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BackgroundEarly 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. ObjectiveThe 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. MethodsTwelve 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. ResultsWhile 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. ConclusionsThe 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.

Introduction: Lupus erythematosus (LE) is an inflammatory autoimmune disease, that can affect the skin to varying degree. In particular, discoid LE (DLE) and the rare form of lupus panniculitis/profundus are associated with scarring alopecia. The heterogeneity of the clinical, dermatoscopic, and histologic presentation poses a major challenge to the clinician in the diagnosis and differential diagnosis of other forms of scarring alopecia. Objective: While noninvasive imaging techniques using optical coherence tomography (OCT) and reflectance confocal microscopy (RCM) have proven to be helpful in the diagnosis of scarring alopecia in the context of LE, this study aimed to investigate line‐field confocal OCT (LC‐OCT) to identify characteristic features of cicatricial alopecia in LE. Methods: Fifteen patients with cicatricial alopecia in LE were included and the most affected/inflamed areas of the scalp were prospectively examined. In analogy to histopathology and previously reported criteria in RCM, all images were evaluated according to seven established criteria and underwent descriptive analyses. Results: LC‐OCT revealed characteristic features of cicatricial alopecia, such as lymphocytic interface dermatitis (14/15; 93.3%) and basal cell vacuolization (13/15; 86.7%). The most impressive feature was the occurrence of prominent hyperreflective fibers in 14/15 patients (93.3%). Conclusion: LC‐OCT imaging can noninvasively detect morphologic criteria such as lymphocytic and vacuolar interface dermatitis of cicatricial alopecia due to LE. In particular, the presence of hyperreflective collagen fibers appears to be a characteristic easily recognizable feature that may facilitate differential diagnosis with other forms of cicatricial alopecia. Further studies are mandatory to differentiate other forms of scarring alopecia. [ABSTRACT FROM AUTHOR]

Background: Inflammatory skin diseases, such as psoriasis, atopic eczema, and contact dermatitis pose diagnostic challenges due to their diverse clinical presentations and the need for rapid and precise diagnostic assessment. Objective: While recent studies described non‐invasive imaging devices such as Optical coherence tomography and Line‐field confocal OCT (LC‐OCT) as possible techniques to enable real‐time visualization of pathological features, a standardized analysis and validation has not yet been performed. Methods: One hundred forty lesions from patients diagnosed with atopic eczema (57), psoriasis (50), and contact dermatitis (33) were imaged using OCT and LC‐OCT. Statistical analysis was employed to assess the significance of their characteristic morphologic features. Additionally, a decision tree algorithm based on Gini's coefficient calculations was developed to identify key attributes and criteria for accurately classifying the disease groups. Results: Descriptive statistics revealed distinct morphologic features in eczema, psoriasis, and contact dermatitis lesions. Multivariate logistic regression demonstrated the significance of these features, providing a robust differentiation between the three inflammatory conditions. The decision tree algorithm further enhanced classification accuracy by identifying optimal attributes for disease discrimination, highlighting specific morphologic criteria as crucial for rapid diagnosis in the clinical setting. Conclusion: The combined approach of descriptive statistics, multivariate logistic regression, and a decision tree algorithm provides a thorough understanding of the unique aspects associated with each inflammatory skin disease. This research offers a practical framework for lesion classification, enhancing the interpretability of imaging results for clinicians. [ABSTRACT FROM AUTHOR]

This article discusses the use of line-field confocal optical coherence tomography (LC-OCT) as a non-invasive imaging technique for the diagnosis and grading of allergic contact dermatitis. The study involved patch testing on the upper back of 20 patients with positive patch-test reactions, and the affected skin lesions were imaged using the LC-OCT device. The LC-OCT images successfully captured microanatomical features of allergic reactions, such as spongiosis, inflammatory cells, and vesicles. The severity of dermatitis correlated with the presence of certain morphological features observed in the LC-OCT images. The authors suggest that LC-OCT can enhance diagnostic accuracy and serve as an objective measurement tool for patch-test grading in allergic contact dermatitis. However, they also acknowledge the need for further research and validation of their findings. [Extracted from the article]

Summary: Background and objectives: Onychomycosis is common and important to distinguish from other nail diseases. Rapid and accurate diagnosis is necessary for optimal patient treatment and outcome. Non‐invasive diagnostic tools have increasing potential for nail diseases including onychomycosis. This study evaluated line‐field confocal optical coherence tomography (LC‐OCT) as a rapid non‐invasive tool for diagnosing onychomycosis as compared to confocal laser scanning microscopy (CLSM), optical coherence tomography (OCT), and conventional methods. Patients and Methods: In this prospective study 86 patients with clinically suspected onychomycosis and 14 controls were examined using LC‐OCT, OCT, and CLSM. KOH‐preparation, fungal culture, PCR, and histopathology were used as comparative conventional methods. Results: LC‐OCT had the highest sensitivity and negative predictive value of all methods used, closely followed by PCR and OCT. Specificity and positive predictive value of LC‐OCT were as high as with CLSM, while OCT scored much lower. The gold standard technique, fungal culture, showed the lowest sensitivity and negative predictive value. Only PCR and culture allowed species differentiation. Conclusions: LC‐OCT enables quick and non‐invasive detection of onychomycosis, with advantages over CLSM and OCT, and similar diagnostic accuracy to PCR but lacking species differentiation. For accurate nail examination, LC‐OCT requires well‐trained and experienced operators. [ABSTRACT FROM AUTHOR]

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Summary: In addition to prevention of surgical site infections after skin surgery, perioperative antibiotic prophylaxis (PAP) aims to prevent the occurrence of other postoperative infectious complications, especially bacterial endocarditis and hematogenous joint prosthesis infections. This article discusses specific indications for the use of PAP. For example, patients who have undergone any type of heart valve replacement, including transcatheter valve replacement or use of prosthetic material to correct the heart valve, or patients who have experienced bacterial endocarditis, require PAP during skin surgery on mucosal membranes or ulcerated tumors. The use of PAP in special situations such as secondary wound healing, septic dermatosurgery or ulcer surgery is also presented and discussed in detail in this paper based on the current scientific literature. This paper represents the second part of the position paper of the Antibiotic Stewardship Working Group of the German Society for Dermatologic Surgery (DGDC) and summarizes evidence‐based recommendations for the administration of PAP during skin surgery for special indications and situations. This is particularly important because, as detailed in Part 1 of this position paper, PAP can and usually should be avoided in skin surgery. [ABSTRACT FROM AUTHOR]