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Fractures: Bone Repair

Fractures: Bone Repair

Treatment for a fracture is based on the type of break, the bone affected, and the patient's age.
Minor fractures with no bone displacement are treated by immobilizing the fractured bone using a cast or splint. However, in the case of fractures with displaced bones, the broken bones are repositioned before immobilization to ensure successful healing without deformation and loss of function. The realignment of fractured bone ends is performed through a process called reduction. If the...

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Orthopedic Robot-Assisted Femoral Neck System in the Treatment of Femoral Neck Fracture

Orthopedic Robot-Assisted Femoral Neck System in the Treatment of Femoral Neck Fracture

Published on: March 3, 2023

Artificial intelligence (AI) for paediatric fracture detection: a multireader multicase (MRMC) study protocol.

Susan C Shelmerdine^1,2,3, Cato Pauling², Emma Allan⁴

¹Clinical Radiology, Great Ormond Street Hospital for Children, London, UK susan.shelmerdine@gosh.nhs.uk.

|December 7, 2024

Summary

This study evaluates if an AI tool improves healthcare professionals' detection of paediatric fractures on radiographs. The AI tool showed potential to enhance diagnostic accuracy in children, aiding timely patient care.

Keywords:

ACCIDENT & EMERGENCY MEDICINE Diagnostic Imaging Paediatric orthopaedics

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Last Updated: Jun 5, 2025

Orthopedic Robot-Assisted Femoral Neck System in the Treatment of Femoral Neck Fracture

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Published on: March 3, 2023

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Fracture Apparatus Design and Protocol Optimization for Closed-stabilized Fractures in Rodents

Fracture Apparatus Design and Protocol Optimization for Closed-stabilized Fractures in Rodents

Published on: August 14, 2018

Area of Science:

Medical Imaging
Artificial Intelligence in Healthcare
Paediatric Radiology

Background:

Paediatric fractures are frequently missed on radiographs, leading to adverse outcomes.
Existing AI tools for fracture detection are primarily validated in adults.
The efficacy of AI in paediatric fracture detection requires further investigation.

Purpose of the Study:

To assess the impact of a certified paediatric AI tool on healthcare professionals' fracture detection accuracy.
To evaluate how AI assistance influences simulated patient management in paediatric radiography.
To determine the utility of AI in improving diagnostic performance for childhood fractures.

Main Methods:

A multicentric dataset of 500 paediatric radiographs (normal and abnormal) was used.
Healthcare professionals evaluated radiographs with and without AI assistance (BoneView, Gleamer).
Diagnostic accuracy was benchmarked against expert radiologist consensus; management plans were also assessed.

Main Results:

AI assistance is expected to improve diagnostic accuracy in paediatric fracture detection.
The study will quantify changes in diagnostic performance and simulated patient management.
Results will provide insights into the real-world application of AI in paediatric radiology.

Conclusions:

AI tools hold promise for enhancing the detection of paediatric fractures.
Improved fracture detection can lead to better patient outcomes and timely interventions.
This research will inform the integration of AI into paediatric diagnostic workflows.