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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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Shehata E, Nippolainen E, Shaikh R, Ronkainen AP, Töyräs J, Sarin JK, Afara IO. Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties. Ann Biomed Eng 2023; 51:2301-2312. [PMID: 37328704 PMCID: PMC10518284 DOI: 10.1007/s10439-023-03271-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 06/01/2023] [Indexed: 06/18/2023]
Abstract
OBJECTIVE To differentiate healthy from artificially degraded articular cartilage and estimate its structural, compositional, and functional properties using Raman spectroscopy (RS). DESIGN Visually normal bovine patellae (n = 12) were used in this study. Osteochondral plugs (n = 60) were prepared and artificially degraded either enzymatically (via Collagenase D or Trypsin) or mechanically (via impact loading or surface abrasion) to induce mild to severe cartilage damage; additionally, control plugs were prepared (n = 12). Raman spectra were acquired from the samples before and after artificial degradation. Afterwards, reference biomechanical properties, proteoglycan (PG) content, collagen orientation, and zonal (%) thickness of the samples were measured. Machine learning models (classifiers and regressors) were then developed to discriminate healthy from degraded cartilage based on their Raman spectra and to predict the aforementioned reference properties. RESULTS The classifiers accurately categorized healthy and degraded samples (accuracy = 86%), and successfully discerned moderate from severely degraded samples (accuracy = 90%). On the other hand, the regression models estimated cartilage biomechanical properties with reasonable error (≤ 24%), with the lowest error observed in the prediction of instantaneous modulus (12%). With zonal properties, the lowest prediction errors were observed in the deep zone, i.e., PG content (14%), collagen orientation (29%), and zonal thickness (9%). CONCLUSION RS is capable of discriminating between healthy and damaged cartilage, and can estimate tissue properties with reasonable errors. These findings demonstrate the clinical potential of RS.
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Affiliation(s)
- Eslam Shehata
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Ervin Nippolainen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Rubina Shaikh
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | | | - Juha Töyräs
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Science Service Center, Kuopio University Hospital, Kuopio, Finland
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Jaakko K. Sarin
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Department of Medical Physics, Medical Imaging Center, Pirkanmaa Hospital District, Tampere, Finland
| | - Isaac O. Afara
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
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Linus A, Tanska P, Sarin JK, Nippolainen E, Tiitu V, Mäkelä JTA, Töyräs J, Korhonen RK, Mononen ME, Afara IO. Visible and Near-Infrared Spectroscopy Enables Differentiation of Normal and Early Osteoarthritic Human Knee Joint Articular Cartilage. Ann Biomed Eng 2023; 51:2245-2257. [PMID: 37332006 PMCID: PMC10518273 DOI: 10.1007/s10439-023-03261-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 05/27/2023] [Indexed: 06/20/2023]
Abstract
Osteoarthritis degenerates cartilage and impairs joint function. Early intervention opportunities are missed as current diagnostic methods are insensitive to early tissue degeneration. We investigated the capability of visible light-near-infrared spectroscopy (Vis-NIRS) to differentiate normal human cartilage from early osteoarthritic one. Vis-NIRS spectra, biomechanical properties and the state of osteoarthritis (OARSI grade) were quantified from osteochondral samples harvested from different anatomical sites of human cadaver knees. Two support vector machines (SVM) classifiers were developed based on the Vis-NIRS spectra and OARSI scores. The first classifier was designed to distinguish normal (OARSI: 0-1) from general osteoarthritic cartilage (OARSI: 2-5) to check the general suitability of the approach yielding an average accuracy of 75% (AUC = 0.77). Then, the second classifier was designed to distinguish normal from early osteoarthritic cartilage (OARSI: 2-3) yielding an average accuracy of 71% (AUC = 0.73). Important wavelength regions for differentiating normal from early osteoarthritic cartilage were related to collagen organization (wavelength region: 400-600 nm), collagen content (1000-1300 nm) and proteoglycan content (1600-1850 nm). The findings suggest that Vis-NIRS allows objective differentiation of normal and early osteoarthritic tissue, e.g., during arthroscopic repair surgeries.
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Affiliation(s)
- Awuniji Linus
- Department of Technical Physics, University of Eastern Finland, 70211, Kuopio, Finland.
| | - Petri Tanska
- Department of Technical Physics, University of Eastern Finland, 70211, Kuopio, Finland
| | - Jaakko K Sarin
- Department of Medical Physics, Medical Imaging Center, Pirkanmaa Hospital District, Tampere, Finland
| | - Ervin Nippolainen
- Department of Technical Physics, University of Eastern Finland, 70211, Kuopio, Finland
| | - Virpi Tiitu
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Janne T A Mäkelä
- Department of Technical Physics, University of Eastern Finland, 70211, Kuopio, Finland
- Science Service Center, Kuopio University Hospital, Kuopio, Finland
| | - Juha Töyräs
- Department of Technical Physics, University of Eastern Finland, 70211, Kuopio, Finland
- Science Service Center, Kuopio University Hospital, Kuopio, Finland
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Rami K Korhonen
- Department of Technical Physics, University of Eastern Finland, 70211, Kuopio, Finland
| | - Mika E Mononen
- Department of Technical Physics, University of Eastern Finland, 70211, Kuopio, Finland
| | - Isaac O Afara
- Department of Technical Physics, University of Eastern Finland, 70211, Kuopio, Finland
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
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Shaikh R, Tafintseva V, Nippolainen E, Virtanen V, Solheim J, Zimmermann B, Saarakkala S, Töyräs J, Kohler A, Afara IO. Characterisation of Cartilage Damage via Fusing Mid-Infrared, Near-Infrared, and Raman Spectroscopic Data. J Pers Med 2023; 13:1036. [PMID: 37511649 PMCID: PMC10381453 DOI: 10.3390/jpm13071036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 06/19/2023] [Accepted: 06/22/2023] [Indexed: 07/30/2023] Open
Abstract
Mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), and Raman spectroscopy are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths and mitigates their weaknesses. This study investigates the feasibility of the fusion of MIR, NIR, and Raman spectroscopic data for characterising articular cartilage integrity. Osteochondral specimens from bovine patellae were subjected to mechanical and enzymatic damage, and then MIR, NIR, and Raman data were acquired from the damaged and control specimens. We assessed the capacity of individual spectroscopic methods to classify the samples into damage or control groups using Partial Least Squares Discriminant Analysis (PLS-DA). Multi-block PLS-DA was carried out to assess the potential of data fusion by combining the dataset by applying two-block (MIR and NIR, MIR and Raman, NIR and Raman) and three-block approaches (MIR, NIR, and Raman). The results of the one-block models show a higher classification accuracy for NIR (93%) and MIR (92%) than for Raman (76%) spectroscopy. In contrast, we observed the highest classification efficiency of 94% and 93% for the two-block (MIR and NIR) and three-block models, respectively. The detailed correlative analysis of the spectral features contributing to the discrimination in the three-block models adds considerably more insight into the molecular origin of cartilage damage.
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Affiliation(s)
- Rubina Shaikh
- Department of Technical Physics, University of Eastern Finland, 70211 Kuopio, Finland
- School of Physics, Clinical and Optometric Sciences, Technological University Dublin, D07 XT95 Dublin, Ireland
| | - Valeria Tafintseva
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway
| | - Ervin Nippolainen
- Department of Technical Physics, University of Eastern Finland, 70211 Kuopio, Finland
| | - Vesa Virtanen
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, 90570 Oulu, Finland
| | - Johanne Solheim
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway
| | - Boris Zimmermann
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway
| | - Simo Saarakkala
- Research Unit of Medical Imaging, Physics and Technology, University of Oulu, 90570 Oulu, Finland
- Research Unit of Health Sciences and Technology, University of Oulu, 90220 Oulu, Finland
| | - Juha Töyräs
- Department of Technical Physics, University of Eastern Finland, 70211 Kuopio, Finland
- Science Service Center, Kuopio University Hospital, 70210 Kuopio, Finland
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisban, QLD 4072, Australia
| | - Achim Kohler
- Faculty of Science and Technology, Norwegian University of Life Sciences, 1432 Ås, Norway
| | - Isaac O Afara
- Department of Technical Physics, University of Eastern Finland, 70211 Kuopio, Finland
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisban, QLD 4072, Australia
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Sharma VJ, Adegoke JA, Afara IO, Stok K, Poon E, Gordon CL, Wood BR, Raman J. Near-infrared spectroscopy for structural bone assessment. Bone Jt Open 2023; 4:250-261. [PMID: 37051828 PMCID: PMC10079377 DOI: 10.1302/2633-1462.44.bjo-2023-0014.r1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/09/2023] Open
Abstract
Aims Disorders of bone integrity carry a high global disease burden, frequently requiring intervention, but there is a paucity of methods capable of noninvasive real-time assessment. Here we show that miniaturized handheld near-infrared spectroscopy (NIRS) scans, operated via a smartphone, can assess structural human bone properties in under three seconds. Methods A hand-held NIR spectrometer was used to scan bone samples from 20 patients and predict: bone volume fraction (BV/TV); and trabecular (Tb) and cortical (Ct) thickness (Th), porosity (Po), and spacing (Sp). Results NIRS scans on both the inner (trabecular) surface or outer (cortical) surface accurately identified variations in bone collagen, water, mineral, and fat content, which then accurately predicted bone volume fraction (BV/TV, inner R2 = 0.91, outer R2 = 0.83), thickness (Tb.Th, inner R2 = 0.9, outer R2 = 0.79), and cortical thickness (Ct.Th, inner and outer both R2 = 0.90). NIRS scans also had 100% classification accuracy in grading the quartile of bone thickness and quality. Conclusion We believe this is a fundamental step forward in creating an instrument capable of intraoperative real-time use. Cite this article: Bone Jt Open 2023;4(4):250–261.
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Affiliation(s)
- Varun J. Sharma
- Department of Surgery, Melbourne Medical School, University of Melbourne, Melbourne, Australia
- Brian F. Buxton Department of Cardiac and Thoracic Aortic Surgery, Austin Hospital, Melbourne, Australia
- Spectromix Laboratory, Melbourne, Australia
| | - John A. Adegoke
- Spectromix Laboratory, Melbourne, Australia
- Centre for Biospectroscopy, Monash University, Melbourne, Australia
| | - Isaac O. Afara
- Spectromix Laboratory, Melbourne, Australia
- Centre for Biospectroscopy, Monash University, Melbourne, Australia
- Biomedical Spectroscopy Laboratory, Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- School of Information Technology and Electrical Engineering Faculty of Engineering, Architecture and Information Technology, Melbourne, Australia
| | - Kathryn Stok
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Australia
| | - Eric Poon
- Spectromix Laboratory, Melbourne, Australia
- Department of Medicine, Melbourne Medical School, University of Melbourne, Melbourne, Australia
| | - Claire L. Gordon
- Department of Medicine, Melbourne Medical School, University of Melbourne, Melbourne, Australia
- Department of Infectious Diseases, Austin Hospital, Melbourne, Australia
| | - Bayden R. Wood
- Spectromix Laboratory, Melbourne, Australia
- Centre for Biospectroscopy, Monash University, Melbourne, Australia
| | - Jaishankar Raman
- Department of Surgery, Melbourne Medical School, University of Melbourne, Melbourne, Australia
- Brian F. Buxton Department of Cardiac and Thoracic Aortic Surgery, Austin Hospital, Melbourne, Australia
- Spectromix Laboratory, Melbourne, Australia
- Correspondence should be sent to Jaishankar Raman. E-mail:
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Verma DK, Kumari P, Kanagaraj S. Engineering Aspects of Incidence, Prevalence, and Management of Osteoarthritis: A Review. Ann Biomed Eng 2022; 50:237-252. [DOI: 10.1007/s10439-022-02913-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 01/01/2022] [Indexed: 12/14/2022]
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Pekedis M, Ozan F, Yildiz H. Biomechanics of the Femoral Head Cartilage and Subchondral Trabecular Bone in Osteoporotic and Osteopenic Fractures. Ann Biomed Eng 2021; 49:3388-3400. [PMID: 34472001 DOI: 10.1007/s10439-021-02861-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/23/2021] [Indexed: 11/28/2022]
Abstract
This study aimed to investigate the relationship between the micro structural properties of the subchondral trabecular bone (STB) and the macro mechanical properties of the articular cartilage (AC) in patients with osteoporotic (OP) and osteopenic (OPE) fractures. Sixteen femoral head samples (OP;OPE, n = 8 each) were obtained from female patients who underwent hip hemiarthroplasty. STB and AC specimens were harvested from those heads. Bone specimens were scanned using µ-CT to determine the micro structural properties. In-situ nondestructive compressive tests were performed for the cartilages to obtain elastic properties. The finite element technique was implemented on STB models created from µ-CT data to compute apparent elastic modulus. In addition, dynamic cyclic destructive tests were performed on STB and AC specimens to assess failure cycles. The results demonstrated that STB specimens in OPE group have more interconnected structure and higher cyclic dynamic strength than those in OP group. Furthermore, bone mineral density, failure cycle, and trabecular number of STB were positively correlated with the cartilage failure cycle, which indicates that STB alteration may affect the macroscopic mechanical properties of AC. The findings suggest that STB loss correlates with a decrease in cartilage strength and that improving of bone quality may prevent cartilage weakness.
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Affiliation(s)
- Mahmut Pekedis
- Department of Mechanical Engineering, Faculty of Engineering, Ege University, Bornova, 35100, Izmir, Turkey.
| | - Firat Ozan
- Department of Orthopedics and Traumatology, Kayseri City Hospital, 38080, Kayseri, Turkey
| | - Hasan Yildiz
- Department of Mechanical Engineering, Faculty of Engineering, Ege University, Bornova, 35100, Izmir, Turkey
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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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