AI accurately detects melanomas
AI algorithms can identify melanomas in dermoscopic images with an accuracy comparable to human specialists, according to research published in JAMA.
“When compared with other forms of skin cancer, malignant melanoma is relatively uncommon; however, the incidence of melanoma is increasing faster than any other form of cancer, and it is responsible for the majority of skin cancer deaths,” wrote lead author Michael Phillips, MMedSci, University of Western Australia, and colleagues.
The study included more than 1,500 images of skin lesions from more than 500 patients, who were all treated from January 2017 to July 2018 at one of seven hospitals in the U.K. Images of suspicious lesions captured by three different cameras—an iPhone 6s, Galaxy S6 and digital single-lens reflex (DSLR) camera—were included in the study. The team turned to Deep Ensemble for Recognition of Malignancy (DERM), an algorithm developed by Skin Analytics Limited using data from more than 7,000 images, to see how its performance compared to physicians.
Overall, the DERM algorithm achieved an area under the ROC curve (AUC) of 90.1% for biopsied skin lesions and 95.8% for all lesions captured by the iPhone 6s camera. It also achieved an AUROC of 85.8% for biopsied lesions and 93.8% for all lesions captured by the Galaxy S6 camera. For the DSLR camera, the AUC was 86.9% for biopsied lesions and 91.8% for all lesions. Physicians, meanwhile, achieved an AUC of 77.8% and a specificity of 69.9%.
“The findings of this diagnostic trial demonstrated that an AI algorithm, using different camera types, can detect melanoma with a similar level of accuracy as specialists,” the authors wrote. “The development of low-cost screening methods, such as AI-based services, could transform patient diagnosis pathways, enabling greater efficiencies throughout the healthcare service.”
Skin Analytics Limited funded this research.
Michael has more than 18 years of experience as a professional writer and editor. He has written at length about cardiology, radiology, artificial intelligence and other key healthcare topics.