ArticleActa orthopaedica et traumatologica turcica2024
Comparison of artificial intelligence algorithm for the diagnosis of hip fracture on plain radiography with decision-making physicians: a validation study.
Article in Acta orthopaedica et traumatologica turcica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 7 citations in OpenAlex.
- Patterns of AI Use in Clinical Work by Hospitalists: Survey Study.Journal of medical Internet research · 2026Article
- Closed-Loop digital therapeutics empowered by deep reinforcement learning and wearable sensing for precision orthopedic rehabilitation: a simulation-based proof-of-concept study.Frontiers in rehabilitation sciences · 2026Review
- Artificial intelligence algorithms in orthopaedics: A narrative review of methods and clinical applications.Journal of experimental orthopaedics · 2025Review
- Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: a multi-center diagnostic study.Frontiers in medicine · 2025Article
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Authors and funding
4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to compare an algorithm developed for diagnosing hip fractures on plain radiographs with the physicians involved in diagnosing hip fractures.
methodsRadiographs labeled as fractured (n=182) and non-fractured (n=542) by an expert on proximal femur fractures were included in the study. General practitioners in the emergency department (n=3), emergency medicine (n=3), radiologists (n=3), orthopedic residents (n=3), and orthopedic surgeons (n=3) were included in the study as the labelers, who labeled the presence of fractures on the right and left sides of the proximal femoral region on each anteroposterior (AP) plain pelvis radiograph as fractured or non-fractured. In addition, all the radiographs were evaluated using an artificial intelligence (AI) algorithm consisting of 3 AI models and a majority voting technique. Each AI model evaluated each graph separately, and majority voting determined the final decision as the majority of the outputs of the 3 AI models. The results of the AI algorithm and labelling physicians included in the study were compared with the reference evaluation.
resultsBased on F-1 scores, here are the average scores of the group: majority voting (0.942) > orthopedic surgeon (0.938) > AI models (0.917) > orthopedic resident (0.858) > emergency medicine (0.758) > general practitioner (0.689) > radiologist (0.677).
conclusionThe AI algorithm developed in our previous study may help recognize fractures in AP pelvis in plain radiography in the emergency department for non-orthopedist physicians. LEVEL OF EVIDENCE: Level IV, Diagnostic Study.
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