ArticleAnnals of biomedical engineering2023
Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties.
Article in Annals of biomedical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- From microtissues to macro solutions - The future of scalable and automated cartilage tissue engineering.Journal of orthopaedic translation · 2026Review
- Assessment of morphology, radiopacity gradient and mechanical properties of articular cartilage with contrast-enhanced high-resolution peripheral quantitative computed tomography.Frontiers in bioengineering and biotechnology · 2026Article
- Raman spectroscopic probe provides optical biomarkers of cartilage composition predictive of tissue function.Osteoarthritis and cartilage · 2025Article
- Gene expression analysis of cytokines and MMPs in melatonin and rhBMP-2 enhanced bone remodeling.World journal of orthopedics · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
objectiveTo differentiate healthy from artificially degraded articular cartilage and estimate its structural, compositional, and functional properties using Raman spectroscopy (RS).
designVisually 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.
resultsThe 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%).
conclusionRS 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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