[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100658034":3},{"organization":4,"outcomesModule":7,"designInfo":30,"detailedDescription":33,"studyPopulation":34,"armGroups":35,"interventions":29,"overallOfficials":39,"centralContacts":43,"locations":52,"responsibleParty":65,"collaborators":29,"id":69,"slug":70,"hasResults":71,"nctId":72,"briefTitle":73,"officialTitle":74,"acronym":29,"eligibilityCriteria":75,"healthyVolunteers":76,"sex":77,"minAge":78,"maxAge":29,"enrollmentInfo":79,"targetDuration":29,"studyType":82,"phases":29,"briefSummary":83,"conditions":84,"keywords":86,"overallStatus":91,"whyStopped":29,"lastUpdateSubmitDate":92,"lastUpdatePostDateStruct":93,"startDateStruct":96,"completionDateStruct":98,"leadSponsor":100,"locationsCount":101},{"fullName":5,"class":6},"Kayseri City Hospital","OTHER_GOV",{"primaryOutcomes":8,"secondaryOutcomes":13,"otherOutcomes":29},[9],{"measure":10,"description":11,"timeFrame":12},"Diagnostic Accuracy for Detection of Thoracic Abnormalities","Percentage of correctly classified image-finding pairs, calculated as (true positives + true negatives) \u002F 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.","During completion of the 150-radiograph assessment, within the 1-month study period",[14,17,20,23,26],{"measure":15,"description":16,"timeFrame":12},"Sensitivity for Detection of Thoracic Abnormalities","Percentage of reference-positive classifications correctly identified: true positives \u002F (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.",{"measure":18,"description":19,"timeFrame":12},"Specificity for Detection of Thoracic Abnormalities","Percentage of reference-negative classifications correctly identified: true negatives \u002F (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.",{"measure":21,"description":22,"timeFrame":12},"Positive Predictive Value","Percentage of positive classifications confirmed by the radiologist reference standard: true positives \u002F (true positives + false positives) × 100. Calculated for residents and hChestXR overall and by finding within the constructed study dataset.",{"measure":24,"description":25,"timeFrame":12},"Negative Predictive Value","Percentage of negative classifications confirmed by the radiologist reference standard: true negatives \u002F (true negatives + false negatives) × 100. Calculated for residents and hChestXR overall and by finding within the constructed study dataset.",{"measure":27,"description":28,"timeFrame":12},"Agreement With the Radiologist Reference Standard","Agreement of resident interpretations and hChestXR classifications with the consensus radiologist reference standard will be assessed using Cohen's kappa. Cohen's kappa will be calculated separately for each resident and for hChestXR, with resident results summarized across readers. Kappa ranges from -1 to 1; 1 indicates perfect agreement, 0 agreement expected by chance, and negative values less agreement than expected by chance. Individual reader results will be evaluated within the multi-reader, multi-case framework.",null,{"allocation":29,"interventionModel":29,"interventionModelDescription":29,"primaryPurpose":29,"observationalModel":31,"timePerspective":32,"maskingInfo":29},"OTHER","CROSS_SECTIONAL","This single-center, cross-sectional, observational diagnostic accuracy study will be coordinated by the Department of Anesthesiology and Reanimation at Kayseri City Hospital. Volunteer resident assessments will be collected prospectively using retrospectively selected chest radiographs. The observational model is classified as Other because this is a comparative multi-reader, multi-case assessment of a fixed image set, without longitudinal follow-up.\n\nA dataset of 150 posteroanterior chest radiographs will be selected from the hospital PACS to represent normal findings, common thoracic abnormalities, and a limited number of atypical or diagnostically challenging cases. The distribution of findings will be balanced to avoid marked overrepresentation or underrepresentation of particular abnormalities. All patient-identifying information will be removed before assessment.\n\nTwo radiologists will independently evaluate each radiograph. Disagreements will be resolved through joint review and consensus. Their final assessments will serve as the reference standard. The same radiographs will be analyzed by the hChestXR artificial intelligence system and independently interpreted by approximately 35 anesthesiology residents who provide informed consent. Residents will record suspected findings using a predefined multiple-choice assessment form and will be blinded to the radiologist and artificial intelligence assessments. Resident training year will also be recorded.\n\nDiagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value will be calculated overall and by finding. Comparisons between resident and hChestXR performance will account for the paired image assessments and the multi-reader, multi-case structure of the data. McNemar testing will be used for appropriate paired comparisons, and analyses will account for variability across readers and radiographs. Agreement with the radiologist reference standard will be evaluated using Cohen's kappa. A multi-reader, multi-case approach will consider individual resident performance and variability across readers interpreting the same image set. Analyses will use SPSS version 31.0, with p \\\u003C 0.05 considered statistically significant.\n\nAn a priori sample size calculation was performed to determine the number of resident readers. Assuming an absolute difference of 15 percentage points in diagnostic accuracy between anesthesiology residents and hChestXR, a standard deviation of 25 percentage points in reader-level accuracy, a two-sided significance level of 0.05, and 90% power, the required minimum sample size was 32 residents (standardized effect size, Cohen's d = 0.60). To allow for incomplete assessments or participant withdrawal, at least 35 anesthesiology residents are planned for enrollment. The 150 radiographs constitute the image dataset and are distinct from the enrolled resident participants.\n\nThe study involves no patient intervention. Resident assessment data will be coded and de-identified for analysis, and identifying information will remain confidential. The planned study period is October 15, 2026, through November 15, 2026. Ethics approval was granted by the Kayseri City Hospital Non-Interventional Clinical Research Ethics Committee on August 26, 2026 (decision number 1081).","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.",[36],{"label":37,"type":29,"description":38,"interventionNames":29},"Anesthesiology Residents","Approximately 35 volunteer anesthesiology and reanimation residents at Kayseri City Hospital will independently interpret 150 anonymized posteroanterior chest radiographs using a predefined multiple-choice form. Residents will be blinded to radiologist assessments and hChestXR outputs. Their diagnostic performance will be compared with hChestXR analysis of the same images, using the consensus of two radiologists as the reference standard. The artificial intelligence system is an image-level comparator, not a separate participant cohort. No treatment is assigned and no patient care is changed.",[40],{"name":41,"affiliation":5,"role":42},"Çiğdem Ünal Kantekin, MD","PRINCIPAL_INVESTIGATOR",[44,48],{"name":41,"role":45,"phone":46,"phoneExt":29,"email":47},"CONTACT","+90 505 443 30 56","drcgdm@hotmail.com",{"name":49,"role":45,"phone":50,"phoneExt":29,"email":51},"Yusuf Yılmaz, MD","+90 555 538 30 94","doktoryusufyilmaz@gmail.com",[53],{"facility":5,"status":29,"city":54,"state":29,"zip":55,"country":56,"countryCode":29,"cosmosGeoPoint":57,"geoPoint":62,"contacts":63},"Kayseri","38080","Turkey (Türkiye)",{"type":58,"coordinates":59},"Point",[60,61],35.48528,38.73222,{"lat":61,"lon":60},[64],{"name":41,"role":45,"phone":46,"phoneExt":29,"email":47},{"type":66,"investigatorFullName":67,"investigatorTitle":68,"investigatorAffiliation":5,"oldNameTitle":29,"oldOrganization":29},"SPONSOR_INVESTIGATOR","Çiğdem Ünal Kantekin","Associate Professor of Anesthesiology and Reanimation","100658034","artificial-intelligence-versus-anesthesiology-residents-in-chest-x-ray-interpretation-100658034",false,"NCT07846553","Artificial Intelligence Versus Anesthesiology Residents in Chest X-Ray Interpretation","Comparison of the Diagnostic Performance of an Artificial Intelligence System and Anesthesiology Residents in Interpreting Posteroanterior Chest Radiographs","Inclusion Criteria:\n\n* An anesthesiology and reanimation resident working at Kayseri City Hospital.\n* Voluntarily agrees to participate in the study.\n* Provides written informed consent.\n* Agrees to evaluate the posteroanterior chest radiographs presented in the study.\n\nExclusion Criteria:\n\n* Failure to complete the 150-radiograph assessment.\n* Withdrawal of consent before completion of the assessment.",true,"ALL","18 Years",{"count":80,"type":81},35,"ESTIMATED","OBSERVATIONAL","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.",[85],"Thoracic Diseases",[87,88,37,89,90],"Artificial Intelligence","Chest Radiography","Diagnostic Accuracy","hChestXR","NOT_YET_RECRUITING","2026-09-23",{"date":94,"type":95},"2026-09-29","ACTUAL",{"date":97,"type":81},"2026-10-15",{"date":99,"type":81},"2026-11-15",{"name":67,"class":6},1]