Optimized parametric skin modelling for diagnosis of skin abnormalities by combining light back-scatter and laser speckle imaging

Progressive Observation of Covid-19 Vaccination Effects on Skin-Cellular Structures by Use of Intelligent Laser Speckle Classification (ILSC) Technique

The recent pandemic showed that the current global research strategies on vaccine development in an emergency period necessitates more optimized supplementary techniques to observe instant progressive vaccines' subtle effects on human metabolisms to make better and speedy evolutionary health assessments. To fill this gap, we have followed a multi-disciplinary approach exploiting AI, laser-optics, and specific imaging methods. The proposed technique can make progressive observations on Covid-19 Astra Zeneca vaccination effects on skin cellular network by use of the well-established technique-Intelligent Laser Speckle Classification (ILSC), as Covid-19 is a skin-affecting systemic disease. The method also managed to distinguish between three different subject groups via their laser speckle skin image samplings, grouped as early-vaccinated, late-vaccinated and non-vaccinated participants. The results have proven that the ILSC technique, in association with the parametrically optimised Bayesian network, can classify hidden skin changes of vaccinated and non-vaccinated individuals up to 90% accuracy and is also capable of detecting instant progressive developments pertaining to skin cellular properties. The proposed method has also proven that the continuous Covid-19 vaccine effect on the sub-skin layers can be observable by high frequency and speedy non-invasive data collection in real-time with high reliability.

Multi-wavelength, handheld laser speckle imaging for skin evaluation

OBJECTIVE

A handheld device was developed and qualified for in vivo human skin evaluation using laser speckle imaging technology.

METHODS

Each laser speckle device prototype allows the choice of up to three different laser wavelengths in the range of 400 nm to 800 nm in total. Speckle pattern analysis gives various speckle parameters, for example, speckle contrast, speckle size, speckle modulation or fractal dimension. The developed laser speckle device prototypes were evaluated investigating three skin issues.

RESULTS

We receive reproducible results from the speckle imaging device. For skin ageing, we found significant changes within three age groups. The effect of a methyl nicotinate treatment was clearly visible and quantifiable using a moorFLPI device as well as our speckle imaging device. In terms of basal cell carcinoma diagnosis, we found significant differences between normal and diseased skin, even though the number of samples was limited.

CONCLUSION

As shown with first application examples, it was possible to demonstrate the potential of the method for skin evaluation in vivo.

Texture-based characterization of subskin features by specified laser speckle effects at λ = 650 nm region for more accurate parametric 'skin age' modelling

OBJECTIVE

The textural structure of 'skin age'-related subskin components enables us to identify and analyse their unique characteristics, thus making substantial progress towards establishing an accurate skin age model.

METHODS

This is achieved by a two-stage process. First by the application of textural analysis using laser speckle imaging, which is sensitive to textural effects within the λ = 650 nm spectral band region. In the second stage, a Bayesian inference method is used to select attributes from which a predictive model is built.

RESULTS

This technique enables us to contrast different skin age models, such as the laser speckle effect against the more widely used normal light (LED) imaging method, whereby it is shown that our laser speckle-based technique yields better results.

CONCLUSION

The method introduced here is non-invasive, low cost and capable of operating in real time; having the potential to compete against high-cost instrumentation such as confocal microscopy or similar imaging devices used for skin age identification purposes.

Prediction of skin ages by means of multi-spectral light sources

Assessment of skin aging is a complex biological process that depends on various internal and external factors but has become important due to personalized skin health and cosmetic treatments. Although there are a small number of attempts to assess the skin aging, they identify only one of the previously classified skin aging groups. The methods used to achieve it are also based generally on highly expensive measurement devices. This work therefore proposes novel low-cost skin aging assessment apparatus by using light back-scatter intensity level of Red, Blue, Green and Infrared bands. This is further enhanced by using a machine learning method to accurately predict actual skin age. The proposed method appears to be highly capable of capturing multi-layer cellular changes exhibited by the biological skin aging process and predicting skin ages with a root-mean-square error of as low as 0.160 by using only four features based on the four multi-spectral light sources. This assessment kit seems to be the first of its kind, which is expected to bring great benefit to both personalized skin healthcare and cosmetic sectors.