ArticleCureus2025
Detecting Fifth Metatarsal Fractures on Radiographs Through the Lens of Smartphones: The FIXUS AI Algorithm.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of X-ray-based deep learning models for detecting ankle and foot fractures: a systematic review and meta-analysis.Skeletal radiology · 2026Pooled it
- Who Is the Surgeon Now: Human Hands or Machine Minds? Artificial Intelligence in Orthopedics from Diagnosis to Follow-Up-A Structured Narrative Review.Journal of clinical medicine · 2026Review
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction Fifth metatarsal (5MT) fractures are common but challenging to diagnose, particularly with limited expertise or subtle fractures. Deep learning shows promise, but faces limitations due to image quality requirements. This study develops a deep learning model to detect 5MT fractures from smartphone-captured radiograph images, enhancing the accessibility of diagnostic tools. Methods A retrospective study included patients aged ≥18 with 5MT fractures (n = 1,240) and controls (n = 1,224). Radiographs (anteroposterior, oblique, and lateral) from Electronic Health Records (EHRs) were obtained and photographed using a smartphone (SP), creating a new dataset. Models using ResNet-152V2 were trained on EHR, SP, and combined datasets, then evaluated on a separate SP test dataset. Results On validation, the SP model achieved optimal performance (area under the receiver operating characteristic curve, or AUROC: 0.99). On the SP test dataset, the EHR model's performance decreased (AUROC: 0.83), whereas the SP and combined models maintained high performance (AUROC: 0.99). Conclusion SP-specific deep learning models effectively detect 5MT fractures, suggesting their practical utility in resource-limited (RL) settings.
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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.