Comparison of the Diagnostic Accuracy of Teledermoscopy, Face-to-Face Examinations and Artificial Intelligence in the Diagnosis of Melanoma

Abstract

Background: Rapid diagnosis of melanoma is necessary for a good prognosis. Using teledermatology and artificial intelligence for this issue is developing, but its diagnostic accuracy is less measured in a clinical setting. Objective: The purpose of this study was to assess the diagnostic accuracy of the teledermoscopy method using the FotoFinder device as well as the Moleanalyzer Pro artificial intelligence (AI) Assistant and to compare them with the face-to-face clinical examination for the diagnosis of melanoma confirmed with histopathology. Methods: Thirty melanocytic moles of 29 patients were included in the study. Each mole was assessed face-to-face, using FotoFinder teledermoscopy and Moleanalyzer Pro software methods. The results obtained from each method were compared with the results of the gold standard (pathology). The sensitivity and specificity of the three methods were calculated for malignant and borderline versus benign lesions. Inter-method reliability between a gold standard and other methods was evaluated using per cent agreement and Cohen’s kappa coefficient. Results: Five moles had a histopathological diagnosis of melanoma, and six and 19 moles were diagnosed as borderline and benign, respectively. Sensitivities and specificities were, respectively, as follows: face-to-face (90.9%, 57.9%), FotoFinder teledermoscopy (63.6%, 78.9%), FotoFinder® Moleanalyzer Pro (36.4%, 42.1%). Agreement with biopsy-obtained diagnosis categories of benign, borderline and malignant for face-to-face was 63.33%, FotoFinder teledermoscopy 73.33%, and FotoFinder® Moleanalyzer Pro 40%. Conclusions: Teledermoscopy had the highest agreement with reference diagnosis as well as the highest specificities that caused a reduction of biopsy referrals. The FotoFinder® Moleanalyzer Pro had the lowest agreement. Therefore, it cannot replace dermatologist decision making.