Artificial Intelligence to Improve Physicians' Interpretation of Chest X-Rays in Breathless Patients

NCT05117320 | Unknown

• 1 other identifier: FACTUAL-XRAI 1.0
• Study Type: interventional
• Target: 33
• Locations: 1 country
• Sites: 1

Brief Summary

Identifying the cause of breathlessness in acute patients in the emergency department is critical and challenging. The chest X-ray is central but challenging to read for non-radiologist physicians. Often the physicians read the CXR alone due to off-hours and shortage of radiology specialists. Artificial Intelligence (AI) has the potential to aid the reading of chest X-rays. The hypothesis is that AI applied to chest X-rays improves emergency physicians' diagnostic accuracy in acute breathless patients.

Conditions

Dyspnea

Keywords

Dyspnea; Dyspnea; Cardiac; Artificial Intelligence; Deep Learning; Emergency Department; Diagnostic; Physicians; Emergency Service, Hospital; X-Rays; Pneumonia; Heart Failure Acute; Diagnostic Accuracy; Multi-reader multi-case (MRMC); Chest X-ray; Randomized

Outcome Measures

Primary Outcomes (2)

• Accuracy of diagnosing ADHF on acute CXR with vs without AI

  The primary outcome is the difference in diagnostic accuracy of the non-radiologist physicians' diagnosis of ADHF on acute CXR compared with the gold standard. Odds of correct diagnosis are compared using an odds ratio with 95% confidence interval estimated using conditional logistic regression stratified by each image with and without AI. Thus, the improvement in the odds of correct classification after versus before AI support is reported. The significance level is 0.025.

  3 months

• Accuracy of diagnosing pneumonia on acute CXR with vs without AI

  The primary outcome is the difference in diagnostic accuracy of the non-radiologist physicians' diagnosis of pneumonia on acute CXR compared with the gold standard. Odds of correct diagnosis are compared using an odds ratio with 95% confidence interval estimated using conditional logistic regression stratified by each image with and without AI. Thus, the improvement in the odds of correct classification after versus before AI support is reported. The significance level is 0.025.

  3 months

Study Arms (2)

AI support

EXPERIMENTAL

Device: AI support

Non-AI support

NO INTERVENTION

Interventions

AI support DEVICE

Images were allocated to participants. In randomized allocation, one half of the images for each participant are viewed with AI support and the other half is viewed without AI support on the first trial day. On the second trial day the same images are viewed without versus with AI, respectively. This ensures that all images are read twice by the same participant both with and without AI support.

Also known as: Oxipit.ai

AI support

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Medical Doctor (MD)
• Working experience with emergency patients

You may not qualify if:

• Current or former employment as a radiologist
• Unwillingness to consent

Sponsors & Collaborators

• Bispebjerg Hospital: lead
• Enlitic.com: collaborator
• Oxipit.ai: collaborator

Study Sites (1)

University Hospital Bispebjerg and Frederiksberg

Copenhagen, Denmark

Location

MeSH Terms

Conditions

Dyspnea; Emergencies; Disease; Pneumonia

Condition Hierarchy (Ancestors)

Respiration Disorders; Respiratory Tract Diseases; Signs and Symptoms, Respiratory; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Disease Attributes; Pathologic Processes; Respiratory Tract Infections; Infections; Lung Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Masking Details: Allocation of images was performed before inclusion of participants began. Allocation process ensured that is was unnecessary for the investigator to assess the randomization.
• Purpose: DIAGNOSTIC
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical professor at University of Copenhagen, MD, PhD

Model Details: In a crossover and multi-reader multi-case study, physicians read CXRs from acute dyspnoic patients. Each physician retrospectively interprets each image twice in two sessions - once with and once without AI-support in random order.The wash-out period was a minimum four weeks. The images were randomly allocated to the physicians via block randomization. Each image was viewed by at least one physician once with and once without AI on trial day 1.

Study Record Dates

• First Submitted: November 1, 2021
• First Posted: November 11, 2021
• Study Start: October 19, 2021
• Primary Completion: February 1, 2022
• Study Completion: July 1, 2022
• Last Updated: January 11, 2022

Record last verified: 2021-12

Locations

DK (1)