qXR
Othera software product that uses artificial intelligence to triage, prioritise, and (for tuberculosis only) diagnose based upon identified abnormalities within the CXR.
NCT Number: NCT06044454
Lung cancer is the most common cause of cancer death in the UK yet compared to Europe it has low survival rates.The NHS aims to find 75% of cancers at an early stage as this can improve the chances of survival.
To support this target, Qure.ai have developed the UK-approved qXR product, which is a software program that automatically analyses chest x-rays using artificial intelligence to identify features associated with lung cancer, indicative of other diagnoses, or that contain no abnormal features ('normal'). qXR is a class IIb medical device that can be used by radiologists to prioritise reporting based upon the presence or absence of these features. This may improve the accuracy and efficiency of reporting these images.
The project includes different elements including:
i) Clinical effectiveness study across 3 sectors within NHS Greater Glasgow and Clyde (NHSGGC).The primary objective is to assess the clinical effectiveness of qXR to prioritise patients that have suspected lung cancer (identified from AI analysis of a chest x-ray) for follow-on CT.
Primary study outcome measure - Time to 'decision to recommend CT', or to a decision not to undertake CT for CXR acquired with USC (CXR acquired to CXR reported).
Secondary objectives include:
i) To assess the potential utility of qXR within the optimised lung cancer pathway in terms of the impact on both patient treatment and radiological workflow.
ii) A technical evaluation utilising retrospective and prospective cohorts. The technical retrospective study will determine the performance of qXR using a sample of 1000 CXR images from all chest x-ray referral sources across all sectors (this differs from the prospective study, which only examines outpatient referred chest x-rays).
iii) A health economic evaluation. Use of per patient healthcare utilisation costs to model cost benefits of qXR, including implementation of supported reporting of normal CXR.
iv) A qualitative evaluation to assess acceptability and barriers to scale-up and implementation
This study is active but is not currently recruiting participants.
Notify Me18 year and older
All sexes
Observational
Glasgow Royal Infirmary (North Sector), Glasgow, United Kingdom
A clinical effectiveness study will be conducted in 3 NHS Greater Glasgow and Clyde sectors over a 12-month period.
Sectors will be identified and initiated into the qXR solution with a 30 day implementation period. The order in which sites will receive the qXR intervention will be determined by computer-based randomisation.
The technical retrospective study will determine the performance of qXR using a sample of 1000 CXR images from all chest x-ray referral sources across all sectors (this differs from the prospective study, which only examines outpatient referred chest x-rays). An economic evaluation will be conducted comparing costs and outcomes with and without the introduction of qXR. The software potentially impacts costs via two mechanisms: the identification of normal can enhance efficiency of CXR reporting; and the identification of USCs can support the prioritisation of CXRs that show signs of lung cancer, accelerating the provision of CT, which leads to faster diagnosis and treatment, and ultimately better outcomes.
Qualitative evaluation: To determine acceptability, staff interviews and patient focus groups will be carried out.
Data will be collected by an experienced qualitative researcher using a semi-structured interview guide, developed based on the key constructs of the Theoretical Framework of Acceptability. All interviews will be conducted via Zoom at a mutually agreed upon date and time and are estimated to last, on average, around 45 minutes.
To capture the NHS service user perspective, the investigators will also conduct three online focus groups with approximately 20 NHS service users.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
a software product that uses artificial intelligence to triage, prioritise, and (for tuberculosis only) diagnose based upon identified abnormalities within the CXR.
Time frame: through study completion, an average of 1 year
Time to 'decision to recommend CT', or to a decision not to undertake CT for CXR acquired with USC (CXR acquired to CXR reported)
Time frame: through study completion, an average of 1 year
Time from acquisition to reporting of all CXRs
Time frame: through study completion, an average of 1 year
Time to diagnosis of lung cancer
Time frame: through study completion, an average of 1 year
Time to treatment initiation lung cancer
Time frame: through study completion, an average of 1 year
Number of hospital visits during screening pathway
Time frame: through study completion, an average of 1 year
Hospitalisation within 6 and 12 months CXR acquisition
Time frame: through study completion, an average of 1 year
Death within 6 and 12 months of CXR acquisition
Time frame: through study completion, an average of 1 year
Percentage of CXRs not identified by qXR as suspected lung cancer that the radiologist refers for CT for USC
Time frame: through study completion, an average of 1 year
Percentage of non-USC that are referred for CT with subsequent detection of lung cancer
Time frame: through study completion, an average of 1 year
Model performance e.g. sensitivity, specificity, positive and negative predictive values.
NHS Greater Glasgow and Clyde
Other
RADICAL: A Mixed Methods Study to Assess the Clinical Effectiveness and Acceptability of an Artificial Intelligence Software to Prioritise Chest X-ray (CXR) Interpretation
Acronym: RADICAL
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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