Clinical trial NCT05187585: Assessment of the Contribution of an Artificial Intelligence Tool to Help the Diagnosis of Limb Fractures in Pediatric Emergencies
Official 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
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Fractures, Bone
Lead sponsor: Fondation Lenval
Sponsor class: OTHER
First posted: 2022-01-12
Enrollment: 1200
Sex: ALL
Maximum age (years): 17
Standard ages: CHILD
Accepts healthy volunteers: no
Allocation: NON_RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

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.

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)

Primary outcomes:
measure: Diagnosis fracture with Rayvolve app compare to gold standard
timeFrame: at inclusion
Secondary outcomes:
measure: Diagnosis fracture with Rayvolve app compare to diagnosis done by physicians
timeFrame: at inclusion
measure: Diagnosis fracture without Rayvolve app compare to diagnosis done by physicians
timeFrame: at inclusion
measure: 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
timeFrame: at inclusion
measure: satisfaction of the residents using the application assessed by Likert scale
timeFrame: through study completion, an average of 6 months
Other outcomes:
Locations:
city: Nice
country: France
facility: Hopitaux Pediatriques de Nice Chu-Lenval
Study officials:
References: