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Updated: Nov 29, 2025
Application of Atomic Force Microscopy to Detect Early Osteoarthritis
Published on: May 24, 2020
Proof-of-concept for the detection of early osteoarthritis pathology by clinically applicable endomicroscopy and
M Tschaikowsky1, M Selig2, S Brander3
1Institute of Physical Chemistry, Albert-Ludwigs-University Freiburg, Albertstr. 21, 79104, Freiburg, Germany; G.E.R.N. Research Center for Tissue Replacement, Regeneration & Neogenesis, Department of Orthopedics and Trauma Surgery, Medical Center - Albert-Ludwigs-University of Freiburg, Faculty of Medicine, Albert-Ludwigs-University of Freiburg, Germany.
Objective:
Clinical trials for osteoarthritis (OA), the leading cause of global disability, are unable to pinpoint the early, potentially reversible disease with clinical technology. Hence, disease-modifying drug candidates cannot be tested early in the disease. To overcome this obstacle, we asked whether early OA-pathology detection is possible with current clinical technology.
Methods:
We determined the relationship between two sensitive early OA markers, atomic force microscopy (AFM)-measured human articular cartilage (AC) surface stiffness, and location-matched superficial zone chondrocyte spatial organizations (SCSOs), asking whether a significant loss of surface stiffness can be detected in early OA SCSO stages. We then tested whether current clinical technology can visualize and accurately diagnose the SCSOs using an approved probe-based confocal laser-endomicroscope and a random forest (RF) model.
Results:
We demonstrated a correlation between AC surface stiffness and the SCSO (rrm = -0.91; 95%CI: -0.97, -0.73), and an extensive loss of surface stiffness specifically in those ACs with early OA-typical SCSO (95%CIs: string SCSO: 269-173 kPa, double string SCSO: 77-46 kPa). This established the SCSO as a visualizable, functionally relevant surrogate marker of early OA AC surface pathology. Moreover, SCSO-based stiffness discrimination worked well in each patient's AC. We then demonstrated feasibility of visualizing the SCSO by clinical laser-endomicroscopy and, importantly, accurate SCSO diagnosis using RF.
Conclusion:
We present the proof-of-concept of early OA-pathology detection with available clinical technology, introducing a future-oriented, AI-supported, non-destructive quantitative optical biopsy for early disease detection. Operationalizing SCSO recognition, this approach allows testing for correlations between local tissue architectures with other experimental and clinical read-outs, but needs clinical validation and a larger sample size for defining diagnostic thresholds.
Insights
Early osteoarthritis (OA) detection is now possible using current clinical technology. Superficial zone chondrocyte spatial organizations (SCSOs) serve as a visualizable marker for early OA, enabling non-destructive diagnosis.
Area of Science:
- Biomedical Engineering
- Orthopedics
- Medical Imaging
Background:
- Osteoarthritis (OA) is a leading cause of disability, but early, reversible disease stages are difficult to detect with current clinical technology.
- This limitation hinders the testing of disease-modifying drugs in early OA.
- A need exists for advanced diagnostic tools to identify early OA pathology.
Purpose of the Study:
- To determine if early OA pathology can be detected using existing clinical technology.
- To investigate the relationship between articular cartilage (AC) surface stiffness and superficial zone chondrocyte spatial organizations (SCSOs) in early OA.
- To assess the feasibility of diagnosing early OA using laser-endomicroscopy and machine learning.
Main Methods:
- Correlated atomic force microscopy (AFM)-measured AC surface stiffness with SCSOs.
- Utilized probe-based confocal laser-endomicroscopy to visualize SCSOs.
- Employed a random forest (RF) model for accurate SCSO diagnosis.
Main Results:
- A strong correlation (r=-0.91) was found between AC surface stiffness and SCSOs.
- Significant loss of AC surface stiffness was observed in early OA-typical SCSOs.
- Laser-endomicroscopy successfully visualized SCSOs, and RF models accurately diagnosed them.
Conclusions:
- Proof-of-concept for early OA detection using available clinical technology.
- Introduced an AI-supported, non-destructive quantitative optical biopsy for early OA diagnosis.
- SCSO recognition enables correlation of tissue architecture with clinical read-outs, pending further validation.
