Publications by authors named "Elisabeth V Goessinger"
Article Synopsis
- * Results showed a significant reduction in the Psoriasis Area and Severity Index (PASI) after 3 and 18 months, with many patients achieving low PASI scores, indicating effective treatment.
- * Improvements in quality of life were noted, and no patients discontinued treatment due to adverse events, suggesting that tildrakizumab has a strong safety profile in a real-world setting.
Psoriasis, a chronic inflammatory skin disease, affects millions of people worldwide. It imposes a significant burden on patients' quality of life and healthcare systems, creating an urgent need for optimized diagnosis, treatment, and management. In recent years, image-based artificial intelligence (AI) applications have emerged as promising tools to assist physicians by offering improved accuracy and efficiency.
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- Early detection and removal of melanoma (a type of skin cancer) are super important because it's becoming more common and dangerous.
- A study was done using a special camera that can see deeper into the skin to help doctors figure out whether skin spots are cancerous.
- The new method using direct light was way better, finding all the bad spots (100% sensitivity) compared to previous methods, showing this technique could help catch skin cancer earlier.
AI image classification algorithms have shown promising results when applied to skin cancer detection. Most public skin cancer image datasets are comprised of dermoscopic photos and are limited by selection bias, lack of standardization, and lend themselves to development of algorithms that can only be used by skilled clinicians. The SLICE-3D ("Skin Lesion Image Crops Extracted from 3D TBP") dataset described here addresses those concerns and contains images of over 400,000 distinct skin lesions from seven dermatologic centers from around the world.
View Article and Find Full Text PDFBackground: Artificial intelligence (AI) shows promising potential to enhance human decision-making as synergistic augmented intelligence (AuI), but requires critical evaluation for skin cancer screening in a real-world setting.
Objectives: To investigate the perspectives of patients and dermatologists after skin cancer screening by human, artificial and augmented intelligence.
Methods: A prospective comparative cohort study conducted at the University Hospital Basel included 205 patients (at high-risk of developing melanoma, with resected or advanced disease) and 8 dermatologists.
Introduction: The worldwide incidence of melanoma has been increasing rapidly in recent decades with Switzerland having one of the highest rates in Europe. Ultraviolet (UV) radiation is one of the main risk factors for skin cancer. Our objective was to investigate UV protective behavior and melanoma awareness in a high-risk cohort for melanoma.
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