ArticleJournal of vascular surgery. Venous and lymphatic disorders2026
Artificial intelligence-assisted identification of skin lesions associated with chronic venous insufficiency.
Article in Journal of vascular surgery. Venous and lymphatic disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to develop and validate a deep learning framework to classify chronic venous insufficiency (CVI)-related skin lesions and differentiate them from other lower-extremity dermatological conditions using dermoscopic images.
methodsWe retrospectively analyzed 677 high-resolution dermoscopic images from 248 patients, all histopathologically confirmed by skin biopsy. The dataset was categorized into three clinically distinct groups: (1) skin conditions related to CVI (eg, stasis dermatitis), (2) common inflammatory dermatological diseases, and (3) vasculitis. A Swin Transformer-based architecture (Microsoft Research)-a deep learning model that processes images at multiple spatial scales to capture both fine local detail and broader contextual patterns-was implemented to analyze dermoscopic images. A strict patient-level split was used to ensure model robustness, so that images from the same patient were never shared between training and test sets, which could otherwise artificially inflate performance estimates.
resultsThe proposed Swin Transformer model demonstrated superior diagnostic performance, achieving an overall area under the curve of 0.935 and classification accuracy of 0.848, significantly outperforming conventional convolutional neural networks and Vision Transformer baseline models. Group-specific area under the curve values were 0.942 for CVI-related conditions, 0.929 for inflammatory dermatoses, and 0.934 for vasculitis. Misclassifications were predominantly associated with overlapping dermoscopic features, such as purpuric patterns shared between stasis dermatitis and early-stage vasculitis. Notably, misclassification errors were directed more often toward the lower-risk pathway, with three CVI-related lesions misread as vasculitis, compared with only one vasculitis case misread as a CVI-related lesion.
conclusionsThis study demonstrates that a Swin Transformer-based deep learning model can effectively differentiate CVI-related skin lesions from inflammatory and vascular conditions, providing objective, noninvasive diagnostic support with the potential to streamline clinical workflows, facilitate early referrals to specialized care, and reduce reliance on invasive diagnostic procedures in the management of lower-extremity skin diseases.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.