Artificial Intelligence in the Diagnosis of Orthopaedic Conditions, Particularly Bone Tumours and Infection (AIortho)
November 10, 2021 updated by: Robert Jones and Agnes Hunt Orthopaedic and District NHS Trust
Translational research aimed at improving diagnostic accuracy of musculoskeletal infection and musculoskeletal tumours using machine learning applied to clinical data, histopathological sections and genomic sequencing.
Study Overview
Status
Enrolling by invitation
Intervention / Treatment
Study Type
Observational
Enrollment (Anticipated)
100
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
-
-
Shropsire
-
Oswestry, Shropsire, United Kingdom, SY10 7AG
- Robert Jones and Agnes Hunt Orthopaedic Hospital NHS Trust
-
-
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
18 years and older (Adult, Older Adult)
Accepts Healthy Volunteers
No
Genders Eligible for Study
All
Sampling Method
Probability Sample
Study Population
Patients with suspected orthopaedic infection (including prosthetic joint infection) or bone tumours
Description
Inclusion Criteria:
Suspected infection of bone tumour and biopsy taken for analysis
Exclusion Criteria:
Patient doesn't consent
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
- Observational Models: Case-Only
- Time Perspectives: Prospective
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
|
Infection
|
Sequencing data
Images for deep learning
|
|
Tumours
|
Sequencing data
Images for deep learning
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Sequencing data and identification
Time Frame: 3 years
|
Identification of infection and tumours using sequencing data
|
3 years
|
|
Histological slides and identification
Time Frame: 3 years
|
Deep learning using histological images
|
3 years
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Collaborators
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
June 28, 2021
Primary Completion (Anticipated)
January 1, 2024
Study Completion (Anticipated)
July 1, 2024
Study Registration Dates
First Submitted
February 8, 2021
First Submitted That Met QC Criteria
February 8, 2021
First Posted (Actual)
February 9, 2021
Study Record Updates
Last Update Posted (Actual)
November 11, 2021
Last Update Submitted That Met QC Criteria
November 10, 2021
Last Verified
November 1, 2021
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 281854_47208_2021
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Undecided
IPD Plan Description
Data may be made available as part of publication
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.