Press Release: Claris Group Identify Suspicious Skin Lesions with Spectroscopy Tools
Our recent press release on Infonews discussed how spectroscopy is used to diagnose skin cancer. Read the release below or view it here.
Computer scientists at Stanford University recently created an artificially intelligent diagnosis algorithm for skin cancer, making a database of 130,000 skin disease images, and trained their algorithm to visually diagnose potential cancer. From the very first test it performed with inspiring accuracy.
Diagnosing skin cancer begins with a visual examination. A dermoscopist usually looks at the suspicious lesion with the naked eye and with the aid of a dermatoscope, which is a handheld microscope that provides low-level magnification of the skin. If these methods are inconclusive or lead the dermoscopist to believe the lesion is cancerous, a biopsy is the next step.
Bringing the algorithm into the examination process follows a trend in computing that combines visual processing with deep learning, a type of artificial intelligence modeled after neural networks in the brain. Deep learning has a decades-long history in computer science but it only recently has been applied to visual processing tasks, with great success. The essence of machine learning, including deep learning, is that a computer is trained to figure out a problem rather than having the answers programmed into it.
“We made a very powerful machine learning algorithm that learns from data,” said Andre Esteva, co-lead author of the paper and a graduate student in the Thrun lab at Stanford. “Instead of writing into computer code exactly what to look for, you let the algorithm figure it out.” The algorithm was fed each image as raw pixels with an associated disease label. Compared to other methods for training algorithms, this one requires very little processing or sorting of the images prior to classification, allowing the algorithm to work off a wider variety of data.
Claris Group’s skin checks begin with a detailed overview of your skin. This is followed by a thorough head-to-toe examination with a dermatoscope. Full Body Photography and identification of moles and lesions are recorded with FotoFinder ATBM and Dermengine software technology. A more in depth mole analysis is performed with MoleMate SIMSYS technology an artificial intelligence system that uses spectroscopy to make a more detail skin analysis of each layer of the skin, capturing early signs of skin cancer.
The Claris Group team will clearly explain any findings and recommend the best course of action going forward. They will also educate you on the self-examination of your skin – a skill that could potentially save your life, so for more information on skin cancer clinics, skin doctors and skin centres please go to http://claris.co.nz.