Reinholz M, et al. Ann Dermatol. 2012 May;24(2):126-135. http://dx.doi.org/10.5021/ad.2012.24.2.126

Background: As a result of the COVID‐19 outbreak, hygiene regulations have been revised and hand sanitation has been intensified.Objective: To investigate the onset of hand eczema during the COVID‐19 pandemic in healthcare workers (HCWs) directly involved in intensive care of COVID‐19 patients and HCWs without direct contact with COVID‐19 patients. Hereby, we aim at increasing awareness about occupational hand eczema and preventive measures that can be adopted.Method: A survey was distributed amongst 114 HCWs at a single surgical centre and at a COVID‐19 intensive care unit of the university hospital Ludwig Maximilian University Munich, Germany. Participants were questioned about the daily frequency of hand hygiene prior to and during the pandemic. Participants self‐reported the onset of hand eczema and associated symptoms.Results: Our study revealed a significant increase in hand washing, disinfection, and use of hand cream across all participants (P‐value <.001), regardless of having direct contact with COVID‐19 patients. A high prevalence of symptoms associated with acute hand dermatitis of 90.4% was found across all HCWs, whereas hand eczema itself was underreported (14.9%).Conclusion: The increase in hand sanitation during the COVID‐19 pandemic impairs the skin of the hands across all HCWs, independent of direct intensive care of affected patients.

Background: Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of artificial intelligence (AI) in classifying dermoscopic images of melanoma and nevi, concluding that these algorithms should assist a dermatologist’s diagnoses. Objective: The aim of this study was to investigate whether AI support improves the accuracy and overall diagnostic performance of dermatologists in the dichotomous image–based discrimination between melanoma and nevus. Methods: Twelve board-certified dermatologists were presented disjoint sets of 100 unique dermoscopic images of melanomas and nevi (total of 1200 unique images), and they had to classify the images based on personal experience alone (part I) and with the support of a trained convolutional neural network (CNN, part II). Additionally, dermatologists were asked to rate their confidence in their final decision for each image. Results: While the mean specificity of the dermatologists based on personal experience alone remained almost unchanged (70.6% vs 72.4%; P=.54) with AI support, the mean sensitivity and mean accuracy increased significantly (59.4% vs 74.6%; P=.003 and 65.0% vs 73.6%; P=.002, respectively) with AI support. Out of the 10% (10/94; 95% CI 8.4%-11.8%) of cases where dermatologists were correct and AI was incorrect, dermatologists on average changed to the incorrect answer for 39% (4/10; 95% CI 23.2%-55.6%) of cases. When dermatologists were incorrect and AI was correct (25/94, 27%; 95% CI 24.0%-30.1%), dermatologists changed their answers to the correct answer for 46% (11/25; 95% CI 33.1%-58.4%) of cases. Additionally, the dermatologists’ average confidence in their decisions increased when the CNN confirmed their decision and decreased when the CNN disagreed, even when the dermatologists were correct. Reported values are based on the mean of all participants. Whenever absolute values are shown, the denominator and numerator are approximations as every dermatologist ended up rating a varying number of images due to a quality control step. Conclusions: The findings of our study show that AI support can improve the overall accuracy of the dermatologists in the dichotomous image–based discrimination between melanoma and nevus. This supports the argument for AI-based tools to aid clinicians in skin lesion classification and provides a rationale for studies of such classifiers in real-life settings, wherein clinicians can integrate additional information such as patient age and medical history into their decisions.