Browsing by Author "Combalia, Marc"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Expert Agreement on the Presence and Spatial Localization of Melanocytic Features in Dermoscopy
    (2024) Liopyris, Konstantinos; Navarrete-Dechent, Cristian; Marchetti, Michael A.; Rotemberg, Veronica; Apalla, Zoe; Argenziano, Giuseppe; Blum, Andreas; Braun, Ralph P.; Carrera, Cristina; Codella, Noel C. F.; Combalia, Marc; Dusza, Stephen W.; Gutman, David A.; Helba, Brian; Hofmann-Wellenhof, Rainer; Jaimes, Natalia; Kittler, Harald; Kose, Kivanc; Lallas, Aimilios; Longo, Caterina; Malvehy, Josep; Menzies, Scott; Nelson, Kelly C.; Paoli, John; Puig, Susana; Rabinovitz, Harold S.; Rishpon, Ayelet; Russo, Teresa; Scope, Alon; Soyer, H. Peter; Stein, Jennifer A.; Stolz, Willhelm; Sgouros, Dimitrios; Stratigos, Alexander J.; Swanson, David L.; Thomas, Luc; Tschandl, Philipp; Zalaudek, Iris; Weber, Jochen; Halpern, Allan C.; Marghoob, Ashfaq A.
    Dermoscopy aids in melanoma detection; however, agreement on dermoscopic features, including those of high clinical relevance, remains poor. In this study, we attempted to evaluate agreement among experts on exemplar images not only for the presence of melanocytic-specific features but also for spatial localization. This was a cross-sectional, multicenter, observational study. Dermoscopy images exhibiting at least 1 of 31 melanocytic-specific features were submitted by 25 world experts as exemplars. Using a web-based platform that allows for image markup of specific contrast-defined regions (superpixels), 20 expert readers annotated 248 dermoscopic images in collections of 62 images. Each collection was reviewed by five independent readers. A total of 4,507 feature observations were performed. Good-to-excellent agreement was found for 14 of 31 features (45.2%), with eight achieving excellent agreement (Gwet's AC >0.75) and seven of them being melanomaspecific features. These features were peppering/granularity (0.91), shiny white streaks (0.89), typical pigment network (0.83), blotch irregular (0.82), negative network (0.81), irregular globules (0.78), dotted vessels (0.77), and blue-whitish veil (0.76). By utilizing an exemplar dataset, a good-to-excellent agreement was found for 14 features that have previously been shown useful in discriminating nevi from melanoma. All images are public (www.isic-archive.com) and can be used for education, scientific communication, and machine learning experiments.
  • Thumbnail Image
    Item
    Position statement of the EADV Artificial Intelligence (AI) Task Force on AI-assisted smartphone apps and web-based services for skin disease
    (2024) Sangers, Tobias E.; Kittler, Harald; Blum, Andreas; Braun, Ralph P.; Barata, Catarina; Cartocci, Alessandra; Combalia, Marc; Esdaile, Ben; Guitera, Pascale; Haenssle, Holger A.; Kvorning, Niels; Lallas, Aimilios; Navarrete-Dechent, Cristian; Navarini, Alexander A.; Podlipnik, Sebastian; Rotemberg, Veronica; Soyer, H. Peter; Tognetti, Linda; Tschandl, Philipp; Malvehy, Josep
    Background: As the use of smartphones continues to surge globally, mobile applications (apps) have become a powerful tool for healthcare engagement. Prominent among these are dermatology apps powered by Artificial Intelligence (AI), which provide immediate diagnostic guidance and educational resources for skin diseases, including skin cancer.Objective: This article, authored by the EADV AI Task Force, seeks to offer insights and recommendations for the present and future deployment of AI-assisted smartphone applications (apps) and web-based services for skin diseases with emphasis on skin cancer detection.Methods: An initial position statement was drafted on a comprehensive literature review, which was subsequently refined through two rounds of digital discussions and meticulous feedback by the EADV AI Task Force, ensuring its accuracy, clarity and relevance.Results: Eight key considerations were identified, including risks associated with inaccuracy and improper user education, a decline in professional skills, the influence of non-medical commercial interests, data security, direct and indirect costs, regulatory approval and the necessity of multidisciplinary implementation. Following these considerations, three main recommendations were formulated: (1) to ensure user trust, app developers should prioritize transparency in data quality, accuracy, intended use, privacy and costs; (2) Apps and web-based services should ensure a uniform user experience for diverse groups of patients; (3) European authorities should adopt a rigorous and consistent regulatory framework for dermatology apps to ensure their safety and accuracy for users.Conclusions: The utilisation of AI-assisted smartphone apps and web-based services in diagnosing and treating skin diseases has the potential to greatly benefit patients in their dermatology journeys. By prioritising innovation, fostering collaboration and implementing effective regulations, we can ensure the successful integration of these apps into clinical practice.