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Completed

NCT Number: NCT05187585

Assessment of the Contribution of an Artificial Intelligence Tool to Help the Diagnosis of Limb Fractures in Pediatric Emergencies

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

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Key information

Conditions

Fractures, Bone Wounds and Injuries

Age range

Up to 17 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Hopitaux Pediatriques de Nice Chu-Lenval

Nice, 06200, France

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Children under 18
  • Showing signs that may suggest a limb fracture and justifying the realization of an X-ray (trauma with pain, deformation, edema, wound)
  • Written informed consent from one of the two parents or the holder of parental authority signed
  • Beneficiaries or members of a Health Insurance scheme

Exclusion criteria

  • A sign (s) of vital distress
  • Any other reason than that of a suspected limb fracture
  • A diagnosis of a limb fracture before its management in the emergency room (x-ray made in pre-hospital)

Treatment and study plan

radiograph interpretation without the support of the RAYVOLVE app

Diagnostic Test

Phase 1 does not involve any intervention: residents, emergency physicians, and radiologists will interpret the x-rays without the support of the RAYVOLVE application.

The emergency physician interprets the x-ray and manage the case as per protocol, all the x-rays will be reinterpreted by the radiologist only later, usually the day after. In case of missed fractures, the physician is notified of the error by the radiologist, and patients will be informed and recalled to the hospital to be reevaluated.

radiograph interpretation with the support of the RAYVOLVE app

Diagnostic Test

The residents interpret the X-ray with the RAYVOLVE application's support and indicate the presence or not of a fracture without sharing it with the senior emergency physician. A senior emergency physician manages the case as usual, and all the x-rays will be reinterpreted by the radiologist only later, usually the day after. In case of missed fractures, the physician is notified of the error by the radiologist, and patients will be informed and recalled to the hospital to be reevaluated

Primary outcomes

  1. Diagnosis fracture with Rayvolve app compare to gold standard

    Time frame: at inclusion

    Assess the statistical concordance between residents using the RAYVOLVE application tool and senior radiologists in diagnosing fractures of the extremities, as gold standard.

    Criteria: binary: fracture Yes/No

Secondary outcomes

  1. Diagnosis fracture with Rayvolve app compare to diagnosis done by physicians

    Time frame: at inclusion

    Assess the statistical concordance between residents using the RAYVOLVE application tool and pediatric emergency physicians in diagnosing fractures of the extremities Criteria: binary: fracture Yes/No

  2. Diagnosis fracture without Rayvolve app compare to diagnosis done by physicians

    Time frame: at inclusion

    Assess the statistical concordance between residents not using the RAYVOLVE application tool and pediatric emergency physicians in diagnosing fractures of the extremities Criteria: binary: presence or no fracture

  3. collection of patient data to define risk factors associated with the discrepancy between residents using the RAYVOLVE application tool and senior radiologists not using the application

    Time frame: at inclusion

    collection patient data such as patient's age, fracture location, fracture type, number of fractures, day and time of diagnosis. The goal is to define potential risk factors to explain diagnostic differences between residents and primary radiologists

  4. satisfaction of the residents using the application assessed by Likert scale

    Time frame: through study completion, an average of 6 months

    measure of satisfaction by an in-house Likert scale: consisting of 4 questions with multiple choice answers on the use and ergonomics of the application. The answers range from not at all satisfied to very satisfied.

Sponsors and collaborators

Lead sponsor

Fondation Lenval

Other

Registry information

Official study title

Assessment of the Contribution of an Artificial Intelligence Tool to Help the Diagnosis of Limb Fractures in Pediatric Emergencies : an Interventional, Prospective, Single-center Study

Acronym: FRACPED

Important dates

Study start
2022
Primary completion
2024
Study completion
2024
First posted
Jan 12, 2022
Registry last updated
Apr 1, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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