A Machine Learning Model to Identify and Classify Fractures From X-ray
Research type
Research Study
Full title
Developing AXIS, an Automated X-ray Interpretation System which will detect and grade fractures from plain radiographs (x-rays) using new machine learning algorithms.
IRAS ID
294586
Contact name
Philip Sauve
Contact email
Sponsor organisation
University of Portsmouth
Clinicaltrials.gov Identifier
TECH 41626, TECH 41626
Duration of Study in the UK
0 years, 5 months, 31 days
Research summary
We are developing our software application in collaboration with the department of Future & Emerging Technologies, University of Portsmouth (UoP).
Our application will use two AI algorithms to automatically detect and classify fractures from plain radiographs (x-rays).
We will use approximately 2000 anonymised plain radiograph DICOM images per body part studied, distal radius fractures initially, exported from the picture archiving and communication system (PACS) at PHU as JPEG images. These images will be used to train a transfer learning algorithm. These newer algorithms have recently been shown to have better results than convolutional neural network (CNN) algorithms in some applications. The algorithm will be trained to detect the distal radius on the anteroposterior and lateral images as the region of interest (ROI). Once trained the images are deleted. This type of algorithm has been proven to be very good at image classification (fracture detection). The algorithms will then be tested on a further 500 previously unseen images, consisting of images with and without distal radius fractures.
The diagnostic performance of the algorithm will be assessed and the accuracy, sensitivity, specificity and Younden Index compared to standards in the medical literature.
We will then combine this algorithm with an additional knowledge-based system (KBS) algorithm. This KBS will be trained using human clinical expertise (consultant orthopaedic surgeon) based on the fracture type.
This will produce an automated system for interpreting plain radiograph images (x-rays) and recommending treatment.
REC name
North West - Greater Manchester Central Research Ethics Committee
REC reference
21/NW/0084
Date of REC Opinion
19 Mar 2021
REC opinion
Favourable Opinion