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Pemphigoid gestationis (PG) and polymorphic eruption of pregnancy (PEP) may be similar morphologically but confer different maternal and fetal risks. Direct immunofluorescence is the gold standard test used to differentiate between the 2 diagnoses but is not always available. To develop and validate a clinical scoring system to differentiate PG from PEP. After developing a scoring system based on differentiating clinical factors reported in existing literature, we tested its diagnostic accuracy in a retrospective international multicenter validation study in collaboration with the European Academy of Dermatology and Venereology's Skin Diseases in Pregnancy Taskforce. Nineteen pregnancies (16 patients) affected by PG and 39 pregnancies (39 patients) affected by PEP met inclusion criteria. PG had a mean score of 4.6 (SD, 2.5) and PEP had a mean score of −0.3 (SD, 2.0). The area under the curve was 0.93 (95% CI, 0.86-1.00). Univariate analysis revealed that almost all criteria used in the scoring system were significantly different between the groups (P <.05), except for skip pregnancy and multiple gestations, which were then removed from the final scoring system. Small retrospective study. The Pregnancy Dermatoses Clinical Scoring System may be useful to differentiate PG from PEP in resource-limited settings. [ABSTRACT FROM AUTHOR]

A patient with tinea incognita is presented together with a review of the literature of figurate erythema. Figurate lesions are emblematic for dermatology and perhaps the most picturesque efflorescences. The differential diagnosis can be broad and sometimes challenging. Many clinical entities with resembling primary and secondary efflorescences have to be considered as differentials and can be due to anti-infectious, paraneoplastic, allergic, autoimmune or other immune reactions. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have outperformed dermatologists in classifying pigmented skin lesions under artificial conditions. We investigated, for the first time, the performance of three-dimensional (3D) and two-dimensional (2D) CNNs and dermatologists in the early detection of melanoma in a real-world setting. In this prospective study, 1690 melanocytic lesions in 143 patients with high-risk criteria for melanoma were evaluated by dermatologists, 2D-FotoFinder-ATBM and 3D-Vectra WB360 total body photography (TBP). Excision was based on the dermatologists' dichotomous decision, an elevated CNN risk score (study-specific malignancy cut-off: FotoFinder >0.5, Vectra >5.0) and/or the second dermatologist's assessment with CNN support. The diagnostic accuracy of the 2D and 3D CNN classification was compared with that of the dermatologists and the augmented intelligence based on histopathology and dermatologists' assessment. Secondary end-points included reproducibility of risk scores and naevus counts per patient by medical staff (gold standard) compared to automated 3D and 2D TBP CNN counts. The sensitivity, specificity, and receiver operating characteristics area under the curve (ROC-AUC) for risk-score-assessments compared to histopathology of 3D-CNN with 95% confidence intervals (CI) were 90.0%, 64.6% and 0.92 (CI 0.85–1.00), respectively. While dermatologists and augmented intelligence achieved the same sensitivity (90%) and comparable classification ROC-AUC (0.91 [CI 0.80–1.00], 0.88 [CI 0.77–1.00]) with 3D-CNN, their specificity was superior (92.3% and 86.2%, respectively). The 2D-CNN (sensitivity: 70%, specificity: 40%, ROC-AUC: 0.68 [CI 0.46–0.90]) was outperformed by 3D CNN and dermatologists. The 3D-CNN showed a higher correlation coefficient for repeated measurements of 246 lesions (R = 0.89) than the 2D-CNN (R = 0.79). The mean naevus count per patient varied significantly (gold standard: 210 lesions; 3D-CNN: 469; 2D-CNN: 1324; p < 0.0001). Our study emphasises the importance of validating the classification of CNNs in real life. The novel 3D-CNN device outperformed the 2D-CNN and achieved comparable sensitivity with dermatologists. The low specificity of CNNs and the lack of automated counting of TBP nevi currently limit the use of augmented intelligence in clinical practice. • Real-world comparison of dermatologists and AI in melanoma detection. • Sensitivity of 3D CNN versus dermatologists comparable in classification. • 2D CNN was outperformed in classifying melanocytic lesions by 3D CNN/dermatologists. • Low specificity by augmented intelligence in real-life setting. • CNN nevi counting still lacking utility for clinical practice due to low correlation. [ABSTRACT FROM AUTHOR]