A5048821166- Cutaneous Melanoma Detection and Management
- Cutaneous lymphoproliferative disorders research
- Genetic and rare skin diseases.
- Nonmelanoma Skin Cancer Studies
- Cancer and Skin Lesions
- Dermatological and Skeletal Disorders
- Nail Diseases and Treatments
- Tumors and Oncological Cases
- Autoimmune and Inflammatory Disorders
- Eosinophilic Disorders and Syndromes
- AI in cancer detection
- Autoimmune Bullous Skin Diseases
- Sarcoma Diagnosis and Treatment
- Histiocytic Disorders and Treatments
- Vascular Tumors and Angiosarcomas
- Skin and Cellular Biology Research
- Cancer Diagnosis and Treatment
- Dermatologic Treatments and Research
- Urticaria and Related Conditions
- Cell Image Analysis Techniques
- Full-Duplex Wireless Communications
- Skin Diseases and Diabetes
- Polyomavirus and related diseases
- Chronic Lymphocytic Leukemia Research
- Inflammatory Myopathies and Dermatomyositis
University Hospital Heidelberg
2016-2025
Heidelberg University
2016-2025
Hautklinik Heidelberg
2016-2025
Chirurgische Universitätsklinik Heidelberg
2016-2025
Klinikum Konstanz
2024
University Medical Center
2017-2022
National Center for Tumor Diseases
2019-2021
University Hospital and Clinics
2017-2019
Université de Lyon
2018
German Cancer Research Center
2011-2013
Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to larger groups of dermatologists are lacking.Google's Inception v4 CNN architecture was trained and validated using dermoscopic images corresponding diagnoses. In comparative cross-sectional reader study 100-image test-set used (level-I: dermoscopy only; level-II: plus clinical information images). Main outcome measures were sensitivity, specificity area...
<h3>Importance</h3> Deep learning convolutional neural networks (CNNs) have shown a performance at the level of dermatologists in diagnosis melanoma. Accordingly, further exploring potential limitations CNN technology before broadly applying it is special interest. <h3>Objective</h3> To investigate association between gentian violet surgical skin markings dermoscopic images and diagnostic approved for use as medical device European market. <h3>Design Setting</h3> A cross-sectional analysis...
Highlights•A market-approved convolutional neural network (CNN) trained on dermoscopic images was tested against 96 dermatologists.•Test data included a broad range of skin lesions and compiled from external sources not involved in CNN training.•Dermatologists indicated their management decisions after reviewing clinical, dermoscopic, textual case information.•In this setting dermatologists performed par with the CNN's classifications based alone.AbstractBackgroundConvolutional networks...
Studies suggest that convolutional neural networks (CNNs) perform equally to trained dermatologists in skin lesion classification tasks. Despite the approval of first for clinical use, prospective studies demonstrating benefits human with machine cooperation are lacking.
Abstract Background Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity a CNN to differentiate melanomas from combined naevi, latter representing well‐known melanoma simulators, has not been investigated. Objective To assess diagnostic performance when used naevi comparison with dermatologists. Methods In this study, regulatory approval for European market (Moleanalyzer‐Pro, FotoFinder Systems GmbH, Bad Birnbach, Germany) was...
Summary Background and objectives Technical advances have allowed for significant improvements in imaging techniques recent years. Specifically, lesions can now be depicted at a much higher magnification – up to 400 x using optical super‐high dermoscopy (OSHMD). Patients methods This is retrospective, observational study assessing 99 melanocytic patients from the University Hospital Heidelberg. Dermoscopy (20 x) OSHMD images (90 x, 120 150 180 270 were acquired. assessed presence/absence of...
Summary Background and objectives Convolutional neural networks (CNN) enable accurate diagnosis of medical images perform on or above the level individual physicians. Recently, collective human intelligence (CoHI) was shown to exceed diagnostic accuracy individuals. Thus, performance CoHI (120 dermatologists) versus dermatologists two state‐of‐the‐art CNN investigated. Patients Methods Cross‐sectional reader study with presentation 30 clinical cases 120 dermatologists. Six diagnoses were...
Zusammenfassung Hintergrund und Ziele Technische Fortschritte haben in den letzten Jahren eine Verbesserung bildgebender Verfahren ermöglicht. Insbesondere können Läsionen nun mittels optischer ultrahochauflösender Dermatoskopie (OSHMD) mit einer sehr viel höheren Vergrößerung bis zu 400fach dargestellt werden. Patienten Methoden Es handelt sich um retrospektive Beobachtungsstudie, der 99 melanozytäre von des Universitätsklinikums Heidelberg untersucht wurden. wurden dermatoskopische (20 x)...
Abstract Necrolytic migratory erythema (NEM) is associated with glucagonoma, an endocrine malignancy of the pancreas. It a rare and likely underrecognized paraneoplastic dermatitis. A 38-year-old woman presented to our clinic 3-year history reocurring pruritic skin rashes increasing intensity. The lesions active annular borders, central scaling, postinflammatory hyperpigmentation, but also erosions, pustules, crusted lesions. Multiple biopsies were taken. workup patient revealed tumor...
Summary Background and objectives Combined nevi (CN) show two or more components of major nevus subtypes simulate melanomas. We investigated a panel dermoscopic features three algorithms for differentiating CN from Patients methods Retrospective, blinded case‐control study using images 36 melanoma controls. Twenty‐one validated the diagnosis melanocytic lesions, number colors, were (ABCD rule dermoscopy, Menzies scoring method, 7‐point checklist). Results Five seven indicative observed...
Total body photography for skin cancer screening is a well-established tool allowing documentation and follow-up of the entire surface. Artificial intelligence-based systems are increasingly applied automated lesion detection diagnosis.
In the recent World Health Organization-European Organisation for Research and Treatment of Cancer classification, primary cutaneous CD4+ small- to medium-sized pleomorphic T-cell lymphoma is listed as a provisional entity that histopathologically characterized by CD3+/CD4+/CD8-/CD30- T lymphocytes. Clinically, it solitary tumors mostly affecting head neck area an indolent clinical course with estimated 5-year survival about 60% 80%. Currently, therapeutic options include topical or systemic...
Summary Background and objectives Convolutional neural networks (CNN) have proven dermatologist‐level performance in skin lesion classification. Prior to a broader clinical application, an assessment of limitations is crucial. Therefore, the influence dark tubular periphery dermatoscopic images (also called corner artefact [DCA]) on diagnostic market‐approved CNN for classification was investigated. Patients methods A prospective image set 233 lesions (60 malignant, 173 benign) without DCA...
Metabolic reprogramming and altered gene expression mediated by hypoxia-inducible factors play crucial roles during tumour growth progression. Nevertheless, studies analysing the of factor-1α its downstream targets in Merkel cell carcinoma (MCC) are lacking but warranted to shed more light on MCC pathogenesis potentially provide new therapeutic options.To analyse immunohistochemical (HIF-1α), vascular endothelial factor-A (referred as VEGF throughout manuscript), receptor-2 (VEGFR-2),...