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Science behind the Spectra-Scope®

There were many trials to help physicians positively confirm skin cancer more efficiently and faster, and most of them use image comparison algorithm-based technology, which is still inaccurate. Unlike these other trials, Spectra-Scope® utilizes biochemical information induced by the laser spectrum, then provide quantitative data based on the collected database on skin lesion with the deep-learning AI algorithm.


The Laser-Induced Plasma Spectroscopy

Biochemical analysis of a skin lesion in an instant

The Laser-Induced Plasma Spectroscopy (LIPS) of Spectra-Scope® uses laser irradiation of a few nanoseconds (equals to 10 ^-9 seconds) to induce micro plasma on the skin tissue with no tissue damage. The micro plasma emissions created by the laser instantly reveals the biochemical information of the skin tissue at the molecular and atomic levels. This is very critical information to determine if the lesion is skin cancer, but hard to interpret without huge and multi-dimensional computing power.

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Deep Learning

Deep Learning AI-based skin cancer detection algorithm for unrivaled accuracy

Each emission spectrum of skin tissue from the LIPS has over 4000 parameters of detailed biochemical information of the tissue. These spectral patterns of the skin tissue's emission vary under different medical conditions. Using Spectra-Scope ®'s deep learning-based proprietary diagnostic algorithm, BCC, SCC, and malignant melanoma are differentiated with high accuracy. The diagnostic algorithm was constructed with AI with in-depth neural network training using a total of 5302 emission spectra.