Early detection of melanoma has long been considered critical to combatting skin cancer, but while providers have relied on visual inspection to identify suspicious pigmented lesions, which can be an indication of skin cancer, it can be difficult to determine which lesions are potentially dangerous due to the high volume of pigmented lesions that may require biopsies.

But according to a report recently published at Science Translational Medicine, researchers from MIT have tapped AI to facilitate the analysis of suspicious pigmented lesions, of SPLs, through the use of wide-field photography common in most smartphones and personal cameras.

According to MIT News, the process begins with a wide-field, smartphone camera image that shows large skin sections from a patient in a primary-care setting. “An automated system detects, extracts, and analyzes all pigmented skin lesions observable in the wide-field image. A pre-trained deep convolutional neural network (DCNN) determines the suspiciousness of individual pigmented lesions and marks them (yellow = consider further inspection, red = requires further inspection or referral to dermatologist). Extracted features are used to further assess pigmented lesions and to display results in a heatmap format.”

DCNNs are neural networks that can be used to classify images to then cluster them for further analysis.

“Our research suggests that systems leveraging computer vision and deep neural networks, quantifying such common signs, can achieve comparable accuracy to expert dermatologists,” explained Luis R. Soenksen, a postdoc and a medical device expert currently acting as MIT’s first Venture Builder in Artificial Intelligence and Healthcare. “We hope our research revitalizes the desire to deliver more efficient dermatological screenings in primary care settings to drive adequate referrals. Early detection of SPLs can save lives; however, the current capacity of medical systems to provide comprehensive skin screenings at scale are still lacking.”

This work was supported by Abdul Latif Jameel Clinic for Machine Learning in Health and by the Consejería de Educación, Juventud y Deportes de la Comunidad de Madrid through the Madrid-MIT M+Visión Consortium.

Using AI, the researchers trained the system using 20,388 wide-field images from 133 patients at the Hospital Gregorio Marañón in Madrid, as well as publicly available images. The images were taken with a variety of ordinary cameras that are readily available to consumers. 

According to MIT News, “(d)ermatologists working with the researchers visually classified the lesions in the images for comparison. They found that the system achieved more than 90.3 percent sensitivity in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds, by avoiding the need for cumbersome and time-consuming individual lesion imaging.