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The coronavirus disease 2019 (COVID-19) pandemic and subsequent physical distancing recommendations created major gaps in traditional dermatologic undergraduate and postgraduate medical education delivery. Nevertheless, the educational consequences of various public health restrictions have indirectly set aside the inertia, resistance, and risk averse approach to pedagogical change in medicine. In Canada, rapid collaboration and innovation in dermatologic education has led to novel programs including the implementation of a range of internet-facilitated group learning activities and a dramatic expansion of digital telehealth and virtual care. Going forward, three key issues arising from these developments will need to be addressed: the ongoing assessment of these innovations for efficacy; sustaining the momentum and creativity that has been achieved; and, determining which of these activities are worth maintaining when traditional "tried and true" learning activities can be resumed. [ABSTRACT FROM AUTHOR]

Background/Purpose: A recent direction in skin disease classification is to develop quantitative diagnostic techniques. Skin relief, colloquially known as roughness, is an important clinical feature. The aim of this study is to demonstrate a novel polarization speckle technique to quantitatively measure roughness on skin lesions in vivo. We then calculate the average roughness of different types of skin lesions to determine the extent to which polarization speckle roughness measurements can be used to identify skin cancer. Methods: The experimental conditions were set to target the fine relief structure on the order of ten microns within a small field of view of 3 mm. The device was tested in a clinical study on patients with malignant and benign skin lesions that resemble cancer. The cancer group includes 37 malignant melanomas (MM), 43 basal cell carcinomas (BCC), and 26 squamous cell carcinomas (SCC), all categories confirmed by gold standard biopsy. The benign group includes 109 seborrheic keratoses (SK), 79 nevi, and 11 actinic keratoses (AK). Normal skin roughness was obtained for the same patients (301 different body sites proximal to the lesion). Results: The average root mean squared (rms) roughness ± standard error of the mean for MM and nevus was equal to 19 ± 5 μm and 21 ± 3 μm, respectively. Normal skin has rms roughness of 31 ± 3 μm, other lesions have roughness of 35 ± 10 μm (AK), 35 ± 7 μm (SCC), 31 ± 4 μm (SK), and 30 ± 5 μm (BCC). Conclusion: An independent‐samples Kruskal–Wallis test indicates that MM and nevus can be separated from each of the tested types of lesions, except each other. These results quantify clinical knowledge of lesion roughness and could be useful for optical cancer detection. [ABSTRACT FROM AUTHOR]

Background: Biases inherent in self‐assessment of sun exposure and sun‐safe behavior may lead to inaccurate conclusions about the effectiveness of sun‐safety educational programs. Objectives: We aimed to compare self‐reports to objective measures of sun exposure, when examining the effectiveness of passive versus active educational interventions. Methods: From May to June 2018, 73 participants recruited at a dermatology clinic were sequentially assigned to receive sun‐safety education through one of 3 modes: interactive online module, video, or no education. A baseline Sun Exposure and Behavior Inventory (SEBI) questionnaire was administered, and spectrophotometric measurements of sun‐exposed and sun‐protected areas were taken and reported in the CIE L*a*b* color space. Participants were followed 4–8 and 16 weeks after the initial visit where the SEBI was re‐administered, and serial measurements of skin color were taken. The change in SEBI scores and L*a*b values, as calculated by the individual typology angle (ITA°), was analyzed. Results: There was a significant increase in skin darkening in all the groups at 4–8 and 16 weeks follow‐up. There was no statistically significant difference between the groups in the magnitude of color change. However, subjectively at 4‐8 weeks post‐intervention, participants in the interactive module and video groups had significantly improved self‐reported SEBI scores compared to control (p <.05, Kruskal‐Wallis). By 16 weeks, only the interactive module group showed significant improvement in SEBI scores compared to control (p <.05, ANOVA). Conclusion: In determining the effectiveness of sun‐safety programs, spectrophotometric evaluation of sun‐induced skin pigmentation can allow for a more complete evaluation of self‐reported sun exposure and sun‐protective behavior. [ABSTRACT FROM AUTHOR]

Background: Although many hair disorders can be readily diagnosed based on their clinical appearance, their progression and response to treatment are often difficult to monitor, particularly in quantitative terms. We introduce an innovative technique utilizing a smartphone and computerized image analysis to expeditiously and automatically measure and compute hair density and diameter in patients in real time. Methods: A smartphone equipped with a dermatoscope lens wirelessly transmits trichoscopy images to a computer for image processing. A black‐and‐white binary mask image representing hair and skin is produced, and the hairs are thinned into single‐pixel‐thick fiber skeletons. Further analysis based on these fibers allows morphometric characteristics such as hair shaft number and diameters to be computed rapidly. The hair‐bearing scalps of fifty participants were imaged to assess the precision of our automated smartphone‐based device in comparison with a specialized trichometry device for hair shaft density and diameter measurement. The precision and operation time of our technique relative to manual trichometry, which is commonly used by hair disorder specialists, is determined. Results: An equivalence test, based on two 1‐sided t tests, demonstrates statistical equivalence in hair density and diameter values between this automated technique and manual trichometry within a 20% margin. On average, this technique actively required 24 seconds of the clinician's time whereas manual trichometry necessitated 9.2 minutes. Conclusion: Automated smartphone‐based trichometry is a rapid, precise, and clinically feasible technique which can significantly facilitate the assessment and monitoring of hair loss. Its use could be easily integrated into clinical practice to improve standard trichoscopy. [ABSTRACT FROM AUTHOR]

• Developed a novel polarization speckle system that incorporates deep learning. • Polarization speckle data lends itself to augmentation for machine learning and deep learning. • In a clinical validation study, multiple types of skin cancers are separated from benign skin lesions. • Deep learning enables improved detection performance over machine learning and previous speckle analysis methods. Polarization speckle is a growing research field, especially for the purposes of biomedical imaging and diagnostics. The statistical uniformity of speckle creates novel opportunities for AI methods to analyze speckle patterns, as they are difficult for a human to interpret. We employed deep learning and traditional machine learning methods (Support Vector Machine, K Nearest Neighbor, and Random Forests) to analyze polarization speckle images generated from the general categories of malignant and benign lesions, a classification task previously found to be very difficult. Their performance in classifying the patterns were compared both to each other and to the previous statistical methods of speckle analysis. A collection of 122 malignant and 196 benign skin lesion speckle images were augmented for deep learning using patch cropping, an advantageous method given the patterns' statistical homogeneity. The machine learning technique performed with a diagnostic accuracy of less than 65%, a result comparable to those of previous statistical methods, despite the fact that the same machine learning technique could achieve a high 90% accuracy in the simpler classification task of differentiating malignant melanoma and the benign lookalike seborrheic keratosis. However, ResNet, our chosen deep learning architecture, achieved the best performance of 82% diagnostic accuracy in the general classification task of malignant and benign. These results show that deep learning extracts previously hidden information from polarization speckle, which further develops towards a rapid, real-time, analysis technique for an automatic skin cancer detection system. [ABSTRACT FROM AUTHOR]