ArticleNature communications2021
A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 3 of them syntheses that pooled it.
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
61 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial Intelligence-Guided Assessment of Femoral Neck Fractures in Radiographs: A Systematic Review and Multilevel Meta-Analysis.Orthopaedic surgery · 2025Pooled it
- Artificial Intelligence for Hip Fracture Detection and Outcome Prediction: A Systematic Review and Meta-analysis.JAMA network open · 2023Pooled it
- Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis.Radiology · 2022Pooled it
- Temporal Reproducibility of Fracture Interpretation in Forensic Radiography: A Multispecialty Comparison of Physicians and Vision Language Models Including Fracture Subtype Description.Tomography (Ann Arbor, Mich.) · 2026Article
- Beyond the pelvic ring: benchmarking multi-bone segmentation for ROI-Guided fracture detection.Scientific reports · 2026Article
- Evaluation of AI developed for rib fracture detection in identifying vertebral fractures on abdominal CT.BMC musculoskeletal disorders · 2026Article
- Artificial Intelligence in Pelvic Fracture Diagnosis and Outcome Prediction: A Systematic Review and Meta-analysis.Mayo Clinic proceedings. Digital health · 2026Review
- Leveraging Large Language Models for Accurate AO Fracture Classification from CT Text Reports.Journal of imaging informatics in medicine · 2026Article
- Advancing trauma scoring through large language models: automated estimation of injury severity.BMC emergency medicine · 2026Article
- Motion-Informed, Patient-Specific Femoral Localization for MPFL Reconstruction Using 4D-CT and Constrained Optimization.Diagnostics (Basel, Switzerland) · 2026Article
- A Comprehensive X-ray Dataset for Pediatric Ulna and Radius Fractures Analysis.Scientific data · 2026Article
- Ghost in the Machine: Leveraging artificial intelligence in the trauma bay.Trauma surgery & acute care open · 2026Review
- Transformer-based and CNN-based models for clinically effective 2D and 3D pelvic bone segmentation in CT imaging.BMC musculoskeletal disorders · 2025Article
- The Evolution of Machine Learning and Its Applications in Orthopaedics: A Bibliometric Analysis.Cureus · 2025Review
- Artificial intelligence for abdominopelvic trauma imaging: trends, gaps, and future directions.Abdominal radiology (New York) · 2025Review
- Efficacy and safety of conventional biplanar and triangulation method for sacroiliac screw placement in the treatment of unstable posterior pelvic ring fractures: A real-world retrospective cohort study.Chinese journal of traumatology = Zhonghua chuang shang za zhi · 2025Article
- Comparative analysis of AI support levels in clinical interpretation of traumatic pelvic radiographs.NPJ digital medicine · 2025Article
- Comparative accuracy of two commercial AI algorithms for musculoskeletal trauma detection in emergency radiographs.Emergency radiology · 2025Article
- An automated hip fracture detection, classification system on pelvic radiographs and comparison with 35 clinicians.Scientific reports · 2025Article
- Classification and segmentation of hip fractures in x-rays: highlighting fracture regions for interpretable diagnosis.Insights into imaging · 2025Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pelvic radiograph (PXR) is essential for detecting proximal femur and pelvis injuries in trauma patients, which is also the key component for trauma survey. None of the currently available algorithms can accurately detect all kinds of trauma-related radiographic findings on PXRs. Here, we show a universal algorithm can detect most types of trauma-related radiographic findings on PXRs. We develop a multiscale deep learning algorithm called PelviXNet trained with 5204 PXRs with weakly supervised point annotation. PelviXNet yields an area under the receiver operating characteristic curve (AUROC) of 0.973 (95% CI, 0.960-0.983) and an area under the precision-recall curve (AUPRC) of 0.963 (95% CI, 0.948-0.974) in the clinical population test set of 1888 PXRs. The accuracy, sensitivity, and specificity at the cutoff value are 0.924 (95% CI, 0.912-0.936), 0.908 (95% CI, 0.885-0.908), and 0.932 (95% CI, 0.919-0.946), respectively. PelviXNet demonstrates comparable performance with radiologists and orthopedics in detecting pelvic and hip fractures.
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.