🤖 AI Summary
Existing dermoscopic datasets often fall short of clinical requirements in terms of acquisition standardization, metadata completeness, and diagnostic reliability, particularly lacking high-quality data suitable for outpatient and mobile settings in Russia. This work proposes a methodology for constructing a clinically validated dermoscopy dataset by systematically integrating a standardized mobile image acquisition protocol, a 16-field structured metadata schema compatible with ISIC standards, and a three-tier diagnostic verification mechanism comprising clinical annotation, expert consensus, and histopathological confirmation. The resulting dataset comprises 1,026 images spanning nine dermatological conditions, including 39 malignancies all verified by histopathology, thereby substantially enhancing clinical credibility and research utility for tasks such as model evaluation, domain shift analysis, and interpretability studies.
📝 Abstract
This study presents a methodology for constructing a clinically verified dataset of dermatoscopic images for medical informatics research. The relevance of the work is driven by the fact that the performance of automated diagnostic support systems depends not only on the volume of images, but also on the reproducibility of the image acquisition procedure, the completeness of structured metadata, and the reliability of diagnostic labels. International collections were primarily created under conditions that differ substantially from routine Russian outpatient practice and mobile dermatoscopy. The proposed methodology integrates three interconnected components: (1) a standard operating procedure (SOP) for acquiring images via mobile dermatoscopy, (2) an information model comprising 16 structured metadata fields organized into six clinically oriented blocks in ISIC-compatible notation, and (3) a multi-stage expert verification of diagnostic labels (initial clinical annotation, consensus review by three specialists, and histological confirmation of all malignant neoplasms). Using this methodology, a dataset of 1,026 unique dermatoscopic images from 443 patients was collected between June 2025 and May 2026. From 1,044 initial records, 18 duplicates were excluded. The dataset includes nine nosological categories; all 39 malignant lesions (18 melanomas, 15 basal cell carcinomas, and 6 squamous cell carcinomas) were histologically verified. Patient age ranged from 2 to 90 years (median 38), with 279 females (63%) and 164 males (37%). Each image is accompanied by expert-annotated dermatoscopic structures and an explicit verification_stage field indicating the level of diagnostic confirmation. The resulting dataset serves as a pilot clinically verified resource suitable for independent model evaluation, domain shift analysis, interpretability studies, and further expansion.