Lip cancers are relatively rare, but early diagnosis is important for a good outcome. Unfortunately, many patients experience a delay in diagnosis. A new machine learning method, deep convolutional neural networks (DCNNs), uses algorithms which can reportedly be used to classify dermatological diseases at the same standard as board-certified dermatologists. However, this has not been verified for locations such as the lips, scalp, and genitals.A DCNN was used to classify malignant (cancerous) and benign (non-cancerous) lip disorders and its performance was evaluated. The images in this study were taken from the photo database of Seoul National University Hospital (SNUH) in South Korea. To validate the results, additional images were collected from two other affiliated hospitals. A total of 1973 lip images from SNUH were used including 853 malignant and 1120 benign diseases.The DCNN was trained with 1629 images (743 malignant, 886 benign) and its performance was evaluated using testing and external validation sets containing 344 and 281 images, respectively. For comparison, 44 participants with different levels of training were asked to classify the images.The study found that the DCNN's performance was equivalent to the dermatologists, and was superior to the nondermatologists when classifying malignancy. When they referenced the DCNN result, non-dermatologists performed significantly better.Thus, DCNNs can be used to classify lip diseases at a standard equivalent to a board-certified dermatologist and they can help unskilled physicians to discriminate between benign and malignant lip diseases. DCNNs could therefore be used to improve diagnosis and consequent patient outcomes for those with suspected lip cancers. This is a summary of the study: Dermatologist-level classification of malignant lip diseases using a deep convolutional neural network This summary relates to https://doi.

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