AI System Analyzes Wide-Field Images of Skin of Melanoma Patients
Written by AZoRoboticsApr 5 2021
Melanoma is a kind of malignant tumor that causes over 70% of all skin cancer-related deaths across the world. For several years, physicians have depended on visual inspection to determine suspicious pigmented lesions (SPLs), which could indicate the occurrence of skin cancer.
Image Credit: Animation courtesy of the researchers.
In primary care settings, such early-stage determination of SPLs can enhance melanoma prognosis and considerably reduce the treatment cost. The difficulty is that it is challenging to find and prioritize SPLs quickly. This is because of the high volume of pigmented lesions that must usually be assessed for potential biopsies.
An artificial intelligence tool that can help detect melanoma
April 5, 2021MIT
Melanoma is a type of malignant tumor responsible for more than 70 percent of all skin cancer-related deaths worldwide. For years, physicians have relied on visual inspection to identify suspicious pigmented lesions (SPLs), which can be an indication of skin cancer. Such early-stage identification of SPLs in primary care settings can improve melanoma prognosis and significantly reduce treatment cost.
The challenge is that quickly finding and prioritizing SPLs is difficult, due to the high volume of pigmented lesions that often need to be evaluated for potential biopsies. Now, researchers from MIT and elsewhere have devised a new artificial intelligence pipeline, using deep convolutional neural networks (DCNNs) and applying them to analyzing SPLs through the use of wide-field photography common in most smartphones and personal cameras.