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

Conditions

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

  • United Kingdom
    • 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
Diagnostic test: Genomic sequencing
Sequencing data
Diagnostic test: Histological examination
Images for deep learning
Tumours
Diagnostic test: Genomic sequencing
Sequencing data
Diagnostic test: Histological examination
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.

Sponsor

Robert Jones and Agnes Hunt Orthopaedic and District NHS Trust

Collaborators

Keele University

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.

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