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Deep Learning-Based OCT Segmentation for Stiffness Quantification in Evaluating Low-Level Laser Therapy for Wound
Gilang Titah Ramadhan1, Yih-Kuen Jan2, Ben-Yi Liau3
1Department of Informatics, Tiga Serangkai University, Surakarta, Indonesia.
Background:
This study evaluated the short-term biomechanical response of wound tissue following low-level laser therapy (LLLT) by examining changes in skin stiffness, a surrogate biomechanical indicator of short-term tissue response, across different limb regions.
Methods:
A 660 nm LLLT protocol was applied to wound sites. Skin stiffness was quantified using optical coherence tomography (OCT) combined with an air-jet indentation system, enabling non-contact measurement of tissue deformation. For accurate layer-specific assessment, a U-Net-based model was employed to automate OCT image segmentation.
Results:
The automated segmentation by the U-Net model achieved a segmentation accuracy of 92%, facilitated precise segmentation of skin layers. LLLT significantly reduced skin stiffness after treatment, indicating an acute modulation of tissue compliance.
Conclusion:
Short-duration LLLT reduces skin stiffness immediately post-treatment, indicating its potential as a non-invasive intervention to modulate the biomechanical environment of wounds.
Trial Registration:
ClinicalTrials.gov identifier: NCT07177274.
