Although deep learning algorithms have demonstrated expert-level performance, previous efforts were mostly binary classifications of limited disorders. We trained an algorithm with 220,680 images of 174 disorders and validated it using Edinburgh (1,300 images; 10 disorders) and SNU datasets (2,201 images; 134 disorders). The algorithm could accurately predict malignancy, suggest primary treatment options, render multi-class classification among 134 disorders, and improve the performance of medical professionals. The area under the curves for malignancy detection were 0.928 AE 0.002 (Edinburgh) and 0.937 AE 0.004 (SNU). The area under the curves of primary treatment suggestion (SNU) were 0.828 AE 0.012, 0.885 AE 0.006, 0.885 AE 0.006, and 0.918 AE 0.006 for steroids, antibiotics, antivirals, and antifungals, respectively. For multi-class classification, the mean top-1 and top-5 accuracies were 56.7 AE 1.6% and 92.0 AE 1.1% (Edinburgh) and 44.8 AE 1.2% and 78.1 AE 0.3% (SNU), respectively. With the assistance of our algorithm, the sensitivity and specificity of 47 clinicians (21 dermatologists and 26 dermatology residents) for malignancy prediction (SNU; 240 images) were improved by 12.1% (P < 0.0001) and 1.1% (P < 0.0001), respectively. The malignancy prediction sensitivity of 23 non-medical professionals was significantly increased by 83.8% (P < 0.0001). The top-1 and top-3 accuracies of four doctors in the multi-class classification of 134 diseases (SNU; 2,201 images) were increased by 7.0% (P ¼ 0.045) and 10.1% (P ¼ 0.0020), respectively. The results suggest that our algorithm may serve as augmented intelligence that can empower medical professionals in diagnostic dermatology.

BackgroundSeverity grading is important for the assessment of psoriasis treatment efficacy. This is most commonly achieved by using the psoriasis area and severity index (PASI), a subjective tool with inherent inter-rater and intra-rater variability. PASI-naive dermatologists require training to properly conduct a PASI assessment.ObjectiveIn the present study, we aimed to investigate whether photographic training improves inter-rater and intra-rater variabilities. We also determined which PASI component has the greatest impact on variability.MethodsTwenty-one dermatologists received 1 hour of PASI training. They were tested before and after the training to evaluate intra-rater variability. The physicians were further tested after training by using a reference photograph.ResultsThe mean of each PASI component was underevaluated compared with scoring by a PASI expert. The concordance rate with the expert's grading was highest for thickness followed by erythema, scaling, and area. The scaling score showed the greatest improvement after training. After training, the distribution of deviation from the expert's grading, which signifies inter-rater variability, improved only for the PASI area component. The deviation of scaling grading improved upon retesting by using a reference photograph.ConclusionPASI assessment training improved variabilities to some degree but not for every PASI component. The development of an objective psoriasis severity assessment tool will help overcome the subjective variabilities in PASI assessment, which can never be completely eliminated via training.

Digital mucous cysts (DMC) are common benign myxoid cysts typically located on the fingers and toes. Recently, dermoscopic patterns of DMC were reported. However, only a small number of cases were described in the published work; therefore, information on this topic is scarce in the published work. We investigated dermoscopic patterns of histopathologically diagnosed DMC. In total, 23 cases were enrolled in this study. Polarized dermoscopy revealed vascular patterns in 13 cases (56.5%), with arborizing vascular patterns, dotted vessels, linear vessels and polymorphous vessels in eight (34.8%), three (13.0%), one (4.3%) and one (4.3%) case, respectively. Red-purple lacunas, ulceration, nail dystrophy and white shiny structures were detected in five (21.7%), two (8.7%), seven (30.4%) and six cases (26.1%), respectively. We report the largest case series regarding dermoscopic features of DMC to date. Dermoscopy can be used as a helpful adjuvant and non-invasive tool in the diagnosis of DMC.

Summary Background and objectives Conventional treatment options for eyelid fat bulging are generally limited to surgical approaches. However, many attempts have been made recently to manage this disfigurement using non‐surgical interventions. The purpose of this study was to evaluate the efficacy and safety of a micro‐insulated needle radiofrequency system for the treatment of lower eyelid fat bulging. Methods This is a single center pre‐post comparative study. Twenty‐two subjects with lower eyelid fat bulging were treated twice using the needle radiofrequency system, at an interval of four weeks. Two types of partially insulated needles with different lengths were used in each session. A three‐dimensional photogrammetry system was used to objectively measure changes in the extent of the fat bulge. The investigator's global assessment (IGA) of the severity of fat bulging was also evaluated. Results The average extent of fat bulging was decreased significantly after twelve weeks, and was maintained until 24 weeks. The IGA score was significantly decreased after four weeks and further decreased after twelve weeks, and then maintained until 24 weeks. There were no side effects, except for lower eyelid swelling and bruising that lasted for about a week. Conclusion The micro‐insulated needle radiofrequency system can be a beneficial and well‐tolerated treatment for lower eyelid fat bulging.

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