project report Promise2007

Das Projekt Promise2007 befasste sich mit der Erstellung und Auswertung einer Statistik zur Mitgliedersituation im Berufsverband Medizinischer Informatiker e.V.. Mit dem Ziel mehr über die Mitglieder und ihre derzeitige Situation zu erfahren wurde das Projekt an der Fachhochschule Hannover initiiert. Statistisch erfasst wurden Fragen zum Beschäftigungsverhältnis, zu Aus- und Weiterbildung, der beruflichen Situation und persönliche Angaben. Die Ergebnisse wurden ausgewertet und daraus wichtige Erkenntnisse für den BVMI e.V. abgeleitet, welche auf die weitere Verbandsarbeit Einfluss nehmen

Measuring elastase, proteinase 3 and cathepsin G activities at the surface of human neutrophils with fluorescence resonance energy transfer substrates

The neutrophil serine proteases (NSPs) elastase, proteinase 3 and cathepsin G are multifunctional proteases involved in pathogen destruction and the modulation of inflammatory processes. A fraction of secreted NSPs remains bound to the external plasma membrane, where they remain enzymatically active. This protocol describes the spectrofluorometric measurement of NSP activities on neutrophil surfaces using highly sensitive Abz-peptidyl-EDDnp fluorescence resonance energy transfer (FRET) substrates that fully discriminate between the three human NSPs. We describe FRET substrate synthesis, neutrophil purification and handling, and kinetic experiments on quiescent and activated cells. These are used to measure subnanomolar concentrations of membrane-bound or free NSPs in low-binding microplates and to quantify the activities of individual proteases in biological fluids like expectorations and bronchoalveolar lavages. the whole procedure, including neutrophil purification and kinetic measurements, can be done in 4-5 h and should not be longer because of the lifetime of neutrophils. Using this protocol will help identify the contributions of individual NSPs to the development of inflammatory diseases and may reveal these proteases to be targets for therapeutic inhibitors.Alexander von Humboldt FoundationGerman Research CouncilVaincre la MucoviscidoseFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Proteases & Vectorisat Pulm Fac Med, INSERM, U618, F-37032 Tours, FranceMax Planck Inst Neurobiol, Dept Neuroimmunol, D-82152 Planegg Martinsried, GermanyUniv Tours, F-37032 Tours, FranceUniversidade Federal de São Paulo, Escola Paulista Med, Dept Biofis, BR-0404420 São Paulo, BrazilINSERM, U921, F-37032 Tours, FranceUniversidade Federal de São Paulo, Escola Paulista Med, Dept Biofis, BR-0404420 São Paulo, BrazilWeb of Scienc

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/)

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. (C) 2019 The Author(s). Published by Elsevier Ltd