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
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 1,200
- 试验地点
- 2
- 主要终点
- Diagnosis fracture with Rayvolve app compare to gold standard
研究概览
简要总结
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
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- — 至 17 Years（Child）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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)
结局指标
主要结局
Diagnosis fracture with Rayvolve app compare to gold standard
时间窗: 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
次要结局
- satisfaction of the residents using the application assessed by Likert scale(through study completion, an average of 6 months)
- Diagnosis fracture with Rayvolve app compare to diagnosis done by physicians(at inclusion)
- 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(at inclusion)
- Diagnosis fracture without Rayvolve app compare to diagnosis done by physicians(at inclusion)