Advancing non-invasive melanoma diagnostics with deep learning and multispectral photoacoustic imaging.

ABSTRACT: The incidence of melanoma is rising and will require more efficient diagnostic procedures to meet a growing demand. Excisional biopsy and histopathology is still the standard, which often requires multiple surgical incisions with increasing margins due inaccurate visual assessment of where the melanoma borders to healthy tissue. This challenge stems, in part, from the inability to reliably delineate the melanoma without visually inspecting chemically stained histopathological cross-sections. Spectroscopic imaging have shown promise to non-invasively characterize the molecular composition of tissue and thereby distinguish melanoma from healthy tissue based on spectral features. In this work we describe a computational framework applied to multispectral photoacoustic (PA) imaging data of mel

SUBMITTER: Merdasa A 

PROVIDER: S-EPMC12272440 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature