ArticleBMC medical imaging2025
Presence of crescents in IgA nephropathy-prediction from ultrasound images using deep learning.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Noninvasive prediction of T-score in IgA nephropathy using machine learning within the Oxford classification system.Frontiers in endocrinology · 2025Article
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9 authors.
Funding
Abstract
backgroundThis study aimed to develop an ultrasound (US)-based deep learning (DL) model to evaluate the presence of crescents in patients with immunoglobulin A nephropathy (IgAN).
methodsWe created a training set consisting of 2,682 US images obtained from 931 patients with IgAN at the First Affiliated Hospital of Anhui Medical University. The external testing set included 198 patients from Nanchong Central Hospital based on the same criteria. Five DL models were trained in the training set and tested in the testing set. The performance of each model was evaluated for the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value.
resultsThe DenseNet121 model achieved an accuracy of 0.773 in the external testing set for predicting the presence of crescents, with a sensitivity of 57.6% and specificity of 87.9%.
conclusionUsing renal ultrasound imaging data, DL may be able to predict crescent status in IgAN, providing clinicians with a potentially non-invasive means to better understand crescent status in patients with IgAN. CLINICAL TRIAL NUMBER: Not applicable.
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