ArticleScientific reports2021
Critical evaluation of deep neural networks for wrist fracture detection.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Impact of deep learning on pediatric elbow fracture detection: a systematic review and meta-analysis.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2025Pooled it
- AI for detection, classification and prediction of loss of alignment of distal radius fractures; a systematic review.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2024Pooled 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
- Will AI Replace Physicians in the Near Future? AI Adoption Barriers in Medicine.Diagnostics (Basel, Switzerland) · 2026Review
- FracDet-v11: a multi-scale attention and wavelet-enhanced network for real-time pediatric wrist fracture detection.Scientific reports · 2026Article
- Advances in Artificial Intelligence for Wrist Joint Injury Diagnosis.International journal of medical sciences · 2026Review
- AI-driven Technologies for Wrist Fracture Prediction: A Narrative Review of Emerging Approaches.Journal of wrist surgery · 2025Article
- Spectral computer-tomography and the ability to detect occult femoral neck and scaphoid fractures - A systematic review and exploratory meta-analysis.European journal of radiology open · 2025Article
- Impact of test set composition on AI performance in pediatric wrist fracture detection in X-rays.European radiology · 2025Article
- Artificial Intelligence in Bone Fracture Detection: A Review of Evidence, Limitations, and Clinical Integration.Cureus · 2025Review
- A Computer Vision and Machine Learning Approach to Classify Views in Distal Radius Radiographs.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025Article
- A Neural Network Model for Intelligent Classification of Distal Radius Fractures Using Statistical Shape Model Extraction Features.Orthopaedic surgery · 2025Article
- Machine learning-based prediction of the necessity for the surgical treatment of distal radius fractures.Journal of orthopaedic surgery and research · 2025Article
- Artificial intelligence in fracture detection on radiographs: a literature review.Japanese journal of radiology · 2025Review
- Role of Artificial Intelligence in Minimizing Missed and Undiagnosed Fractures Among Trainee Residents.Journal of multidisciplinary healthcare · 2025Review
- AI-based biplane X-ray image-guided method for distal radius fracture reduction.Frontiers in bioengineering and biotechnology · 2025Article
- Application and Prospects of Deep Learning Technology in Fracture Diagnosis.Current medical science · 2024Review
- The Detection and Classification of Scaphoid Fractures in Radiograph by Using a Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wrist Fracture is the most common type of fracture with a high incidence rate. Conventional radiography (i.e. X-ray imaging) is used for wrist fracture detection routinely, but occasionally fracture delineation poses issues and an additional confirmation by computed tomography (CT) is needed for diagnosis. Recent advances in the field of Deep Learning (DL), a subfield of Artificial Intelligence (AI), have shown that wrist fracture detection can be automated using Convolutional Neural Networks. However, previous studies did not pay close attention to the difficult cases which can only be confirmed via CT imaging. In this study, we have developed and analyzed a state-of-the-art DL-based pipeline for wrist (distal radius) fracture detection-DeepWrist, and evaluated it against one general population test set, and one challenging test set comprising only cases requiring confirmation by CT. Our results reveal that a typical state-of-the-art approach, such as DeepWrist, while having a near-perfect performance on the general independent test set, has a substantially lower performance on the challenging test set-average precision of 0.99 (0.99-0.99) versus 0.64 (0.46-0.83), respectively. Similarly, the area under the ROC curve was of 0.99 (0.98-0.99) versus 0.84 (0.72-0.93), respectively. Our findings highlight the importance of a meticulous analysis of DL-based models before clinical use, and unearth the need for more challenging settings for testing medical AI systems.
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.