ArticleBMC medical imaging2025
Automated abdominal aortic calcification scoring via deep learning: a multi-center validation of LVLCRNet.
Article in BMC medical imaging, 2025. 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
Abstract
backgroundTo develop and validate a deep learning model for automated quantification of abdominal aortic calcification scores (AACS) adhering to the Kauppila protocol, with multicenter clinical validation.
methodsThis retrospective multicenter study analyzed 2,660 lateral lumbar/thoracoabdominal radiographs from four centers, partitioned into development (training: n = 1,478; validation: n = 423) and test cohorts (internal: n = 211; external: n = 157 from Center C and n = 391 from Center D). We proposed the Lumbar Vertebrae Localization-Contrastive Rank-Aware Network (LVLCRNet), incorporating automatic lumbar vertebrae localization, aortic region segmentation, and contrastive rank-aware network for ordinal classification. Comparative analyses against baseline network and Lumbar Vertebrae Localization Network were conducted using expert-annotated AACS as ground truth (GT), evaluated through Wilcoxon matched-paired signed-rank test, intraclass correlation coefficient (ICC), mean absolute error (MAE), coefficient of determination (R²), Bland-Altman analysis, and multiclass accuracy.
resultsNo significant difference was found between LVLCRNet and GT, whereas the baseline network showed significant deviations from GT across all cohorts (p < 0.017, Bonferroni-corrected). LVLCRNet achieved superior agreement with GT, demonstrating R² of 0.858 (internal) and 0.842/0.837 (external), ICC of 0.916 (internal) and 0.904/0.899 (external), and MAE of 1.547 (internal) and 1.189/1.972 (external). Bland-Altman analysis showed minimal systemic bias. Classification accuracy reached 82.94% (internal) and 79.62%/81.59% (external), outperforming comparators by 5.10–9.98%.
conclusionLVLCRNet provides reliable automated AACS through integrated anatomic localization and contrastive rank-aware learning. Its strong generalizability and precision in severity grading support clinical utility for cardiovascular risk stratification.
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.