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Clinical Trial Summary

Limb fracture is a common pathology in children. It represents the first complaint in traumatology among children in developed countries. Failure to diagnose a fracture can have severe consequences in pediatric patients with growing bones, that can lead to delayed treatment, pain and poor functional recovery. X-ray is the first tool used by doctors to diagnose a fracture. However, the diagnosis of fracture in the emergency room can be challenging. Most images are interpreted and processed by emergency pediatricians before being reviewed by radiologists (most often the day after). Previous studies have reported the rate of misdiagnosis in fracture by emergency physicians from 5% to 15%. A tool to investigate in diagnosing limb fractures could be helpful for any emergency physicians exposed to this condition


Clinical Trial Description

Limb fracture is a common pathology in children with trauma. It represents the first complaint in traumatology among children in developed countries. Failure to diagnose a fracture on an X-ray can have severe consequences in pediatric patients, with growing bones, that can lead to delayed treatment, pain and poor functional recovery (with risk of bone deformity and bad consolidation). X-ray is the first tool used by doctors to diagnose a fracture. However, the diagnosis of fracture in the emergency room can be challenging. Most images are interpreted and processed by both residents and pediatricians before the radiologists proofread (most often the day after). Previous studies have reported the rate of misdiagnosis in fracture by emergency physicians from 5 to 15%. A tool to investigate in diagnosing limb fractures could be helpful for any clinician exposed to this condition. Artificial intelligence (AI) in medicine is booming and has already proven its worth, in terms of prevention, monitoring and diagnosis. AZMED has created RAYVOLVE®, a deep learning algorithm to help physicians in diagnosing fractures. The RAYVOLVE® tool connects to the PACS (Picture Archiving and Communication System) of any hospital and indicates, using a frame, the location of a potential fracture. The tool has not yet been validated in pediatric patients. The purpose of this research project is to evaluate the contribution of this artificial intelligence-based tool in the diagnosis of limb fracture in pediatric population. The investigators will study the concordance in diagnosing limb fracture between the junior emergency physicians using the RAYVOLVE® application and senior radiologists, as the gold standard. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT05187585
Study type Interventional
Source Fondation Lenval
Contact MARCO OLLA, MD
Phone 049203 0442
Email [email protected]
Status Not yet recruiting
Phase N/A
Start date April 2022
Completion date October 2022

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