ReviewNPJ digital medicine2024
Prospective clinical evaluation of deep learning for ultrasonographic screening of abdominal aortic aneurysms.
Review in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
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.
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Who cites it
13 citing papers in PubMed.
- From Automated ECG Interpretation to Multimodal Cardiovascular Intelligence: The Evolution of Artificial Intelligence in Cardiovascular Medicine.Medical sciences (Basel, Switzerland) · 2026Review
- Diagnostic performance of an artificial intelligence algorithm for detecting pneumoperitoneum on abdominal CT scans.Insights into imaging · 2026Article
- A Robotic Ultrasound System for Automated Abdominal Aorta Screening: Feasibility Study in Healthy Volunteers.Sensors (Basel, Switzerland) · 2026Article
- Beyond Computer-Aided Diagnosis: Artificial Intelligence as a "Digital Mentor" for POCUS Image Acquisition and Quality Assurance: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Deep learning-based classification of superficial femoral arterial lesions: a pilot study.Annals of surgical treatment and research · 2026Article
- Early prediction of in-hospital deterioration after emergency department admission using machine learning models.BMC emergency medicine · 2026Article
- Age-stratified analysis of descending aorta diameter in traumatic massive hemorrhage: a machine learning approach.Trauma surgery & acute care open · 2026Article
- Abdominal Aortic Aneurysm Pathomechanics, Current Understanding and Emerging Directions: A Perspective for 2025 and Beyond.Advances in experimental medicine and biology · 2026Review
- Automated Assessment of Choroidal Mass Dimensions Using Static and Dynamic Ultrasonographic Imaging.Translational vision science & technology · 2025Article
- Automated evaluation for pericardial effusion and cardiac tamponade with echocardiographic artificial intelligence.European heart journal. Digital health · 2025Article
- Clinical benefits of deep learning-assisted ultrasound in predicting lymph node metastasis in pancreatic cancer patients.Future oncology (London, England) · 2025Article
- Commercialization of medical artificial intelligence technologies: challenges and opportunities.NPJ digital medicine · 2025Article
- PACT-3D, a deep learning algorithm for pneumoperitoneum detection in abdominal CT scans.Nature communications · 2024Article
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
Abdominal aortic aneurysm (AAA) often remains undetected until rupture due to limited access to diagnostic ultrasound. This trial evaluated a deep learning (DL) algorithm to guide AAA screening by novice nurses with no prior ultrasonography experience. Ten nurses performed 15 scans each on patients over 65, assisted by a DL object detection algorithm, and compared against physician-performed scans. Ultrasound scan quality, assessed by three blinded expert physicians, was the primary outcome. Among 184 patients, DL-guided novices achieved adequate scan quality in 87.5% of cases, comparable to the 91.3% by physicians (p = 0.310). The DL model predicted AAA with an AUC of 0.975, 100% sensitivity, and 97.8% specificity, with a mean absolute error of 2.8 mm in predicting aortic width compared to physicians. This study demonstrates that DL-guided POCUS has the potential to democratize AAA screening, offering performance comparable to experienced physicians and improving early detection.
Identifiers
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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.