Optimization of the Diagnosis of Bone Fractures in Patients Treated in the Emergency Department by Using Artificial Intelligence for Reading Radiological Images in Comparison With Traditional Reading by the Emergency Doctor. (FracturIA)

Elsan

Status: Enrolling

Conditions: Artificial Intelligence; Bone Fracture

Treatments: Device: Artificial intelligence; Procedure: Emergency physician

Study type: Interventional

Funder types: Other

Identifiers

NCT06051682

2023-A00639-36

Details and patient eligibility

About

As part of the management of a patient with suspected bone fractures, emergency physicians are required to make treatment decisions before obtaining the imaging reading report from the radiologist, who is generally not available only a few hours after the patient's admission, or even the following day. This situation of the emergency doctor, alone interpreting the radiological image, in a context of limited time due to the large flow of patients to be treated, leads to a significant risk of interpretation error. Unrecognized fractures represent one of the main causes of diagnostic errors in emergency departments.

This comparative study consists of two cohorts of patients referred to the emergency department for suspected bone fracture. The first will be of interest to patients whose radiological images will be interpreted by the reading of the emergency doctor systematically doubled by the reading of the artificial intelligence. The other will interest a group of patients cared for by the simple reading of the emergency doctor.

All of the images from both groups of patients will be re-read by the establishment's group of radiologists no later than 24 hours following the patient's treatment.

A centralized review will be provided by two expert radiologists. Also, patients in both groups will be systematically recalled in the event of detection of an unknown fracture for hospitalization.

Enrollment: 1,500 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Major Subject
• Patient admitted to the emergency department for suspected peripheral fractures in the extremities of the upper limb and/or lower limb (wrist/hand and ankle/foot).
• Patient affiliated to or entitled to a social security system
• Patient having received written and informed information about the study and having signed a free and informed consent to participate in the study.

Exclusion criteria

• Patient previously admitted to the emergency room for suspicion of fractures and not included in the study
• Patient admitted to the emergency room with suspicion of multiple fractures
• Refusal to participate in the study
• Protected patient: adult under guardianship, curatorship or other legal protection, deprived of liberty by judicial or administrative decision and under judicial protection
• Pregnant, breastfeeding or parturient patient

Trial design

Primary purpose: Supportive Care

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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1,500 participants in 2 patient groups, including a placebo group

Patient with emergency physician and AI for diagnosis

Experimental group

Description:

Patient benefiting from imaging submitted to radiological reading by the emergency physician and the AI for diagnosis and treatment decision

Treatment:

Procedure: Emergency physician

Device: Artificial intelligence

Patient with emergency physician only for diagnosis

Placebo Comparator group

Treatment:

Procedure: Emergency physician