The SLICE-3D dataset: 400,000 skin lesion image crops extracted from 3D TBP for skin cancer detection.

AI image classification algorithms have shown promising results when applied to skin cancer detection. Most public skin cancer image datasets are comprised of dermoscopic photos and are limited by selection bias, lack of standardization, and lend themselves to development of algorithms that can only be used by skilled clinicians. The SLICE-3D ("Skin Lesion Image Crops Extracted from 3D TBP") dataset described here addresses those concerns and contains images of over 400,000 distinct skin lesions from seven dermatologic centers from around the world.

A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification.

Introduction: Artificial Intelligence (AI) has proven effective in classifying skin cancers using dermoscopy images. In experimental settings, algorithms have outperformed expert dermatologists in classifying melanoma and keratinocyte cancers. However, clinical application is limited when algorithms are presented with 'untrained' or out-of-distribution lesion categories, often misclassifying benign lesions as malignant, or misclassifying malignant lesions as benign.

Validating the Reliability and Clinical Relevance of a Nasolabial Fold Photonumeric Scale.

Background: The use of tissue fillers to treat age-related deepening of the nasolabial fold (NLF) has increased and become the standard clinical approach, creating a need for evidence-based, objective evaluation for pre- and post-procedure assessment of the NLF.

Methods: A 5-point rating scale was developed to assess the NLF, specifically the presence of depression and shadowing. Live validation of the scale was performed with a total of 73 participants representing the full range of NLF severities.