BACKGROUND: A recently introduced dermatoscopic method for diagnosis of early lentigo maligna (LM) is based on the absence of prevalent patterns of pigmented actinic keratosis (PAK) and solar lentigo/flat seborrheic keratosis (SL/SK). We term this the "inverse approach" OBJECTIVE: To determine whether training on the inverse approach increases the diagnostic accuracy of readers as compared to classic pattern analysis.METHODS: We used clinical and dermatoscopic images of histopathologically diagnosed LMs, PAKs and SLs/SKs. Participants of a dermatoscopy masterclass classified the lesions at baseline, after training on pattern analysis and the inverse approach. We compared their diagnostic performance among the 3 time points and to that of a trained convolutional neural network (CNN).RESULTS: The mean sensitivity for LM without training was 51.5%, after training on pattern analysis increased to 56.7% and after learning the inverse approach to 83.6%. The mean proportion of correct answers at the 3 time points was 62.1%, 65.5% and 78.5%. The percentage of readers outperforming the CNN was 6.4%, 15.4% and 53.9%, respectively.LIMITATIONS: The experimental setting and the inclusion of histopathologically diagnosed lesions only.CONCLUSIONS: The inverse approach, added to the classic pattern analysis, significantly improves the sensitivity of human readers for early LM diagnosis.