Low incidence of heparin-induced skin lesions in orthopedic surgery patients with low-molecular-weight heparins.

BACKGROUND Heparins are widely prescribed for prevention and therapy of arterial and venous thromboembolic diseases. Heparin-induced skin lesions are the most frequent adverse effect of subcutaneous heparin treatment in non-surgical patients (7.5%-39.8%); no data exist on surgical patients. Commonly, they are due to a delayed-type hypersensitivity reaction (DTH), but may also be a manifestation of life-threatening heparin-induced thrombocytopenia (HIT). Lesions of both entities resemble initially. The risk of HIT is highest among heparin-anticoagulated orthopedic surgery patients. OBJECTIVE To determine incidence and causes of heparin-induced skin lesions in major orthopedic surgery patients. METHODS In a prospective cohort study, consecutive patients with subcutaneous low-molecular-weight heparin (LMWH) treatment were examined for cutaneous adverse effects. Further diagnostics (skin biopsy, clinical/laboratory assessment for thrombosis, bleeding, HIT, cross-allergies) were performed. RESULTS Six of 316 enrolled patients (1.9%; 95% CI: 0.4%-3.4%) developed heparin-induced skin lesions. All were caused by a DTH reaction, and none was due to HIT or other rare heparin-associated skin diseases. Therapeutic use (dosage) of LMWH was identified as only risk factor (odds ratio: 3.1, 95% CI: 1.4-4.9; P = .00141). In addition to DTH, 5 thromboembolic, 4 major bleeding complications but no cases of HIT or cross-allergies were observed. CONCLUSIONS AND CLINICAL RELEVANCE Orthopedic surgery patients have-unlike non-surgical patients-a low risk for heparin-induced skin lesions during LMWH treatment; all lesions were due to a DTH reaction. The risk for DTH differs considerably between individual patient cohorts. No association with HIT was observed. These data help to tailor anticoagulatory treatment individually and to increase patient safety

Erratum: Contact sensitization to plants of the Compositae family: Data of the Information Network of Departments of Dermatology (IVDK) from 2007 to 2016 (vol 80, pg 222, 2019)

Baron JM, Grabbe J, Ludwig A, et al. Erratum: Contact sensitization to plants of the Compositae family: Data of the Information Network of Departments of Dermatology (IVDK) from 2007 to 2016 (vol 80, pg 222, 2019). Contact Dermatitis. 2019;80(6):415

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

BACKGROUND: Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy. METHODS: We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics. FINDINGS: The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%-100%) and 60% (range 21.3%-91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%-91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%-95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%. INTERPRETATION: A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity

A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task

Background: Recent studies have demonstrated the use of convolutional neural networks (CNNs) to classify images of melanoma with accuracies comparable to those achieved by board-certified dermatologists. However, the performance of a CNN exclusively trained with dermoscopic images in a clinical image classification task in direct competition with a large number of dermatologists has not been measured to date. This study compares the performance of a convolutional neuronal network trained with dermoscopic images exclusively for identifying melanoma in clinical photographs with the manual grading of the same images by dermatologists. Methods: We compared automatic digital melanoma classification with the performance of 145 dermatologists of 12 German university hospitals. We used methods from enhanced deep learning to train a CNN with 12,378 open-source dermoscopic images. We used 100 clinical images to compare the performance of the CNN to that of the dermatologists. Dermatologists were compared with the deep neural network in terms of sensitivity, specificity and receiver operating characteristics. Findings: The mean sensitivity and specificity achieved by the dermatologists with clinical images was 89.4% (range: 55.0%-100%) and 64.4% (range: 22.5%-92.5%). At the same sensitivity, the CNN exhibited a mean specificity of 68.2% (range 47.5%-86.25%). Among the dermatologists, the attendings showed the highest mean sensitivity of 92.8% at a mean specificity of 57.7%. With the same high sensitivity of 92.8%, the CNN had a mean specificity of 61.1%. Interpretation: For the first time, dermatologist-level image classification was achieved on a clinical image classification task without training on clinical images. The CNN had a smaller variance of results indicating a higher robustness of computer vision compared with human assessment for dermatologic image classification tasks. (C) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)