Arthroscopic Raman Probe Provides Biomarkers of TMJ Tissue Composition that Account for Tissue Function
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A Probing Device for Quantitatively Measuring the Mechanical Properties of Soft Tissues during Arthroscopy
Published on: May 1, 2020
Background:
Temporomandibular joint (TMJ) disorders affect up to 25% of the population and may require diverse surgical interventions. However, intraoperative decision-making relies largely on visual assessment, which does not quantitatively characterize tissue composition or mechanical function. Raman spectroscopy offers a nondestructive approach to molecular-level tissue characterization, but its application to TMJ tissues remains unvalidated.
Methods:
An arthroscopy-compatible Raman probe was used to characterize mandibular condyles (n=15 porcine, n=11 human) and human articular discs (n=7). Raman-derived biomarkers associated with glycosaminoglycans (GAG), collagen (COL), water (H2O), and hydroxyapatite (HA) were evaluated against condylar indentation modulus and disc tensile modulus. Arthroscopic measurements in cadaveric TMJs assessed technical feasibility, while hyperspectral Raman imaging characterized spatial compositional distributions.
Results:
For condylar cartilage, combined GAG and H2O biomarkers accounted for 86% of the variation in indentation modulus (R2=0.86), compared with 4% for Outerbridge grading (R2=0.04). For articular discs, combined proline and water-associated biomarkers accounted for 88% of the variation in tensile modulus (R2=0.88). Raman spectra acquired in situ under simulated arthroscopic conditions were comparable in quality to ex vivo measurements. Raman imaging revealed distinct compositional patterns in healthy and degenerated tissues, including GAG depletion and cartilage thinning in degenerated condylar cartilage.
Conclusions:
Raman-derived biomarkers are strongly associated with TMJ cartilage and disc mechanical properties and outperform Outerbridge grading in explaining variation in condylar indentation modulus. Arthroscopic Raman spectroscopy is technically feasible and may complement visual assessment by providing objective compositional information to support future development of tissue-informed surgical decision-making.
