Sensitivity and specificity for detecting Merkel cell carcinoma and amelanotic melanoma was 79.4 and 37.7 percent, respectively<\/p>\n<\/h3>\n
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THURSDAY, Oct. 14, 2021 (HealthDay News) — A direct-to-consumer machine learning model for detecting skin cancers may not adequately recognize rare, but aggressive, skin cancers, according to a study presented at the annual meeting of the European Academy of Dermatology and Venereology, held virtually from Sept. 29 to Oct 2.<\/p>\n
Lloyd Steele, M.B.Ch.B., from the University of London, and colleagues assessed the performance of a machine learning model for Merkel cell carcinoma (MCC) and amelanotic melanoma. The performance of a direct-to-consumer model, which is available in Europe as a certified medical device, was assessed using a set of images that included 28 MCCs, 35 amelanotic melanomas, 28 seborrheic keratoses, and 25 hemangiomas.<\/p>\n
The researchers found that the direct-to-consumer app incorrectly classified five of 28 MCCs (17.9 percent) and seven of 35 amelanotic melanomas (22.9 percent) as low risk. Nearly two-thirds of benign lesions (62.2 percent) were classified as high risk. The model’s sensitivity for detecting malignancy was 79.4 percent, with a specificity of 37.7 percent.<\/p>\n
“In order to improve, machine learning model evaluations should consider the spectrum of diseases that will be seen in practice,” Steele said in a statement. “At the moment, most of the performance of those models is driven by the imaging data available, which is particularly scarce when it comes to rare skin cancers.”<\/p>\n