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

NCT ID: NCT05187585

Last Updated: 2025-04-01

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 1200 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2022-02-10
• Study Completion Date: 2024-02-17

Brief 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

Detailed 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.

Conditions

Fractures, Bone

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

radiograph interpretation without the support of the RAYVOLVE app

Group Type: SHAM_COMPARATOR

radiograph interpretation without the support of the RAYVOLVE app

Intervention Type: 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

Group Type: EXPERIMENTAL

radiograph interpretation with the support of the RAYVOLVE app

Intervention Type: 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

Interventions

radiograph interpretation without the support of the RAYVOLVE app

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.

Intervention Type: DIAGNOSTIC_TEST

radiograph interpretation with the support of the RAYVOLVE app

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

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

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)

• Maximum Eligible Age: 17 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Fondation Lenval

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Hopitaux Pediatriques de Nice Chu-Lenval

Nice, , France

Countries

France

Other Identifiers

21-HPNCL-06

Identifier Type: -

Identifier Source: org_study_id