Atopic dermatitis (AD) and psoriasis are the two most prevalent skin disorders, often assessed through subjective questionnaires or visual evaluations conducted by clinicians, which can be subject to interpersonal variations. This study aims to explore the distinctions between these skin conditions and healthy skin using a portable confocal Raman spectroscopy (CRS) system for objective assessment. Spectral measurements at 671 nm and 785 nm on 9 AD, 6 psoriasis, and 11 healthy subjects reveal lower water content in AD compared to psoriasis and healthy skin. Ceramide subclasses show disease-specific trends, distinguishing AD, and psoriasis. Cholesterol levels further differentiate these conditions, with lower concentrations in lesional AD and significantly higher concentrations in lesional psoriasis compared to healthy skin. These differences contribute to the objective differentiation of skin conditions aiding in thorough assessment and treatment monitoring. Furthermore, it offers valuable insights for developing targeted disease-specific topical treatments.
Non-melanoma skin cancers (NMSC) pose challenges with current clinical approaches. In this context, Multispectral Optoacoustic Tomography (MSOT) offers a promising non-invasive imaging solution. With high isotropic resolution and contrast-enhanced capabilities, MSOT provides a 3D tumor map by resolving melanin and haemoglobin signals. Our feature extraction and automated level-set image segmentation algorithm enables tumor profiling and precise boundary delineation of width, depth, and volume. Validated against histology, these metrics aid preoperative tumor mapping and surgical planning as it fosters a comprehensive understanding of tumor morphology and metabolic activities. Thus, clinicians can optimize NMSC management, revolutionizing diagnostics and treatment for improved patient outcomes.
Auretek is the first of its kind portable Confocal Raman Spectroscopy (CRS) system equipped with fiber based handheld probe, that cascades dual-wavelength lasers. With its flexible probe, Auretek can acquire CRS data of skin from various parts of the human body with depth profiling. A clinical study was conducted to investigate the effects of ceramide-based moisturizer (CBM) on skin physiology and biochemistry. Quantitative skin component characterization in different epidermal layers was done with spectral unmixing analysis of the CRS data. Even though the analysis revealed a significant increase in ceramide content on the side of CBM application, the increase was in the layers within the stratum corneum.
Atopic dermatitis (AD) is a common inflammatory skin disorder which affects ~20% of children and ~3% adults worldwide. There lacks a direct, non-invasive method of evaluating atopic dermatitis (AD) accurately. Here, the use a multispectral raster-scanning optoacoustic mesoscopy (MS-RSOM) as an objective imaging tool for AD is proposed. MS-RSOM is a novel, non-invasive optoacoustic imaging modality which can provide label-free, high resolution imaging up to 1.5 mm below the skin. It can provide useful information on melanin, oxyhemoglobin (HbO2), deoxyhemoglobin (Hb) and oxygen saturation (sO2) from the skin layers. This preliminary study was conducted on 4 AD patients and 2 healthy volunteers using MS-RSOM system. From the MS-RSOM images, the epidermis thickness and oxygen saturation were computed from the healthy volunteers as well as from the non-lesional and eczema lesional areas of the eczema subjects.
Inflammatory skin disorder, eczema, is usually assessed by subjective disease scoring systems such as SCORAD and EASI. These scoring systems are based on clinical observations and questionnaires and hence it is subjected to inter and intra-assessor variability. Here, for the first time, we used optoacoustic imaging to image the structural and morphological changes of the skin in a non-invasive manner. Through a clinical study, we computed specific metrics such as epidermis thickness, total blood volume, vessel diameter in the dermis, ratio of low and high frequency signals. We trained a linear kernel-based support vector machine model for eczema classification using these metrics. We could achieve an accuracy of 86.6% and high sensitivity and specificity of 96.2% and 82.1% respectively. We also formulated a novel Eczema Vascular and Structural Index (EVSI) to objectively assess the severity of eczema.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.