K-Nearest Neighbour-based classification of skin barrier damage using Raman imaging: A non-invasive assessment method...

Area of Science:

• Dermatology
• Biomedical Engineering
• Spectroscopy

Background:

• The stratum corneum is crucial for skin barrier function, preventing water loss.
• Impaired stratum corneum increases skin sensitivity, inflammation, and related issues.

Purpose of the Study:

• Establish reproducible skin damage models.
• Develop a non-invasive method to classify skin barrier damage severity.

Main Methods:

• Simulated skin barrier impairment using tape stripping and friction-induced injury models.
• Utilized Raman imaging to analyze stratum corneum thickness, lipid, and keratin levels.
• Applied K-nearest neighbor (KNN) algorithm for damage severity classification.

Main Results:

• Successfully classified skin damage severity from normal to severe.
• The integrated KNN model accurately predicted severity in unknown samples.
• Established a robust diagnostic framework for skin barrier assessment.

Conclusions:

• The non-invasive Raman imaging and KNN classification model shows significant potential for dermatological diagnostics.
• This method provides a reliable approach for evaluating and monitoring skin barrier damage.