The application of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the stratum corneum

Drug permeation through the intercellular lipids, which pack around and between corneocytes, may be enhanced by increasing the thermodynamic activity of the active in a formulation. However, this may also result in unwanted drug crystallisation on and in the skin. In this work, we explore the combination of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the skin. Ex vivo permeation studies of saturated solutions of diclofenac sodium (DF Na) in two vehicles, propylene glycol (PG) and dimethyl sulphoxide (DMSO), were carried out in porcine ear skin. Tape stripping and ATR-FTIR spectroscopy were conducted simultaneously to collect spectral data as a function of skin depth. Multivariate data analysis was applied to visualise and categorise the spectral data in the region of interest (1700-1500cm(-1)) containing the carboxylate (COO(-)) asymmetric stretching vibrations of DF Na. Spectral data showed the redshifts of the COO(-) asymmetric stretching vibrations for DF Na in the solution compared with solid drug. Similar shifts were evident following application of saturated solutions of DF Na to porcine skin samples. Multivariate data analysis categorised the spectral data based on the spectral differences and drug crystallisation was found to be confined to the upper layers of the skin. This proof-of-concept study highlights the utility of ATR-FTIR spectroscopy in combination with multivariate data analysis as a simple and rapid approach in the investigation of drug deposition in the skin. The approach described here will be extended to the study of other actives for topical application to the skin

In Vivo Spectral Absorption Analysis Enabling the Classification of Pigmented Cutaneous Lesions: Investigating System Design and the Role of Faculative Melanin in the Spectral Determination of Skin Type Classification

A spectroradiometer was designed and built to record in vivo absorption characteristics of the lateral elbow. An analysis of variance of spectral characteristics between 400 and 1100 nanometers of faculative melanin to skin types one two and three was performed. The hypothysis that skin type could be differentiate by spectral recordings of in vivo faculative melanin was rejected. Further research is required to correlate constitutional melanin to skin type classification, in order to form a reference for pigmented cutaneous lesion classification

Familial cases of a submicroscopic Xp22.2 deletion : genotype-phenotype correlation in microphthalmia with linear skin defects syndrome

Purpose: Microphthalmia with linear skin defects syndrome (MLS or MIDAS, OMIM #309801) is a rare X-linked male-lethal disorder characterized by microphthalmia or other ocular anomalies and skin lesions limited to the face and neck. However, inter-and intrafamilial variability is high. Here we report a familial case of MLS. Methods: A mother and daughter with MLS underwent a complete ophthalmological examination, and extensive imaging, including anterior segment pictures, corneal topography and keratometry, autofluorescence, infrared reflectance and red free images, as well as spectral-domain optical coherence tomography. The mother also underwent full-field flash electroretinography. In addition, high-resolution array comparative genomic hybridization analysis was performed in both as well as in the maternal grandparents of the proband. Results: Microphthalmia and retinal abnormalities were noted in the proband and the mother, whereas only the mother presented with scars of the typical neonatal linear skin defects. Array comparative genomic hybridization analysis revealed a 185-220 kb deletion on chromosome band Xp22.2 including the entire HCCS gene. Conclusions: The identification of a deletion including HCCS led to the diagnosis of MLS in these patients. Retinal abnormalities can be part of the ocular manifestations of MLS

Multispectral imaging methods for the diagnosis of skin cancer lesions

En col·laboració amb la Universitat Autònoma de Barcelona (UAB) i la Universitat de Barcelona (UB).Skin cancer is the most prevalent form of cancer, and melanoma is one of the most threat disease of it. But it can be cured if it is detected early enough. Multispectral imaging is a potential method to differenciate melanoma from nevi as it provides spectral images with information of absorbance and reflectance. With this aim, spectral images along the visible and near infrared range (from 415nm to 995nm) of 165 lesions including nevi, melanomas and basal cell carcinomas were processed in this master thesis. After obtaining all data in terms of reflectance and absorbance and other related parameters for each pixel of the segmented lesions, a statistical analysis was carried out to quantify their spatial distribution all over each lesion. Algorithms such as Support vector machine (SVM) and Discriminant Analysis (DA) were used as a means of classifying the lesions. The results show that DA linear classifier provides a better diagnosis than the SVM. BCCs are easier to discriminate from nevi than melanomas

Hyper-Skin: A Hyperspectral Dataset for Reconstructing Facial Skin-Spectra from RGB Images

We introduce Hyper-Skin, a hyperspectral dataset covering wide range of wavelengths from visible (VIS) spectrum (400nm - 700nm) to near-infrared (NIR) spectrum (700nm - 1000nm), uniquely designed to facilitate research on facial skin-spectra reconstruction. By reconstructing skin spectra from RGB images, our dataset enables the study of hyperspectral skin analysis, such as melanin and hemoglobin concentrations, directly on the consumer device. Overcoming limitations of existing datasets, Hyper-Skin consists of diverse facial skin data collected with a pushbroom hyperspectral camera. With 330 hyperspectral cubes from 51 subjects, the dataset covers the facial skin from different angles and facial poses. Each hyperspectral cube has dimensions of 1024$\times$1024$\times$448, resulting in millions of spectra vectors per image. The dataset, carefully curated in adherence to ethical guidelines, includes paired hyperspectral images and synthetic RGB images generated using real camera responses. We demonstrate the efficacy of our dataset by showcasing skin spectra reconstruction using state-of-the-art models on 31 bands of hyperspectral data resampled in the VIS and NIR spectrum. This Hyper-Skin dataset would be a valuable resource to NeurIPS community, encouraging the development of novel algorithms for skin spectral reconstruction while fostering interdisciplinary collaboration in hyperspectral skin analysis related to cosmetology and skin's well-being. Instructions to request the data and the related benchmarking codes are publicly available at: \url{https://github.com/hyperspectral-skin/Hyper-Skin-2023}.Comment: Skin spectral datase

Assessment of skin inflammation using near-infrared Raman spectroscopy combined with artificial intelligence analysis in an animal model

Raman spectroscopy is a powerful method for estimating the molecular structure of a target that can be adapted for biomedical analysis given its non-destructive nature. Inflammatory skin diseases impair the skin’s barrier function and interfere with the patient’s quality of life. There are limited methods for non-invasive and objective assessment of skin inflammation. We examined whether Raman spectroscopy can be used to predict skin inflammation with high sensitivity and specificity when combined with artificial intelligence (AI) analysis. Inflammation was chemically induced in mouse ears, and Raman spectra induced by a 785 nm laser were recorded. A principal component (PC) analysis of the Raman spectra was performed to extract PCs with the highest percentage of variance and to estimate the statistical score. The accuracy in predicting inflammation based on the Raman spectra with or without AI analysis was assessed using receiver operating characteristic (ROC) curves. We observed some typical changes in the Raman spectra upon skin inflammation, which may have resulted from vasodilation and interstitial oedema. The estimated statistical scores based on spectral changes correlated with the histopathological changes in the skin. The ROC curve based on PC2, which appeared to include some spectral features, revealed a maximum accuracy rate of 80.0% with an area under the curve (AUC) of 0.864. The AI analysis improved the accuracy rate to 93.1% with an AUC of 0.972. The current findings demonstrate that the combination of Raman spectroscopy with near-infrared excitation and AI analysis can provide highly accurate information on the pathology of skin inflammation

Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours—A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and Convolutional Neural Networks

Several optical imaging techniques have been developed to ease the burden of skin cancer disease on our health care system. Hyperspectral images can be used to identify biological tissues by their diffuse reflected spectra. In this second part of a three-phase pilot study, we used a novel hand-held SICSURFIS Spectral Imager with an adaptable field of view and target-wise selectable wavelength channels to provide detailed spectral and spatial data for lesions on complex surfaces. The hyperspectral images (33 wavelengths, 477–891 nm) provided photometric data through individually controlled illumination modules, enabling convolutional networks to utilise spectral, spatial, and skin-surface models for the analyses. In total, 42 lesions were studied: 7 melanomas, 13 pigmented and 7 intradermal nevi, 10 basal cell carcinomas, and 5 squamous cell carcinomas. All lesions were excised for histological analyses. A pixel-wise analysis provided map-like images and classified pigmented lesions with a sensitivity of 87% and a specificity of 93%, and 79% and 91%, respectively, for non-pigmented lesions. A majority voting analysis, which provided the most probable lesion diagnosis, diagnosed 41 of 42 lesions correctly. This pilot study indicates that our non-invasive hyperspectral imaging system, which involves shape and depth data analysed by convolutional neural networks, is feasible for differentiating between malignant and benign pigmented and non-pigmented skin tumours, even on complex skin surfaces

Differentiating Malignant from Benign Pigmented or Non-Pigmented Skin Tumours—A Pilot Study on 3D Hyperspectral Imaging of Complex Skin Surfaces and Convolutional Neural Networks

Several optical imaging techniques have been developed to ease the burden of skin cancer disease on our health care system. Hyperspectral images can be used to identify biological tissues by their diffuse reflected spectra. In this second part of a three-phase pilot study, we used a novel hand-held SICSURFIS Spectral Imager with an adaptable field of view and target-wise selectable wavelength channels to provide detailed spectral and spatial data for lesions on complex surfaces. The hyperspectral images (33 wavelengths, 477–891 nm) provided photometric data through individually controlled illumination modules, enabling convolutional networks to utilise spectral, spatial, and skin-surface models for the analyses. In total, 42 lesions were studied: 7 melanomas, 13 pigmented and 7 intradermal nevi, 10 basal cell carcinomas, and 5 squamous cell carcinomas. All lesions were excised for histological analyses. A pixel-wise analysis provided map-like images and classified pigmented lesions with a sensitivity of 87% and a specificity of 93%, and 79% and 91%, respectively, for non-pigmented lesions. A majority voting analysis, which provided the most probable lesion diagnosis, diagnosed 41 of 42 lesions correctly. This pilot study indicates that our non-invasive hyperspectral imaging system, which involves shape and depth data analysed by convolutional neural networks, is feasible for differentiating between malignant and benign pigmented and non-pigmented skin tumours, even on complex skin surfaces

Spectral morphological analysis of skin lesions with a polarization multispectral dermoscope.

Dermoscopy is the conventional technique used for the clinical inspection of human skin lesions. However, the identification of diagnostically relevant morphologies can become a complex task. We report on the development of a polarization multispectral dermoscope for the in vivo imaging of skin lesions. Linearly polarized illumination at three distinct spectral regions (470, 530 and 625 nm), is performed by high luminance LEDs. Processing of the acquired images, by means of spectral and polarization filtering, produces new contrast images, each one specific for melanin absorption, hemoglobin absorption, and single scattering. Analysis of such images could facilitate the identification of pathological morphologies. (C) 2013 Optical Society of Americ

Spectral imaging of human portraits and image quality

This dissertation addresses the problem of capturing spectral images for human portraits and evaluating image quality of spectral images. A new spectral imaging approach is proposed in this dissertation for spectral images of human portraits. Thorough statistical analysis is performed for spectral reflectances from various races and different face parts. A spectral imaging system has been designed and calibrated for human portraits. The calibrated imaging system has the ability to represent not only the facial skin but also the spectra of lips, eyes and hair from various races as well. The generated spectral images can be applied to color-imaging system design and analysis. To evaluate the image quality of spectral imaging systems, a visual psychophysical image quality experiment has been performed in this dissertation. The spectral images were simulated based on real spectral imaging system. Meaningful image quality results have been obtained for spectral images generated from different spectral imaging systems. To bridge the gap between the physical measures and subjective visual perceptions of image quality, four image distortion factors were defined. Image quality metrics were obtained and evaluated based statistical analysis and multiple analysis. The image quality metrics have high correlation with subjective assessment for image quality. The image quality contribution of the distortion factors were evaluated. As an extension of the work other researchers in MCSL have initiated, this dissertation research will, working with other researchers in MCSL, put effort to build a publicly accessible database of spectral images, Lippmann2000