Artificial Intelligence Versus Anesthesiology Residents in Chest X-Ray Interpretation
ConditionThoracic Diseases
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorÇiğdem Ünal Kantekin
About this trial
This observational study will compare how accurately anesthesiology residents and an artificial intelligence system (hChestXR) identify abnormalities on chest X-rays. Approximately 35 volunteer anesthesiology residents at Kayseri City Hospital will review the same set of 150 anonymized posteroanterior chest radiographs selected from the hospital imaging archive. The set will include normal images and images showing findings such as infiltration, atelectasis, and pleural effusion. Two radiologists will independently assess the images and resolve disagreements by consensus to establish the reference standard. Residents will not see the radiologists' assessments or the artificial intelligence results while interpreting the images. The study will measure diagnostic accuracy and other measures of diagnostic performance. No additional imaging, treatment, or change in patient care is planned.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
An anesthesiology and reanimation resident working at Kayseri City Hospital.
Voluntarily agrees to participate in the study.
Provides written informed consent.
Agrees to evaluate the posteroanterior chest radiographs presented in the study.
Disqualifiers
Failure to complete the 150-radiograph assessment.
Withdrawal of consent before completion of the assessment.
Trial population
Approximately 35 adult anesthesiology and reanimation residents working at Kayseri City Hospital who volunteer and provide written informed consent. Enrollment refers to resident readers, not to the 150 retrospectively selected, anonymized chest radiographs used as the assessment dataset.
Trial design
Design model
Other
Time perspective
Cross-sectional
Treatments tested in this trial
Not listed
Trial groups
35 Participants
are grouped into 1 trial groupGroup A: Anesthesiology Residents
Trial outcomes
Primary outcomes
1
Diagnostic Accuracy for Detection of Thoracic Abnormalities
Percentage of correctly classified image-finding pairs, calculated as (true positives + true negatives) / all evaluated image-finding classifications × 100, using the consensus of two radiologists as the reference standard. Accuracy will be calculated for each resident and for hChestXR overall and separately for each predefined thoracic finding.
Time frame
During completion of the 150-radiograph assessment, within the 1-month study periodSecondary outcomes
1
Sensitivity for Detection of Thoracic Abnormalities
Percentage of reference-positive classifications correctly identified: true positives / (true positives + false negatives) × 100. Sensitivity will be calculated for residents and hChestXR overall and by finding, using the consensus of two radiologists as the reference standard.
Time frame
During completion of the 150-radiograph assessment, within the 1-month study period2
Specificity for Detection of Thoracic Abnormalities
Percentage of reference-negative classifications correctly identified: true negatives / (true negatives + false positives) × 100. Specificity will be calculated for residents and hChestXR overall and by finding, using the consensus of two radiologists as the reference standard.
Time frame
During completion of the 150-radiograph assessment, within the 1-month study period3
Positive Predictive Value
Percentage of positive classifications confirmed by the radiologist reference standard: true positives / (true positives + false positives) × 100. Calculated for residents and hChestXR overall and by finding within the constructed study dataset.
Time frame
During completion of the 150-radiograph assessment, within the 1-month study period4
Negative Predictive Value
Percentage of negative classifications confirmed by the radiologist reference standard: true negatives / (true negatives + false negatives) × 100. Calculated for residents and hChestXR overall and by finding within the constructed study dataset.
Time frame
During completion of the 150-radiograph assessment, within the 1-month study periodSponsors and contacts
Click on the lead sponsor to view all of their trials.
Çiğdem Ünal Kantekin
Lead sponsor
Kayseri City Hospital
Sponsor institution
Source ClinicalTrials.gov
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