A non-invasive device for skin cancer diagnosis: first clinical evidence with spectroscopic data enhanced by machine learning algorithms

Abstract

Skin cancer represents a significant global health concern, with melanoma alone accounting for thousands of deaths annually. Early diagnosis is crucial for improving survival rates and reducing healthcare costs. While traditional diagnostic approaches involve visual inspection followed by biopsy, emerging technologies offer less invasive options with improved precision. In this study, a novel non-invasive device was designed, developed, and validated to employ near-infrared reflectance spectroscopy for skin lesion analysis. Furthermore, this work presents a machine learning approach aimed at classifying different types of skin lesions, as well as a new sequential approach to distinguish benign from malignant lesions based on spectral data and exploring the impact of anamnestic features. The device was used in two independent hospitals in Italy to collect data from 69 patients in total, including various types of skin lesions, all of whom followed the standard protocol for screening and diagnosis intervention. The implemented model achieved a recall of 93.8% and an accuracy of 75% for melanoma and benign classification, and a recall of 100% and an accuracy of 98.6% in distinguishing non-melanoma cancer from benign lesions, demonstrating promising results for skin cancer diagnosis utilizing spectral and anamnestic data. In summary, this study contributes to the development of allied non-invasive diagnostic tools and underscores the potential of machine learning in dermatology using spectroscopic data.