Artificial Intelligence Versus Anesthesiology Residents in Chest X-Ray Interpretation

NCT07846553 | Not Yet Recruiting

• 1 other identifier: KCH-PAAG-AI-2026-1081
• Study Type: observational
• Target: 35
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Thoracic Diseases

Keywords

Artificial Intelligence; Chest Radiography; Anesthesiology Residents; Diagnostic Accuracy; hChestXR

Outcome Measures

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.

  During completion of the 150-radiograph assessment, within the 1-month study period

Secondary Outcomes (5)

• Sensitivity for Detection of Thoracic Abnormalities

  During completion of the 150-radiograph assessment, within the 1-month study period

• Specificity for Detection of Thoracic Abnormalities

  During completion of the 150-radiograph assessment, within the 1-month study period

• Positive Predictive Value

  During completion of the 150-radiograph assessment, within the 1-month study period

• Negative Predictive Value

  During completion of the 150-radiograph assessment, within the 1-month study period

• Agreement With the Radiologist Reference Standard

  During completion of the 150-radiograph assessment, within the 1-month study period

Study Arms (1)

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study 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.

You may qualify if:

• 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.

You may not qualify if:

• Failure to complete the 150-radiograph assessment.
• Withdrawal of consent before completion of the assessment.

Sponsors & Collaborators

• Çiğdem Ünal Kantekin: lead

Study Sites (1)

Kayseri City Hospital

Kayseri, 38080, Turkey (Türkiye)

Location

Related Publications (4)

• Colquitt J, Jordan M, Court R, Loveman E, Parr J, Ghosh I, Auguste P, Patel M, Stinton C. Artificial intelligence software for analysing chest X-ray images to identify suspected lung cancer: an evidence synthesis early value assessment. Health Technol Assess. 2024 Aug;28(50):1-75. doi: 10.3310/LKRT4721.

  PMID: 39254229 BACKGROUND

• Nam JG, Park S, Hwang EJ, Lee JH, Jin KN, Lim KY, Vu TH, Sohn JH, Hwang S, Goo JM, Park CM. Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs. Radiology. 2019 Jan;290(1):218-228. doi: 10.1148/radiol.2018180237. Epub 2018 Sep 25.

  PMID: 30251934 BACKGROUND

• Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, Duan T, Ding D, Bagul A, Langlotz CP, Patel BN, Yeom KW, Shpanskaya K, Blankenberg FG, Seekins J, Amrhein TJ, Mong DA, Halabi SS, Zucker EJ, Ng AY, Lungren MP. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 2018 Nov 20;15(11):e1002686. doi: 10.1371/journal.pmed.1002686. eCollection 2018 Nov.

  PMID: 30457988 BACKGROUND

• Eisen LA, Berger JS, Hegde A, Schneider RF. Competency in chest radiography. A comparison of medical students, residents, and fellows. J Gen Intern Med. 2006 May;21(5):460-5. doi: 10.1111/j.1525-1497.2006.00427.x.

  PMID: 16704388 BACKGROUND

MeSH Terms

Conditions

Thoracic Diseases

Condition Hierarchy (Ancestors)

Respiratory Tract Diseases

Study Officials

• Çiğdem Ünal Kantekin, MD

  Kayseri City Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Çiğdem Ünal Kantekin, MD

CONTACT

+90 505 443 30 56; drcgdm@hotmail.com

Yusuf Yılmaz, MD

CONTACT

+90 555 538 30 94; doktoryusufyilmaz@gmail.com

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER GOV
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Associate Professor of Anesthesiology and Reanimation

Study Record Dates

• First Submitted: September 23, 2026
• First Posted: September 29, 2026
• Study Start (Estimated): October 15, 2026
• Primary Completion (Estimated): November 15, 2026
• Study Completion (Estimated): November 15, 2026
• Last Updated: September 29, 2026

Record last verified: 2026-09

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)