Non-invasive biochemical analysis and comparison of atopic dermatitis and psoriasis skin using handheld confocal Raman spectroscopy.

A handheld non-invasive confocal Raman system (CRS) was used to evaluate the differences in skin biochemicals between atopic dermatitis (AD) and psoriasis, which are inflammatory skin conditions. Raman spectral measurements in the fingerprint and high wavenumber region were acquired using a portable in-house CRS system with excitation lasers operating at 671 and 785 nm. It was deduced that relative amount of water decreases in the following sequence of skin: healthy, psoriasis and AD. Moreover, differential trends were observed for the subclasses of ceramides such that ceramide 3 is lower in the lesional AD and psoriasis skin as compared to healthy, while ceramide 2 showed a contrasting trend of decrease in lesional AD and increase in lesional psoriasis as opposed to healthy skin. Amount of cholesterol was significantly higher in lesional psoriasis as compared to lesional AD and healthy skin. These differences can aid in an objective classification of the skin conditions and in the formulation of new disease-specific topical treatments.

Automated grading of acne vulgaris by deep learning with convolutional neural networks.

BACKGROUND: The visual assessment and severity grading of acne vulgaris by physicians can be subjective, resulting in inter- and intra-observer variability. OBJECTIVE: To develop and validate an algorithm for the automated calculation of the Investigator's Global Assessment (IGA) scale, to standardize acne severity and outcome measurements. MATERIALS AND METHODS: A total of 472 photographs (retrieved 01/01/2004-04/08/2017) in the frontal view from 416 acne patients were used for training and testing. Photographs were labeled according to the IGA scale in three groups of IGA clear/almost clear (0-1), IGA mild (2), and IGA moderate to severe (3-4). The classification model used a convolutional neural network, and models were separately trained on three image sizes. The photographs were then subjected to analysis by the algorithm, and the generated automated IGA scores were compared to clinical scoring. The prediction accuracy of each IGA grade label and the agreement (Pearson correlation) of the two scores were computed. RESULTS: The best classification accuracy was 67%. Pearson correlation between machine-predicted score and human labels (clinical scoring and researcher scoring) for each model and various image input sizes was 0.77. Correlation of predictions with clinical scores was highest when using Inception v4 on the largest image size of 1200 × 1600. Two sets of human labels showed a high correlation of 0.77, verifying the repeatability of the ground truth labels. Confusion matrices show that the models performed sub-optimally on the IGA 2 label. CONCLUSION: Deep learning techniques harnessing high-resolution images and large datasets will continue to improve, demonstrating growing potential for automated clinical image analysis and grading.