SynthesisEuropean radiology2024
Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis.
Synthesis in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Enhanced fracture detection on radiographs with AI assistance for clinicians: a systematic review and meta-analysis.Annals of medicine · 2026Pooled it
- A review of digital orthopedic techniques in pre- and intra-operative management of scaphoid fracture.EFORT open reviews · 2026Article
- Single-sequence low-field 3D T1-weighted MRI for scaphoid fracture detection compared to radiography: prospective diagnostic accuracy pilot study.Emergency radiology · 2026Article
- Identifying Volar Locking Plates on Plain Radiographs: Can Artificial Intelligence Models 'Beat' Clinicians?Cureus · 2025Article
- An Overview of Artificial Intelligence Applications in Radiological Imaging for Bone Fracture Diagnosis.Cureus · 2025Review
- Review
- Application of artificial intelligence in the diagnosis of scaphoid fractures: impact of automated detection of scaphoid fractures in a real-life study.La Radiologia medica · 2025Article
- Addressing fractures that are hard to diagnose on imaging: Radiomics or deep learning?La Radiologia medica · 2025Review
- AI for Lesion Detection in Musculoskeletal Radiology.Journal of the Korean Society of Radiology · 2025Review
- Artificial intelligence in the interpretation of upper extremity trauma radiographs: a systematic review and meta-analysis.JSES reviews, reports, and techniques · 2025Review
- Deep Learning in Scaphoid Nonunion Treatment.Journal of clinical medicine · 2025Article
- Lightweight Deep Learning Framework for Accurate Detection of Sports-Related Bone Fractures.Diagnostics (Basel, Switzerland) · 2025Article
- Trends in the Use of Weightbearing Computed Tomography.Journal of clinical medicine · 2024Review
- Juvenile Osteochondritis Dissecans: A Case Report.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesScaphoid fractures are usually diagnosed using X-rays, a low-sensitivity modality. Artificial intelligence (AI) using Convolutional Neural Networks (CNNs) has been explored for diagnosing scaphoid fractures in X-rays. The aim of this systematic review and meta-analysis is to evaluate the use of AI for detecting scaphoid fractures on X-rays and analyze its accuracy and usefulness. MATERIALS AND
methodsThis study followed the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and PRISMA-Diagnostic Test Accuracy. A literature search was conducted in the PubMed database for original articles published until July 2023. The risk of bias and applicability were evaluated using the QUADAS-2 tool. A bivariate diagnostic random-effects meta-analysis was conducted, and the results were analyzed using the Summary Receiver Operating Characteristic (SROC) curve.
resultsTen studies met the inclusion criteria and were all retrospective. The AI's diagnostic performance for detecting scaphoid fractures ranged from AUC 0.77 to 0.96. Seven studies were included in the meta-analysis, with a total of 3373 images. The meta-analysis pooled sensitivity and specificity were 0.80 and 0.89, respectively. The meta-analysis overall AUC was 0.88. The QUADAS-2 tool found high risk of bias and concerns about applicability in 9 out of 10 studies.
conclusionsThe current results of AI's diagnostic performance for detecting scaphoid fractures in X-rays show promise. The results show high overall sensitivity and specificity and a high SROC result. Further research is needed to compare AI's diagnostic performance to human diagnostic performance in a clinical setting. CLINICAL RELEVANCE STATEMENT: Scaphoid fractures are prone to be missed secondary to assessment with a low sensitivity modality and a high occult fracture rate. AI systems can be beneficial for clinicians and radiologists to facilitate early diagnosis, and avoid missed injuries. KEY POINTS: • Scaphoid fractures are common and some can be easily missed in X-rays. • Artificial intelligence (AI) systems demonstrate high diagnostic performance for the diagnosis of scaphoid fractures in X-rays. • AI systems can be beneficial in diagnosing both obvious and occult scaphoid fractures.
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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.