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• W4320857501 abstract "Background and objectives The incidence of skin cancer is rising worldwide and there is medical need to optimize its early detection. This study was conducted to determine the diagnostic and risk-assessment accuracy of two new diagnosis-based neural networks (analyze and detect), which comply with the CE-criteria, in evaluating the malignant potential of various skin lesions on a smartphone. Of note, the intention of our study was to evaluate the performance of these medical products in a clinical setting for the first time. Methods This was a prospective, single-center clinical study at one tertiary referral center in Graz, Austria. Patients, who were either scheduled for preventive skin examination or removal of at least one skin lesion were eligible for participation. Patients were assessed by at least two dermatologists and by the integrated algorithms on different mobile phones. The lesions to be recorded were randomly selected by the dermatologists. The diagnosis of the algorithm was stated as correct if it matched the diagnosis of the two dermatologists or the histology (if available). The histology was the reference standard, however, if both clinicians considered a lesion as being benign no histology was performed and the dermatologists were stated as reference standard. Results A total of 238 patients with 1171 lesions (86 female; 36.13%) with an average age of 66.19 (SD = 17.05) was included. Sensitivity and specificity of the detect algorithm were 96.4% (CI 93.94–98.85) and 94.85% (CI 92.46–97.23); for the analyze algorithm a sensitivity of 95.35% (CI 93.45–97.25) and a specificity of 90.32% (CI 88.1–92.54) were achieved. Discussion The studied neural networks succeeded analyzing the risk of skin lesions with a high diagnostic accuracy showing that they are sufficient tools in calculating the probability of a skin lesion being malignant. In conjunction with the wide spread use of smartphones this new AI approach opens the opportunity for a higher early detection rate of skin cancer with consecutive lower epidemiological burden of metastatic cancer and reducing health care costs. This neural network moreover facilitates the empowerment of patients, especially in regions with a low density of medical doctors. Registration Approved and registered at the ethics committee of the Medical University of Graz, Austria (Approval number: 30–199 ex 17/18)." @default.
• W4320857501 created "2023-02-16" @default.
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• W4320857501 date "2023-02-15" @default.
• W4320857501 modified "2023-10-18" @default.
• W4320857501 title "New AI-algorithms on smartphones to detect skin cancer in a clinical setting—A validation study" @default.
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• W4320857501 doi "https://doi.org/10.1371/journal.pone.0280670" @default.
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• W4320857501 hasPublicationYear "2023" @default.
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