Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the early diagnosis of these conditions. A total of 724 dermoscopy images comprising acral melanoma (350 images from 81 patients) and benign nevi (374 images from 194 patients), and confirmed by histopathological examination, were analyzed in this study. To perform the 2-fold cross validation, we split them into two mutually exclusive subsets: half of the total image dataset was selected for training and the rest for testing, and we calculated the accuracy of diagnosis comparing it with the dermatologist’s and non-expert’s evaluation. The accuracy (percentage of true positive and true negative from all images) of the convolutional neural network was 83.51% and 80.23%, which was higher than the non-expert’s evaluation (67.84%, 62.71%) and close to that of the expert (81.08%, 81.64%). Moreover, the convolutional neural network showed area-under-the-curve values like 0.8, 0.84 and Youden’s index like 0.6795, 0.6073, which were similar score with the expert. Although further data analysis is necessary to improve their accuracy, convolutional neural networks would be helpful to detect acral melanoma from dermoscopy images of the hands and feet.

With the recognition of dermoscopy as a new medical technology and its available fee assessment in Korea comes an increased interest in imaging-based dermatological diagnosis. For the dermatologist, who treats benign tumors and malignant skin cancers, imaging-based evaluations can assist with determining the surgical method and future follow-up plans. The identification of the tumor's location and the existence of blood vessels can guide safe treatment and enable the use of minimal incisions. The recent development of high-resolution microscopy based on laser reflection has enabled observation of the skin at the cellular level. Despite the limitation of a shallow imaging depth, non-invasive light-based histopathologic examinations are being investigated as a rapid and pain-free process that would be appreciated by patients and feature reduced time from consultation to treatment. In the United States, the current procedural terminology billing code was established for reflectance confocal microscopy in 2016 and has been used for the skin cancer diagnosis ever since. In this review, we introduce the basic concepts and images of ultrasound imaging, optical coherence tomography, confocal microscopy, and two-photon microscopy and discuss how they can be utilized in the field of dermatological oncology.