Full name: PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones Full data descriptor published by Pacheco et al. at https://doi.org/10.1016/j.dib.2020.106221 Pacheco AGC, Lima GR, Salomão AS, et al. PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones. Data Brief. 2020;32:106221. Published 2020 Aug 25. doi:10.1016/j.dib.2020.106221 Summary description - The PAD-UFES-20 dataset was collected along with the Dermatological and Surgical Assistance Program (in Portuguese: Programa de Assistência Dermatológica e Cirurgica - PAD) at the Federal University of Espírito Santo (UFES-Brazil), which is a nonprofit program that provides free skin lesion treatment, in particular, to low-income people who cannot afford private treatment. - The dataset consists of 2,298 samples of six different types of skin lesions. Each sample consists of a clinical image and up to 22 clinical features including the patient's age, skin lesion location, Fitzpatrick skin type, and skin lesion diameter. - The skin lesions are: Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC), Actinic Keratosis (ACK), Seborrheic Keratosis (SEK), Bowen’s disease (BOD), Melanoma (MEL), and Nevus (NEV). As the Bowen’s disease is considered SCC in situ, we clustered them together, which results in six skin lesions in the dataset, three skin cancers (BCC, MEL, and SCC) and three skin disease (ACK, NEV, and SEK) - All BCC, SCC, and MEL are biopsy-proven. The remaining ones may have clinical diagnosis according to a consensus of a group of dermatologists. In total, approximately 58% of the samples in this dataset are biopsy-proven. This information is described in the metadata. - The images present in the dataset have different sizes because they are collected using different smartphone devices. All images are available in .png format. - The metadata associated with each skin lesion is composed of up to 26 features. All features are available in a CSV document in which each line represents a skin lesion and each column a metadata feature. - In total, there are 1,373 patients and 2,298 images present in the dataset. Each image/sample has a reference to the patient and the skin lesion in the metadata. Ethics statement The dataset was collected along with the Dermatological and Surgical Assistance Program (PAD) of the Federal University of Espírito Santo. The program is managed by the Department of Specialized Medicine and was approved by the university ethics committee (nº 500002/478) and the Brazilian government through Plataforma Brasil (nº 4.007.097), the Brazilian agency responsible for research involving human beings. In addition, all data is collected under patient consent and the patient’s privacy is completely preserved. Data is also available on Mendeley at https://doi.org/10.17632/zr7vgbcyr2.1

We conducted a prospective, observational clinical validation study to assess the diagnostic accuracy of the AI algorithm (ADAE) in predicting melanoma from dermoscopy skin lesion images. Patients who had consented for a skin biopsy to exclude melanoma were eligible. All lesions underwent biopsy.

Images captured with an RCM device.

Dataset from a prospective, observational clinical cohort study to assess the consistency of two commercially available convolutional neural networks (CNNs) in classifying melanoma risk of five sequentially acquired dermoscopic images of melanocytic lesions on the torso. 117 repeat image series of 116 melanocytic lesions from 66 patients were included. Biopsies were performed in cases of suspected melanoma or two consecutive elevated CNN risk scores. Expert consensus including 1-year follow-up images (where available) confirmed benign dignity of lesions without histological assessment. Goessinger EV, Cerminara SE, Mueller AM, et al. Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence. J Eur Acad Dermatol Venereol. 2024;38(5):945-953. doi:10.1111/jdv.19777

Moles in children. Benign melanocytic lesions from a pediatric population. The benign nature of the lesions is based on clinical assessment and/or no change on serial imaging.

Moles and melanomas. Biopsy-confirmed melanocytic lesions. Both malignant and benign lesions are included.

Moles and melanomas. Biopsy-confirmed melanocytic lesions. Both malignant and benign lesions are included.