DermIDS: Dermatology imaging data structure for scalable and interoperable AI systems

Dermatologic imaging has been rapidly expanding, with over 70% of related PubMed articles published since 2016 and over a million images across international research challenges and large-scale datasets with skin images. To improve data quality and usability, standardizing dermatologic imaging data for non-protected health information (non-PHI) research systems is essential. While the International Skin Imaging Collaboration (ISIC) has advanced standards in skin imaging, the field lacks a generalizable infrastructure to organize and describe imaging data for non-PHI research systems. This results in inconsistently labeled, heterogeneous datasets that hinder data integration, scalability, and interoperability. To address this gap, we propose the Dermatology Imaging Data Structure (DermIDS), inspired by the Brain Imaging Data Structure (BIDS) for neuroimaging. This structured framework aims to improve usability across datasets, reveal metadata gaps, and enable scalable artificial intelligence (AI)/machine learning (ML)-ready workflows. To illustrate this system, we curated and processed 1,000,692 images with DermIDS. We demonstrate that DermIDS (1) supports multimodal photographic data acquired from clinical photography, general photography, dermoscopy, reflectance confocal microscopy, and surface 3D imaging; (2) facilitates image-specific technical and clinical metadata organization; and (3) streamlines quality control and harmonization. Across all images, 1,256 unique metadata features were identified. However, 70% of clinical metadata features and 98% of technical metadata features were present in less than 100k images, highlighting key gaps and demonstrating the utility of DermIDS in revealing inconsistencies and opportunities for standardization. Our work supports large-scale analysis and harmonization, laying the foundation for AI/ML-ready workflows to advance dermatologic imaging research.