Dermatoskopie, sequenzielle Videodermatoskopie, Ganzkörperfotografie, künstliche Intelligenz – was bringt uns die Zukunft?

Zusammenfassung

Die Dermatoskopie ist mittlerweile ein fester Bestandteil einer jeden dermatologischen Untersuchung. Mit dieser Technik konnten die Früherkennung von Malignomen der Haut und deren Vorstufen deutlich verbessert und unnötige Exzisionen benigner Hautläsionen reduziert werden. Der Einsatz der Dermatoskopie umfasst mittlerweile auch die Beurteilung von entzündlichen und parasitären Dermatosen sowie Haarerkrankungen. Bei definierten Risikogruppen für ein Melanom sollte der Zwei-Schritte-Algorithmus, bestehend aus Ganzkörperfotografie und Videodermatoskopie, in entsprechenden Zeitintervallen zur Anwendung kommen. Mit dieser Methode können Melanome meist in einem sehr frühen Stadium entdeckt werden, da Veränderungen bestehender Muttermale sowie neu entstandene Läsionen früher festgestellt werden. In den letzten Jahren konnten mehrere Studien nachweisen, dass speziell geschulte neuronale Netzwerke Malignome der Haut, insbesondere Melanome, mit einer sehr hohen Treffsicherheit diagnostizieren können. Wie diese Computer-gestützte Diagnostik mithilfe einer künstlichen Intelligenz in den klinischen Alltag integriert werden kann und welche Vorteile sich hiervon ableiten lassen, ist derzeit noch nicht geklärt.

Abstract

Dermoscopy is an essential part in every dermatological consultation. This technique improved sensitivity and specificity in terms of early recognition of malignant skin tumors dramatically. Moreover, excisions of benign tumors were reduced. To date, dermoscopy is also used in the diagnosis of non-melanocytic skin tumors as well as for diagnosing inflammatory and parasitic dermatoses as well as hair diseases. The two-step algorithm consisting of total-body photography and sequential digital dermoscopy is a valuable tool for patients with an increased risk for developing a melanoma. These patients benefit from this method as melanomas are mostly recognised at an early stage, because dynamic changes in already existing lesions as well as newly developed lesions are observable earlier. There is growing evidence that specifically trained convolutional neural networks (CNN) are able to diagnose melanomas with a high diagnostic accuracy. The real-world applicability of this computer-aided diagnosis, its integration and advantages in the daily routine are currently a matter of debate.

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