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Abstract

Advanced dermoscopy in melanoma: from artificial intelligence to somatic mutation identification

Summary

Advanced dermatoscopy has revolutionized early detection of melanoma, improving the accuracy of diagnosing of this aggressive form of skin cancer. This approach not only allows for visual evaluation of skin lesions but has also expanded with emerging technologies, such as artificial intelligence (AI) and genomics, to address challenges in the early identification of melanoma. The use of AI in dermatoscopy has gained popularity due to its ability to analyze substantial amounts of image data and detect subtle patterns that may go unnoticed by the human eye. Deep learning algorithms have demonstrated accuracy comparable to that of experienced dermatologists. This enables faster and more accurate classification of suspicious lesions, aiding in clinical decision-making and reducing the need for unnecessary biopsies. On the other hand, the integration of dermatoscopy with the analysis of somatic mutations in melanoma has opened new avenues for personalized medicine. Advanced techniques allow for the identification of specific mutations, such as those in the BRAF or NRAS genes, which can guide targeted treatment and improve patient outcomes. By combining high-precision visual evaluation with genetic analysis, a more comprehensive and accurate approach to melanoma diagnosis and treatment in its advanced stages might be achieved.

Key words: melanoma, dermatoscopy, advanced dermatoscopy, artificial intelligence, somatic mutations