Dermoscopy of Pitted Keratolysis

Irritated hyperhidrotic soles with multiple small pits are pathognomonic for pitted keratolysis (PK). Here we show the dermatoscopic view of typical pits that can ensure the diagnosis. PK is a plantar infection caused by Gram-positive bacteria, particularly Corynebacterium. Increases in skin surface pH, hyperhidrosis, and prolonged occlusion allow these bacteria to proliferate. The diagnosis is fundamentally clinical and treatment generally consists of a combination of hygienic measures, correcting plantar hyperhidrosis and topical antimicrobials

Giant Cardiac Fibroma: An Unusual Cause of Failure to Thrive

Cardiac fibromas are extremely rare in the general pediatric population and may present with a wide spectrum of clinical signs, including life-threatening arrhythmias and sudden death. We report a 14-month-old boy who presented with failure to thrive as the only symptom. Echocardiography showed a large cardiac fibroma in the right ventricle. Cardiac magnetic resonance imaging confirmed the diagnosis. After complete surgical tumor resection, the boy showed normal catch-up growth. This case underlines the diversity of clinical features of cardiac tumors, which implies that they should be considered early in the differential diagnosis of infants with failure to thriv

Skin Detachment and Regrowth in Toxic Epidermal Necrolysis

Toxic epidermal necrolysis is a rare but clinically well-described dermatological pathology. However, clinical pictures of this disorder in text books do not reflect its dynamic evolution. Usually, the desquamative post-bullous stage is represented, neglecting the initial bullous stage as well as the skin healing. With one clinical case, we provide a day-after-day illustration of the evolution of a patient suffering from toxic epidermal necrolysis. During one month, a skin area of a limb was regularly photo-documented

Augmented and virtual reality in dermatology : where do we stand and what comes next?

As the skin is an accessible organ and many dermatological diagnostics still rely on the visual examination and palpation of the lesions, dermatology could be dramatically impacted by augmented and virtual reality technologies. If the emergence of such tools raised enormous interest in the dermatological community, we must admit that augmented and virtual reality have not experienced the same breakthrough in dermatology as they have in surgery. In this article, we investigate the status of such technologies in dermatology and review their current use in education, diagnostics, and dermatologic surgery; additionally, we try to predict how it might evolve in the near future

Robust T-Loss for Medical Image Segmentation

This paper presents a new robust loss function, the T-Loss, for medical image segmentation. The proposed loss is based on the negative log-likelihood of the Student-t distribution and can effectively handle outliers in the data by controlling its sensitivity with a single parameter. This parameter is updated during the backpropagation process, eliminating the need for additional computation or prior information about the level and spread of noisy labels. Our experiments show that the T-Loss outperforms traditional loss functions in terms of dice scores on two public medical datasets for skin lesion and lung segmentation. We also demonstrate the ability of T-Loss to handle different types of simulated label noise, resembling human error. Our results provide strong evidence that the T-Loss is a promising alternative for medical image segmentation where high levels of noise or outliers in the dataset are a typical phenomenon in practice. The project website can be found at https://robust-tloss.github.ioComment: Early accepted to MICCAI 202

A Report of Two Cases of Solid Facial Edema in Acne

Solid facial edema (SFE) is a rare complication of acne vulgaris. To examine the clinical features of acne patients with solid facial edema, and to give an overview on the outcome of previous topical and systemic treatments in the cases so far published.; We report two cases from Switzerland, both young men with initially papulopustular acne resistant to topical retinoids.; Both cases responded to oral isotretinoin, in one case combined with oral steroids. Our cases show a strikingly similar clinical appearance to the cases described by Connelly and Winkelmann in 1985 (Connelly MG, Winkelmann RK. Solid facial edema as a complication of acne vulgaris. Arch Dermatol. 1985;121(1):87), as well as to cases of Morbihan's disease that occurs as a rare complication of rosacea.; Even 30 years after, the cause of the edema remains unknown. In two of the original four cases, a potential triggering factor was identified such as facial trauma or insect bites; however, our two patients did not report such occurrencies. The rare cases of solid facial edema in both acne and rosacea might hold the key to understanding the specific inflammatory pattern that creates both persisting inflammation and disturbed fluid homeostasis which can occur as a slightly different presentation in dermatomyositis, angioedema, Heerfordt's syndrome and other conditions

Giant Cardiac Fibroma: An Unusual Cause of Failure to Thrive

Cardiac fibromas are extremely rare in the general pediatric population and may present with a wide spectrum of clinical signs, including life-threatening arrhythmias and sudden death. We report a 14-month-old boy who presented with failure to thrive as the only symptom. Echocardiography showed a large cardiac fibroma in the right ventricle. Cardiac magnetic resonance imaging confirmed the diagnosis. After complete surgical tumor resection, the boy showed normal catch-up growth. This case underlines the diversity of clinical features of cardiac tumors, which implies that they should be considered early in the differential diagnosis of infants with failure to thrive

HautTief Multidisciplinary Educational Program for Patients with Psoriasis or Atopic Dermatitis: A Randomized Controlled Study

BACKGROUND Improving health-related quality of life (HRQoL), disease severity, and treatment adherence through patient education is an increasingly important, yet relatively new area in dermatology. This randomized controlled trial aims to contribute to this growing area of research by exploring the effects of a 9-week educational program for patients with chronic skin diseases. OBJECTIVE The aim of the study was to evaluate the effect of a multidisciplinary educational program on HRQoL and disease severity in patients with psoriasis or atopic dermatitis (AD). METHODS Sixty-four patients with diagnosed psoriasis or AD were recruited from University Hospital Zurich and randomized (1:1) to the intervention or control group. To assess HRQoL, the following self-reported questionnaires were used: Dermatology Life Quality Index (DLQI), Skindex-29, EuroQol-5D (EQ-5D), RAND 36-Item Short Form Survey (SF-36), and Beck Depression Inventory (BDI) to measure depression symptoms. Psoriasis Area and Severity Index (PASI) and the Eczema Area and Severity Index (EASI) were used to capture disease extent. These scores were assessed at four study visits, which were performed at baseline and 3, 6, and 9 months after the start of the program. RESULTS At month 6, an improvement of at least 25% in BDI was recorded in 15 (68.2%) of 22 patients in the intervention group and 6 (27.3%) of 22 patients in the control group (difference 40.9%, p = 0.016). 53.3% (16 of 30) of patients achieved an improvement in one subdomain of the SF-36 score (role limitations due to emotional problems) at 6-month follow-up, compared with 23.1% (6 of 26) of those not attending the educational program (difference 30.2%; p = 0.042). No significant differences in DLQI, Skindex-29, EQ-5D, PASI, and EASI between both groups at the three time points were found. CONCLUSION An educational program may improve HRQoL and depression status of patients with psoriasis or AD

SelfClean: A Self-Supervised Data Cleaning Strategy

Most benchmark datasets for computer vision contain irrelevant images, near duplicates, and label errors. Consequently, model performance on these benchmarks may not be an accurate estimate of generalization capabilities. This is a particularly acute concern in computer vision for medicine where datasets are typically small, stakes are high, and annotation processes are expensive and error-prone. In this paper we propose SelfClean, a general procedure to clean up image datasets exploiting a latent space learned with self-supervision. By relying on self-supervised learning, our approach focuses on intrinsic properties of the data and avoids annotation biases. We formulate dataset cleaning as either a set of ranking problems, which significantly reduce human annotation effort, or a set of scoring problems, which enable fully automated decisions based on score distributions. We demonstrate that SelfClean achieves state-of-the-art performance in detecting irrelevant images, near duplicates, and label errors within popular computer vision benchmarks, retrieving both injected synthetic noise and natural contamination. In addition, we apply our method to multiple image datasets and confirm an improvement in evaluation reliability

Towards Reliable Dermatology Evaluation Benchmarks

Benchmark datasets for digital dermatology unwittingly contain inaccuracies that reduce trust in model performance estimates. We propose a resource-efficient data cleaning protocol to identify issues that escaped previous curation. The protocol leverages an existing algorithmic cleaning strategy and is followed by a confirmation process terminated by an intuitive stopping criterion. Based on confirmation by multiple dermatologists, we remove irrelevant samples and near duplicates and estimate the percentage of label errors in six dermatology image datasets for model evaluation promoted by the International Skin Imaging Collaboration. Along with this paper, we publish revised file lists for each dataset which should be used for model evaluation. Our work paves the way for more trustworthy performance assessment in digital dermatology.Comment: Link to the revised file lists: https://github.com/Digital-Dermatology/SelfClean-Revised-Benchmark