ReviewFrontiers in cardiovascular medicine2026
Performance of image-based deep learning models for aortic dissection segmentation and diagnosis: a systematic review and meta-analysis.
Review in Frontiers in cardiovascular medicine, 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This meta-analysis was conducted to systematically evaluate the accuracy of image-based deep learning models for aortic dissection segmentation and diagnosis, aiming to provide an evidence base for developing intelligent detection tools. Methods: A comprehensive search was performed across the Cochrane Library, PubMed, Embase, and Web of Science to identify studies on the effectiveness of deep learning in aortic dissection segmentation or diagnosis up to November 3, 2024. Risk evaluation was carried out with the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Results: A total of 48 studies on deep learning models for aortic dissection segmentation or diagnostic tasks were included, with 28 on segmentation tasks and 20 on diagnostic tasks. For segmentation tasks, the mean Dice coefficient was 89.2% ± 4.4% for false lumen segmentation, 90.8% ± 3.4% for true lumen segmentation, and 91.7% ± 6.1% for entire aorta segmentation. For diagnostic tasks, computed tomography (CT) based deep learning showed pooled sensitivity and specificity of 0.94 [95% confidence interval (CI): 0.89-0.96] and 0.92 (95% CI: 0.88-0.95), respectively. In terms of electrocardiogram-based deep learning, the pooled sensitivity and specificity were 0.85 (95% CI: 0.79-0.89) and 0.90 (95% CI: 0.87-0.92), respectively. Regarding the computed tomography angiography (CTA) based deep learning, the pooled sensitivity and specificity were 0.94 (95% CI: 0.90-0.96) and 0.95 (95% CI: 0.91-0.98), respectively. Additionally, some studies compared the diagnostic performance of deep learning with that of clinicians. The pooled sensitivity and specificity were 0.79 (95% CI: 0.65-0.89) and 0.95 (95% CI: 0.88-0.94), respectively. Conclusions: Image-based deep learning models demonstrated high accuracy for aortic dissection segmentation and diagnosis. They performed comparably to or better than clinicians. These findings support their potential as clinical assistive tools. Future work should prioritize multicenter validation, seamless integration of these models into clinical workflows, and enhancement of model generalizability to facilitate broader clinical adoption. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024619403, identifier CRD42024619403.
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.