, 70120, Finland
| | - Dmitry Semenov
- University of Eastern Finland, School of Computing, Lämsikatu 15, Joensuu, Finland, 80110, Finland
| | - Markku Hauta-Kasari
- University of Eastern Finland, School of Computing, Lämsikatu 15, Joensuu, Finland, 80110, Finland
| | - Juha Töyräs
- University of Eastern Finland, Department of Applied Physics, Yliopistonranta 1, Kuopio, Finland, 70120, Finland
- The University of Queensland, School of Information Technology, and Electrical Engineering, St. Lucia, Australia, QLD 4072, Australia
| | - Isaac O. Afara
- University of Eastern Finland, Department of Applied Physics, Yliopistonranta 1, Kuopio, Finland, 70120, Finland
- The University of Queensland, School of Information Technology, and Electrical Engineering, St. Lucia, Australia, QLD 4072, Australia
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Spectroscopic photoacoustic imaging of cartilage damage. Osteoarthritis Cartilage 2021; 29:1071-1080. [PMID: 33848681 DOI: 10.1016/j.joca.2021.04.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 03/10/2021] [Accepted: 04/05/2021] [Indexed: 02/02/2023]
Abstract
OBJECTIVE Osteoarthritis (OA) is a chronic joint disease characterized by progressive degradation of cartilage. It affects more than 10% of the people aged over 60 years-old worldwide with a rising prevalence due to the increasingly aging population. OA is a major source of pain, disability, and socioeconomic cost. Currently, the lack of effective diagnosis and affordable imaging options for early detection and monitoring of OA presents the clinic with many challenges. Spectroscopic Photoacoustic (sPA) imaging has the potential to reveal changes in cartilage composition with different degrees of damage, based on optical absorption contrast. DESIGN In this study, the capabilities of sPA imaging and its potential to characterize cartilage damage were explored. To this end, 15 pieces of cartilage samples from patients undergoing a total joint replacement were collected and were imaged ex vivo with sPA imaging at a wide optical spectral range (between 500 nm and 1,300 nm) to investigate the photoacoustic properties of cartilage tissue. All the PA spectra of the cartilage samples were analyzed and compared to the corresponding histological results. RESULTS The collagen related PA spectral changes were clearly visible in our imaging data and were related to different degrees of cartilage damage. The results are in good agreement with histology and the current gold standard, i.e., the Mankin score. CONCLUSIONS This study demonstrates the potential and possible clinical application of sPA imaging in OA.
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Sarin JK, Te Moller NCR, Mohammadi A, Prakash M, Torniainen J, Brommer H, Nippolainen E, Shaikh R, Mäkelä JTA, Korhonen RK, van Weeren PR, Afara IO, Töyräs J. Machine learning augmented near-infrared spectroscopy: In vivo follow-up of cartilage defects. Osteoarthritis Cartilage 2021; 29:423-432. [PMID: 33359249 DOI: 10.1016/j.joca.2020.12.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 11/06/2020] [Accepted: 12/11/2020] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To assess the potential of near-infrared spectroscopy (NIRS) for in vivo arthroscopic monitoring of cartilage defects. METHOD Sharp and blunt cartilage grooves were induced in the radiocarpal and intercarpal joints of Shetland ponies and monitored at baseline (0 weeks) and at three follow-up timepoints (11, 23, and 39 weeks) by measuring near-infrared spectra in vivo at and around the grooves. The animals were sacrificed after 39 weeks and the joints were harvested. Spectra were reacquired ex vivo to ensure reliability of in vivo measurements and for reference analyses. Additionally, cartilage thickness and instantaneous modulus were determined via computed tomography and mechanical testing, respectively. The relationship between the ex vivo spectra and cartilage reference properties was determined using convolutional neural network. RESULTS In an independent test set, the trained networks yielded significant correlations for cartilage thickness (ρ = 0.473) and instantaneous modulus (ρ = 0.498). These networks were used to predict the reference properties at baseline and at follow-up time points. In the radiocarpal joint, cartilage thickness increased significantly with both groove types after baseline and remained swollen. Additionally, at 39 weeks, a significant difference was observed in cartilage thickness between controls and sharp grooves. For the instantaneous modulus, a significant decrease was observed with both groove types in the radiocarpal joint from baseline to 23 and 39 weeks. CONCLUSION NIRS combined with machine learning enabled determination of cartilage properties in vivo, thereby providing longitudinal evaluation of post-intervention injury development. Additionally, radiocarpal joints were found more vulnerable to cartilage degeneration after damage than intercarpal joints.
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Affiliation(s)
- J K Sarin
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
| | - N C R Te Moller
- Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands.
| | - A Mohammadi
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - M Prakash
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
| | - J Torniainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
| | - H Brommer
- Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands.
| | - E Nippolainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - R Shaikh
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - J T A Mäkelä
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - R K Korhonen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - P R van Weeren
- Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands; Regenerative Medicine Utrecht, Utrecht, the Netherlands.
| | - I O Afara
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - J Töyräs
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia.
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Querido W, Kandel S, Pleshko N. Applications of Vibrational Spectroscopy for Analysis of Connective Tissues. Molecules 2021; 26:922. [PMID: 33572384 PMCID: PMC7916244 DOI: 10.3390/molecules26040922] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/30/2021] [Accepted: 02/04/2021] [Indexed: 02/07/2023] Open
Abstract
Advances in vibrational spectroscopy have propelled new insights into the molecular composition and structure of biological tissues. In this review, we discuss common modalities and techniques of vibrational spectroscopy, and present key examples to illustrate how they have been applied to enrich the assessment of connective tissues. In particular, we focus on applications of Fourier transform infrared (FTIR), near infrared (NIR) and Raman spectroscopy to assess cartilage and bone properties. We present strengths and limitations of each approach and discuss how the combination of spectrometers with microscopes (hyperspectral imaging) and fiber optic probes have greatly advanced their biomedical applications. We show how these modalities may be used to evaluate virtually any type of sample (ex vivo, in situ or in vivo) and how "spectral fingerprints" can be interpreted to quantify outcomes related to tissue composition and quality. We highlight the unparalleled advantage of vibrational spectroscopy as a label-free and often nondestructive approach to assess properties of the extracellular matrix (ECM) associated with normal, developing, aging, pathological and treated tissues. We believe this review will assist readers not only in better understanding applications of FTIR, NIR and Raman spectroscopy, but also in implementing these approaches for their own research projects.
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Affiliation(s)
| | | | - Nancy Pleshko
- Department of Bioengineering, Temple University, Philadelphia, PA 19122, USA; (W.Q.); (S.K.)
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Afara IO, Shaikh R, Nippolainen E, Querido W, Torniainen J, Sarin JK, Kandel S, Pleshko N, Töyräs J. Characterization of connective tissues using near-infrared spectroscopy and imaging. Nat Protoc 2021; 16:1297-1329. [PMID: 33462441 DOI: 10.1038/s41596-020-00468-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 11/20/2020] [Indexed: 02/06/2023]
Abstract
Near-infrared (NIR) spectroscopy is a powerful analytical method for rapid, non-destructive and label-free assessment of biological materials. Compared to mid-infrared spectroscopy, NIR spectroscopy excels in penetration depth, allowing intact biological tissue assessment, albeit at the cost of reduced molecular specificity. Furthermore, it is relatively safe compared to Raman spectroscopy, with no risk of laser-induced photothermal damage. A typical NIR spectroscopy workflow for biological tissue characterization involves sample preparation, spectral acquisition, pre-processing and analysis. The resulting spectrum embeds intrinsic information on the tissue's biomolecular, structural and functional properties. Here we demonstrate the analytical power of NIR spectroscopy for exploratory and diagnostic applications by providing instructions for acquiring NIR spectra, maps and images in biological tissues. By adapting and extending this protocol from the demonstrated application in connective tissues to other biological tissues, we expect that a typical NIR spectroscopic study can be performed by a non-specialist user to characterize biological tissues in basic research or clinical settings. We also describe how to use this protocol for exploratory study on connective tissues, including differentiating among ligament types, non-destructively monitoring changes in matrix formation during engineered cartilage development, mapping articular cartilage proteoglycan content across bovine patella and spectral imaging across the depth-wise zones of articular cartilage and subchondral bone. Depending on acquisition mode and experiment objectives, a typical exploratory study can be completed within 6 h, including sample preparation and data analysis.
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Affiliation(s)
- Isaac O Afara
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.
| | - Rubina Shaikh
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Centre, Kuopio University Hospital, Kuopio, Finland
| | - Ervin Nippolainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - William Querido
- Department of Bioengineering, Temple University, Philadelphia, PA, USA
| | - Jari Torniainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Jaakko K Sarin
- Diagnostic Imaging Centre, Kuopio University Hospital, Kuopio, Finland
| | - Shital Kandel
- Department of Bioengineering, Temple University, Philadelphia, PA, USA
| | - Nancy Pleshko
- Department of Bioengineering, Temple University, Philadelphia, PA, USA
| | - Juha Töyräs
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
- Diagnostic Imaging Centre, Kuopio University Hospital, Kuopio, Finland
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Near Infrared Spectroscopy Enables Differentiation of Mechanically and Enzymatically Induced Cartilage Injuries. Ann Biomed Eng 2020; 48:2343-2353. [PMID: 32300956 PMCID: PMC7452885 DOI: 10.1007/s10439-020-02506-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 04/02/2020] [Indexed: 01/29/2023]
Abstract
This study evaluates the feasibility of near infrared (NIR) spectroscopy to distinguish between different cartilage injury types associated with post-traumatic osteoarthritis and idiopathic osteoarthritis (OA) induced by mechanical and enzymatic damages. Bovine osteochondral samples (n = 72) were subjected to mechanical (n = 24) and enzymatic (n = 36) damage; NIR spectral measurements were acquired from each sample before and after damage, and from a separate control group (n = 12). Biomechanical measurements were then conducted to determine the functional integrity of the samples. NIR spectral variations resulting from different damage types were investigated and the samples classified using partial least squares discriminant analysis (PLS-DA). Partial least squares regression (PLSR) was then employed to investigate the relationship between the NIR spectra and biomechanical properties of the samples. Results of the study demonstrate that substantial spectral changes occur in the region of 1700–2200 nm due to tissue damages, while differences between enzymatically and mechanically induced damages can be observed mainly in the region of 1780–1810 nm. We conclude that NIR spectroscopy, combined with multivariate analysis, is capable of discriminating between cartilage injuries that mimic idiopathic OA and traumatic injuries based on specific spectral features. This information could be useful in determining the optimal treatment strategy during cartilage repair in arthroscopy.
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13
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Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy. Cell Mol Bioeng 2020; 13:219-228. [PMID: 32426059 PMCID: PMC7225230 DOI: 10.1007/s12195-020-00612-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Accepted: 02/18/2020] [Indexed: 12/15/2022] Open
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. Electronic supplementary material The online version of this article (10.1007/s12195-020-00612-5) contains supplementary material, which is available to authorized users.
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Karchner JP, Yousefi F, Bitman SR, Darvish K, Pleshko N. Non-Destructive Spectroscopic Assessment of High and Low Weight Bearing Articular Cartilage Correlates with Mechanical Properties. Cartilage 2019; 10:480-490. [PMID: 29690771 PMCID: PMC6755878 DOI: 10.1177/1947603518764269] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
OBJECTIVE Autologous articular cartilage (AC) harvested for repair procedures of high weight bearing (HWB) regions of the femoral condyles is typically obtained from low weight bearing (LWB) regions, in part due to the lack of non-destructive techniques for cartilage composition assessment. Here, we demonstrate that infrared fiber optic spectroscopy can be used to non-destructively evaluate variations in compositional and mechanical properties of AC across LWB and HWB regions. DESIGN AC plugs (N = 72) were harvested from the patellofemoral groove of juvenile bovine stifle joints, a LWB region, and femoral condyles, a HWB region. Near-infrared (NIR) and mid-infrared (MIR) fiber optic spectra were collected from plugs, and indentation tests were performed to determine the short-term and equilibrium moduli, followed by gravimetric water and biochemical analysis. RESULTS LWB tissues had a significantly greater amount of water determined by NIR and gravimetric assay. The moduli generally increased in tissues from the patellofemoral groove to the condyles, with HWB condyle cartilage having significantly higher moduli. A greater amount of proteoglycan content was also found in HWB tissues, but no differences in collagen content. In addition, NIR-determined water correlated with short-term modulus and proteoglycan content (R = -0.40 and -0.31, respectively), and a multivariate model with NIR data was able to predict short-term modulus within 15% error. CONCLUSIONS The properties of tissues from LWB regions differ from HWB tissues and can be determined non-destructively by infrared fiber optic spectroscopy. Clinicians may be able to use this modality to assess AC prior to harvesting osteochondral grafts for focal defect repair.
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Affiliation(s)
- James P. Karchner
- Department of Bioengineering, Temple University, Philadelphia, PA, USA
| | - Farzad Yousefi
- Department of Bioengineering, Temple University, Philadelphia, PA, USA
| | | | - Kurosh Darvish
- Department of Mechanical Engineering, Temple University, Philadelphia, PA, USA
| | - Nancy Pleshko
- Department of Bioengineering, Temple University, Philadelphia, PA, USA
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Mirbagheri M, Hakimi N, Ebrahimzadeh E, Pourrezaei K, Setarehdan SK. Enhancement of optical penetration depth of LED-based NIRS systems by comparing different beam profiles. Biomed Phys Eng Express 2019. [DOI: 10.1088/2057-1976/ab42d9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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16
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Prakash M, Joukainen A, Torniainen J, Honkanen MKM, Rieppo L, Afara IO, Kröger H, Töyräs J, Sarin JK. Near-infrared spectroscopy enables quantitative evaluation of human cartilage biomechanical properties during arthroscopy. Osteoarthritis Cartilage 2019; 27:1235-1243. [PMID: 31026649 DOI: 10.1016/j.joca.2019.04.008] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Revised: 02/11/2019] [Accepted: 04/09/2019] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To investigate the feasibility of near-infrared (NIR) spectroscopy (NIRS) for evaluation of human articular cartilage biomechanical properties during arthroscopy. DESIGN A novel arthroscopic NIRS probe designed in our research group was utilized by an experienced orthopedic surgeon to measure NIR spectra from articular cartilage of human cadaver knee joints (ex vivo, n = 18) at several measurement locations during an arthroscopic surgery. Osteochondral samples (n = 265) were extracted from the measurement sites for reference analysis. NIR spectra were remeasured in a controlled laboratory environment (in vitro), after which the corresponding cartilage thickness and biomechanical properties were determined. Hybrid multivariate regression models based on principal component analysis and linear mixed effects modeling (PCA-LME) were utilized to relate cartilage in vitro spectra and biomechanical properties, as well as to account for the spatial dependency. Additionally, a k-nearest neighbors (kNN) classifier was employed to reject outlying ex vivo NIR spectra resulting from a non-optimal probe-cartilage contact. Model performance was evaluated for both in vitro and ex vivo NIR spectra via Spearman's rank correlation (ρ) and the ratio of performance to interquartile range (RPIQ). RESULTS Regression models accurately predicted cartilage thickness and biomechanical properties from in vitro NIR spectra (Model: 0.77 ≤ ρ ≤ 0.87, 2.03 ≤ RPIQ ≤ 3.0; Validation: 0.74 ≤ ρ ≤ 0.84, 1.87 ≤ RPIQ ≤ 2.90). When predicting cartilage properties from ex vivo NIR spectra (0.33 ≤ ρ ≤ 0.57 and 1.02 ≤ RPIQ ≤ 2.14), a kNN classifier enhanced the accuracy of predictions (0.52 ≤ ρ ≤ 0.87 and 1.06 ≤ RPIQ ≤ 1.88). CONCLUSION Arthroscopic NIRS could substantially enhance identification of damaged cartilage by enabling quantitative evaluation of cartilage biomechanical properties. The results demonstrate the capacity of NIRS in clinical applications.
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Affiliation(s)
- M Prakash
- Department of Applied Physics, University of Eastern Finland, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
| | - A Joukainen
- Department of Orthopedics, Traumatology and Hand Surgery, Kuopio University Hospital, Kuopio, Finland.
| | - J Torniainen
- Department of Applied Physics, University of Eastern Finland, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
| | - M K M Honkanen
- Department of Applied Physics, University of Eastern Finland, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
| | - L Rieppo
- Department of Applied Physics, University of Eastern Finland, Finland; Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.
| | - I O Afara
- Department of Applied Physics, University of Eastern Finland, Finland.
| | - H Kröger
- Department of Orthopedics, Traumatology and Hand Surgery, Kuopio University Hospital, Kuopio, Finland.
| | - J Töyräs
- Department of Applied Physics, University of Eastern Finland, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia.
| | - J K Sarin
- Department of Applied Physics, University of Eastern Finland, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
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Arthroscopic Determination of Cartilage Proteoglycan Content and Collagen Network Structure with Near-Infrared Spectroscopy. Ann Biomed Eng 2019; 47:1815-1826. [PMID: 31062256 PMCID: PMC6647474 DOI: 10.1007/s10439-019-02280-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 04/24/2019] [Indexed: 11/24/2022]
Abstract
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.
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18
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Arthroscopic near infrared spectroscopy enables simultaneous quantitative evaluation of articular cartilage and subchondral bone in vivo. Sci Rep 2018; 8:13409. [PMID: 30194446 PMCID: PMC6128946 DOI: 10.1038/s41598-018-31670-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 08/23/2018] [Indexed: 01/24/2023] Open
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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Karchner JP, Querido W, Kandel S, Pleshko N. Spatial correlation of native and engineered cartilage components at micron resolution. Ann N Y Acad Sci 2018; 1442:104-117. [PMID: 30058180 DOI: 10.1111/nyas.13934] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Revised: 06/20/2018] [Accepted: 06/27/2018] [Indexed: 02/06/2023]
Abstract
Tissue engineering (TE) approaches are being widely investigated for repair of focal defects in articular cartilage. However, the amount and/or type of extracellular matrix (ECM) produced in engineered constructs does not always correlate with the resultant mechanical properties. This could be related to the specifics of ECM distribution throughout the construct. Here, we present data on the amount and distribution of the primary components of native and engineered cartilage (i.e., collagen, proteoglycan (PG), and water) using Fourier transform infrared imaging spectroscopy (FT-IRIS). These data permit visualization of matrix and water at 25 μm resolution throughout the tissues, and subsequent colocalization of these components using image processing methods. Native and engineered cartilage were cryosectioned at 80 μm for evaluation by FT-IRIS in the mid-infrared (MIR) and near-infrared (NIR) regions. PG distribution correlated strongly with water in native and engineered cartilage, supporting the binding of water to PG in both tissues. In addition, NIR-derived matrix peaks correlated significantly with MIR-derived collagen peaks, confirming the interpretation that these absorbances arise primarily from collagen and not PG. The combined use of MIR and NIR permits assessment of ECM and water spatial distribution at the micron level, which may aid in improved development of TE techniques.
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Affiliation(s)
- James P Karchner
- Department of Bioengineering, Temple University, Philadelphia, Pennsylvania
| | - William Querido
- Department of Bioengineering, Temple University, Philadelphia, Pennsylvania
| | - Shital Kandel
- Department of Bioengineering, Temple University, Philadelphia, Pennsylvania
| | - Nancy Pleshko
- Department of Bioengineering, Temple University, Philadelphia, Pennsylvania
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Characterizing human subchondral bone properties using near-infrared (NIR) spectroscopy. Sci Rep 2018; 8:9733. [PMID: 29950563 PMCID: PMC6021410 DOI: 10.1038/s41598-018-27786-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 06/06/2018] [Indexed: 12/16/2022] Open
Abstract
Degenerative joint conditions are often characterized by changes in articular cartilage and subchondral bone properties. These changes are often associated with subchondral plate thickness and trabecular bone morphology. Thus, evaluating subchondral bone integrity could provide essential insights for diagnosis of joint pathologies. This study investigates the potential of optical spectroscopy for characterizing human subchondral bone properties. Osteochondral samples (n = 50) were extracted from human cadaver knees (n = 13) at four anatomical locations and subjected to NIR spectroscopy. The samples were then imaged using micro-computed tomography to determine subchondral bone morphometric properties, including: plate thickness (Sb.Th), trabecular thickness (Tb.Th), volume fraction (BV/TV), and structure model index (SMI). The relationship between the subchondral bone properties and spectral data in the 1st (650–950 nm), 2nd (1100–1350 nm) and 3rd (1600–1870 nm) optical windows were investigated using partial least squares (PLS) regression multivariate technique. Significant correlations (p < 0.0001) and relatively low prediction errors were obtained between spectral data in the 1st optical window and Sb.Th (R2 = 92.3%, error = 7.1%), Tb.Th (R2 = 88.4%, error = 6.7%), BV/TV (R2 = 83%, error = 9.8%) and SMI (R2 = 79.7%, error = 10.8%). Thus, NIR spectroscopy in the 1st tissue optical window is capable of characterizing and estimating subchondral bone properties, and can potentially be adapted during arthroscopy.
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Ala-Myllymäki J, Danso EK, Honkanen JTJ, Korhonen RK, Töyräs J, Afara IO. Optical spectroscopic characterization of human meniscus biomechanical properties. JOURNAL OF BIOMEDICAL OPTICS 2017; 22:1-10. [PMID: 29275548 DOI: 10.1117/1.jbo.22.12.125008] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Accepted: 11/27/2017] [Indexed: 06/07/2023]
Abstract
This study investigates the capacity of optical spectroscopy in the visible (VIS) and near-infrared (NIR) spectral ranges for estimating the biomechanical properties of human meniscus. Seventy-two samples obtained from the anterior, central, and posterior locations of the medial and lateral menisci of 12 human cadaver joints were used. The samples were subjected to mechanical indentation, then traditional biomechanical parameters (equilibrium and dynamic moduli) were calculated. In addition, strain-dependent fibril network modulus and permeability strain-dependency coefficient were determined via finite-element modeling. Subsequently, absorption spectra were acquired from each location in the VIS (400 to 750 nm) and NIR (750 to 1100 nm) spectral ranges. Partial least squares regression, combined with spectral preprocessing and transformation, was then used to investigate the relationship between the biomechanical properties and spectral response. The NIR spectral region was observed to be optimal for model development (83.0%≤R2≤90.8%). The percentage error of the models are: Eeq (7.1%), Edyn (9.6%), Eϵ (8.4%), and Mk (8.9%). Thus, we conclude that optical spectroscopy in the NIR range is a potential method for rapid and nondestructive evaluation of human meniscus functional integrity and health in real time during arthroscopic surgery.
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Affiliation(s)
- Juho Ala-Myllymäki
- University of Eastern Finland, Department of Applied Physics, Kuopio, Finland
- Kuopio University Hospital, Diagnostic Imaging Center, Kuopio, Finland
| | - Elvis K Danso
- Colorado State University, Department of Mechanical Engineering, Fort Collins, Colorado, United States
| | | | - Rami K Korhonen
- University of Eastern Finland, Department of Applied Physics, Kuopio, Finland
- Kuopio University Hospital, Diagnostic Imaging Center, Kuopio, Finland
| | - Juha Töyräs
- University of Eastern Finland, Department of Applied Physics, Kuopio, Finland
- Kuopio University Hospital, Diagnostic Imaging Center, Kuopio, Finland
| | - Isaac O Afara
- University of Eastern Finland, Department of Applied Physics, Kuopio, Finland
- Kuopio University Hospital, Diagnostic Imaging Center, Kuopio, Finland
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Prakash M, Sarin JK, Rieppo L, Afara IO, Töyräs J. Optimal Regression Method for Near-Infrared Spectroscopic Evaluation of Articular Cartilage. APPLIED SPECTROSCOPY 2017; 71:2253-2262. [PMID: 28753034 DOI: 10.1177/0003702817726766] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Near-infrared (NIR) spectroscopy has been successful in nondestructive assessment of biological tissue properties, such as stiffness of articular cartilage, and is proposed to be used in clinical arthroscopies. Near-infrared spectroscopic data include absorbance values from a broad wavelength region resulting in a large number of contributing factors. This broad spectrum includes information from potentially noisy variables, which may contribute to errors during regression analysis. We hypothesized that partial least squares regression (PLSR) is an optimal multivariate regression technique and requires application of variable selection methods to further improve the performance of NIR spectroscopy-based prediction of cartilage tissue properties, including instantaneous, equilibrium, and dynamic moduli and cartilage thickness. To test this hypothesis, we conducted for the first time a comparative analysis of multivariate regression techniques, which included principal component regression (PCR), PLSR, ridge regression, least absolute shrinkage and selection operator (Lasso), and least squares version of support vector machines (LS-SVM) on NIR spectral data of equine articular cartilage. Additionally, we evaluated the effect of variable selection methods, including Monte Carlo uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), variable combination population analysis (VCPA), backward interval PLS (BiPLS), genetic algorithm (GA), and jackknife, on the performance of the optimal regression technique. The PLSR technique was found as an optimal regression tool (R2Tissue thickness = 75.6%, R2Dynamic modulus = 64.9%) for cartilage NIR data; variable selection methods simplified the prediction models enabling the use of lesser number of regression components. However, the improvements in model performance with variable selection methods were found to be statistically insignificant. Thus, the PLSR technique is recommended as the regression tool for multivariate analysis for prediction of articular cartilage properties from its NIR spectra.
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Affiliation(s)
- Mithilesh Prakash
- 1 Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Jaakko K Sarin
- 1 Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- 2 Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Lassi Rieppo
- 1 Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- 3 Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Isaac O Afara
- 1 Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- 2 Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Juha Töyräs
- 1 Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- 2 Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
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Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy. Sci Rep 2017; 7:11463. [PMID: 28904358 PMCID: PMC5597588 DOI: 10.1038/s41598-017-11844-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2017] [Accepted: 08/29/2017] [Indexed: 02/07/2023] Open
Abstract
We demonstrate in this study the potential of near infrared (NIR) spectroscopy as a tool for monitoring progression of cartilage degeneration in an animal model. Osteoarthritic degeneration was artificially induced in one joint in laboratory rats, and the animals were sacrificed at four time points: 1, 2, 4, and 6 weeks (3 animals/week). NIR spectra were acquired from both (injured and intact) knees. Subsequently, the joint samples were subjected to histological evaluation and glycosaminoglycan (GAG) content analysis, to assess disease severity based on the Mankin scoring system and to determine proteoglycan loss, respectively. Multivariate spectral techniques were then employed for classification (principal component analysis and support vector machines) and prediction (partial least squares regression) of the samples’ Mankin scores and GAG content from their NIR spectra. Our results demonstrate that NIR spectroscopy is sensitive to degenerative changes in articular cartilage, and is capable of distinguishing between mild (weeks 1&2; Mankin <=2) and advanced (weeks 4&6; Mankin =>3) cartilage degeneration. In addition, the spectral data contains information that enables estimation of the tissue’s Mankin score (error = 12.6%, R2 = 86.2%) and GAG content (error = 7.6%, R2 = 95%). We conclude that NIR spectroscopy is a viable tool for assessing cartilage degeneration post-injury, such as, post-traumatic osteoarthritis.
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Sarin JK, Rieppo L, Brommer H, Afara IO, Saarakkala S, Töyräs J. Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure. Sci Rep 2017; 7:10586. [PMID: 28878384 PMCID: PMC5587743 DOI: 10.1038/s41598-017-10973-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Accepted: 08/17/2017] [Indexed: 01/28/2023] Open
Abstract
Conventional arthroscopic evaluation of articular cartilage is subjective and poorly reproducible. Therefore, implementation of quantitative diagnostic techniques, such as near infrared spectroscopy (NIRS) and optical coherence tomography (OCT), is essential. Locations (n = 44) with various cartilage conditions were selected from mature equine fetlock joints (n = 5). These locations and their surroundings were measured with NIRS and OCT (n = 530). As a reference, cartilage proteoglycan (PG) and collagen contents, and collagen network organization were determined using quantitative microscopy. Additionally, lesion severity visualized in OCT images was graded with an automatic algorithm according to International Cartilage Research Society (ICRS) scoring system. Artificial neural network with variable selection was then employed to predict cartilage composition in the superficial and deep zones from NIRS data, and the performance of two models, generalized (including all samples) and condition-specific models (based on ICRS-grades), was compared. Spectral data correlated significantly (p < 0.002) with PG and collagen contents, and collagen orientation in the superficial and deep zones. The combination of NIRS and OCT provided the most reliable outcome, with condition-specific models having lower prediction errors (9.2%) compared to generalized models (10.4%). Therefore, the results highlight the potential of combining both modalities for comprehensive evaluation of cartilage during arthroscopy.
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Affiliation(s)
- Jaakko K Sarin
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
| | - Lassi Rieppo
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Harold Brommer
- Department of Equine Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
| | - Isaac O Afara
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Simo Saarakkala
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
- Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
- Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Juha Töyräs
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
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Sarin JK, Brommer H, Argüelles D, Puhakka PH, Inkinen SI, Afara IO, Saarakkala S, Töyräs J. Multimodality scoring of chondral injuries in the equine fetlock joint ex vivo. Osteoarthritis Cartilage 2017; 25:790-798. [PMID: 27965140 DOI: 10.1016/j.joca.2016.12.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2016] [Revised: 12/01/2016] [Accepted: 12/01/2016] [Indexed: 02/02/2023]
Abstract
OBJECTIVE We investigate the potential of a prototype multimodality arthroscope, combining ultrasound, optical coherence tomography (OCT) and arthroscopic indentation device, for assessing cartilage lesions, and compare the reliability of this approach with conventional arthroscopic scoring ex vivo. DESIGN Areas of interest (AIs, N = 43) were selected from equine fetlock joints (N = 5). Blind-coded AIs were independently scored by two equine surgeons employing International Cartilage Repair Society (ICRS) scoring system via conventional arthroscope and multimodality arthroscope, in which high-frequency ultrasound and OCT catheters were attached to an arthroscopic indentation device. In addition, cartilage stiffness was measured with the indentation device, and lesions in OCT images scored using custom-made automated software. Measurements and scorings were performed twice in two separate rounds. Finally, the scores were compared to histological ICRS scores. RESULTS OCT and arthroscopic examinations showed the highest average agreements (55.2%) between the scoring by surgeons and histology scores, whereas ultrasound had the lowest (50.6%). Average intraobserver agreements of surgeons and interobserver agreements between rounds were, respectively, for conventional arthroscope (68.6%, 69.8%), ultrasound (68.6%, 68.6%), OCT (65.1%, 61.7%) and automated software (65.1%, 59.3%). CONCLUSIONS OCT imaging supplemented with the automated software provided the most reliable lesion scoring. However, limited penetration depth of light limits the clinical potential of OCT in assessing human cartilage thickness; thus, the combination of OCT and ultrasound could be optimal for reliable diagnostics. Present findings suggest imaging and quantitatively analyzing the entire articular surface to eliminate surgeon-related variation in the selection of the most severe lesion to be scored.
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Affiliation(s)
- J K Sarin
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
| | - H Brommer
- Department of Equine Sciences, Utrecht University, Utrecht, The Netherlands.
| | - D Argüelles
- Veterinary Teaching Hospital, School of Veterinary Medicine, University of Helsinki, Helsinki, Finland.
| | - P H Puhakka
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
| | - S I Inkinen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - I O Afara
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; Department of Electrical and Computer Engineering, Elizade University, Ondo, Nigeria.
| | - S Saarakkala
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland; Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland.
| | - J Töyräs
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
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Near Infrared Spectroscopic Mapping of Functional Properties of Equine Articular Cartilage. Ann Biomed Eng 2016; 44:3335-3345. [PMID: 27234817 DOI: 10.1007/s10439-016-1659-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Accepted: 05/20/2016] [Indexed: 12/13/2022]
Abstract
Mechanical properties of articular cartilage are vital for normal joint function, which can be severely compromised by injuries. Quantitative characterization of cartilage injuries, and evaluation of cartilage stiffness and thickness by means of conventional arthroscopy is poorly reproducible or impossible. In this study, we demonstrate the potential of near infrared (NIR) spectroscopy for predicting and mapping the functional properties of equine articular cartilage at and around lesion sites. Lesion and non-lesion areas of interests (AI, N = 44) of equine joints (N = 5) were divided into grids and NIR spectra were acquired from all grid points (N = 869). Partial least squares (PLS) regression was used to investigate the correlation between the absorbance spectra and thickness, equilibrium modulus, dynamic modulus, and instantaneous modulus at the grid points of 41 AIs. Subsequently, the developed PLS models were validated with spectral data from the grid points of 3 independent AIs. Significant correlations were obtained between spectral data and cartilage thickness (R 2 = 70.3%, p < 0.0001), equilibrium modulus (R 2 = 67.8%, p < 0.0001), dynamic modulus (R 2 = 68.9%, p < 0.0001) and instantaneous modulus (R 2 = 41.8%, p < 0.0001). Relatively low errors were observed in the predicted thickness (5.9%) and instantaneous modulus (9.0%) maps. Thus, if well implemented, NIR spectroscopy could enable arthroscopic evaluation and mapping of cartilage functional properties at and around lesion sites.
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