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Automated atomic force microscopy analysis using convolutional and recurrent neural networks.
Jonathan Haydak1, Evren U Azeloglu1
1Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, New York.
Biophysical Journal
|May 10, 2025
Summary
A new machine learning algorithm, COBRA, accurately analyzes atomic force microscopy (AFM) data. This method reliably identifies the contact point and filters low-quality curves, improving biomechanical property analysis.
Area of Science:
- Biophysics
- Materials Science
- Cell Biology
Background:
- Atomic force microscopy (AFM) is crucial for characterizing cell and tissue biomechanics.
- Analyzing AFM force curves is challenging due to noise and contact point uncertainty.
- Existing methods lack speed, reproducibility, and quantitative accuracy.
Purpose of the Study:
- To develop a novel machine learning algorithm for processing AFM force curves.
- To improve the accuracy and reliability of contact point determination in AFM data.
- To enable high-throughput, precise biomechanical analysis of cells and tissues.
Main Methods:
- A convolutional bidirectional recurrent neural network (COBRA) was developed.
- The algorithm was trained on over 5000 curated AFM force curves from diverse cell types.
- COBRA was compared against classical and other machine learning techniques.
Main Results:
- COBRA demonstrated superior identification of low-quality AFM curves (AUC 0.92).
- The algorithm achieved minimal contact point error (28 ± 3 nm).
- Pointwise elastic modulus was determined with a mean absolute percentage error of 5.3% ± 0.7%.
Conclusions:
- COBRA reliably filters low-quality AFM force curves and determines the contact point.
- The method enhances precision and reproducibility in high-throughput AFM analyses.
- COBRA offers a significant advancement for quantitative biomechanical characterization.
