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PneumoniaINTERPRETNCT07744555

Change in the Proportion of Correct Interpretations of Pulmonary Opacities

A study testing Artificial intelligence expert system in people with Pneumonia and Radiography, run by Universidad de la Sabana at 1 site in Colombia.

Active, not recruiting

Under way, not looking for participants. Everyone needed has enrolled. The study is still running.

Official record
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Trial summary

This randomized crossover clinical trial will evaluate whether an artificial intelligence-based expert system improves the interpretation of chest radiographs by final-year medical students, rural physicians, and general practitioners with less than two years of clinical experience. Read more about this study

Who is it for?
Ages 18 years and olderHealthy volunteers can take part
Conditions the study is for
Pneumonia, Radiography
What is being tested?
Artificial intelligence expert system
Type of treatment
Medical device
Could I receive a placebo?
No, everyone receives an active treatment
How many people
50 participants
Where
1 siteColombia
Study dates
1 Jul 2026to Dec 2026
Phase
Interventional
Are there results?
Not yet: the study is under way

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 18 years and older
  • All sexes
  • Healthy volunteers accepted
  • Adult, Older adult

Who may be able to join

  • Final-year undergraduate medical students.
  • Physicians completing mandatory rural or social service.
  • Licensed general practitioners with less than two years of clinical experience.
  • Willingness to participate as a chest radiograph reader.
  • Ability to complete both study reading sessions.

Who cannot join

  • Corrected visual acuity worse than 20/60 in the better-seeing eye.
  • Visual-field restriction, impaired contrast perception, binocular vision abnormalities, or another visual condition that prevents adequate interpretation of chest radiographs.
  • Inability to complete the visual acuity assessment before the reading session.

Trial locations and contacts

1 site in 1 country.

Colombia1 site
  • Clínica Universidad de La Sabana

    Chía, Cundinamarca, 111321

Other trials in Colombia

About this study

Official title: Change in the Proportion of Correct Interpretations of Pulmonary Opacities by General Practitioners Using an Expert System

This randomized crossover clinical trial will evaluate whether an artificial intelligence-based expert system improves the interpretation of chest radiographs by final-year medical students, rural physicians, and general practitioners with less than two years of clinical experience.

Participants will interpret chest radiographs containing normal findings or pulmonary opacities classified as alveolar, interstitial, or mixed. Each participant will review the same set of radiographs twice: once without assistance from the expert system and once with assistance from the artificial intelligence system. The order of these two reading conditions will be randomly assigned, with a six-week interval between sessions to reduce memory effects.

Read more

The main outcome will be the proportion of correct interpretations compared with a previously established reference standard based on radiologist interpretation supported by chest computed tomography findings. The study will also assess diagnostic confidence and agreement with the reference standard.

This study does not involve treatment decisions or direct patient care. It is intended to determine whether artificial intelligence support may improve the accuracy and confidence of less-experienced physicians when interpreting pulmonary opacities on chest radiographs.

Read the detailed description

Study Design

A randomized, controlled, two-period crossover trial with a six-week washout period will be conducted to evaluate the change in the proportion of correct interpretations of pulmonary opacities on chest radiographs by final-year medical students, physicians in mandatory rural or social service, and general practitioners with less than two years of clinical experience, before and after assistance from an artificial intelligence-based image-recognition expert system.

Each participant serves as their own control, allowing direct comparison of interpretation performance with and without assistance from the expert system. Each participant evaluates a set of chest radiographs under two conditions: Phase A, interpretation without expert-system assistance, and Phase B, interpretation with expert-system assistance. The order of the phases is randomized. Sequence 1 begins with Phase A and, after a six-week washout period, proceeds to Phase B; Sequence 2 begins with Phase B and, after the washout period, proceeds to Phase A. The six-week interval is considered sufficient to reduce memory or learning effects. During this period participants have no access to the previously evaluated images and receive no feedback about their responses.

Expert System

The expert system is a freely available, open-source deep-learning model for computer-aided interpretation of chest radiographs that reports the presence of pulmonary opacities. Its output is displayed together with each image during Phase B only. The system is used exclusively as a reading aid for study participants; it is not used for clinical care, and no diagnostic, treatment or management decision for any patient is based on its output.

Study Population

Participants are final-year undergraduate medical students, physicians completing mandatory rural or social service, and licensed general practitioners with less than two years of clinical experience who agree to participate as chest radiograph readers. Eligible participants are randomly selected from the available list and invited individually to participate. Participants with corrected visual acuity worse than 20/60 (0.3 on the decimal scale) in the better-seeing eye are excluded, because such a limitation in resolving fine detail may be a source of performance bias when interpreting digital radiographs on calibrated monitors, independently of the intervention. Visual acuity is verified by documentation of acuity with corrective lenses, when applicable, or by a simple visual acuity test before the reading session.

Participant-level variables collected are sex, prior training in radiology or radiograph interpretation, and previous rotation or work experience in radiology services. Image-level variables are the type of pulmonary opacity according to Fleischner Society terminology (alveolar, interstitial or mixed), laterality, and severity of pulmonary involvement. The study is conducted at Universidad de La Sabana and Clinica Universidad de La Sabana.

Reference Standard

The reference standard was established in a previous study conducted by the same research group and is not generated in the present trial. In that study, two board-certified radiologists independently interpreted each chest radiograph under blinded conditions, without knowledge of the other reader's findings or of the expert-system output. Radiographs classified as normal and radiographs with abnormal findings were included. When the two readers disagreed, the case was reviewed by a third board-certified radiologist who examined the radiograph together with the corresponding chest computed tomography examination; that reading defines the final reference standard for the case.

In the present protocol the assigned radiologist does not redefine the reference standard. The radiologist only verifies that the images used correspond to the previously classified database and supervises the technical allocation of cases, without modifying the original diagnoses. All comparisons involving the expert system and the participants are therefore made against a single, previously established diagnosis.

All radiographs are reviewed by the supervising radiologist to confirm uniform technical quality, including adequate penetration, inspiration and centering; technically suboptimal images are excluded. Only cases in which the interval between chest radiography and chest computed tomography is no longer than seven days are selected, in order to reduce bias from disease progression and to ensure stability of the opacity pattern.

Primary Outcome Definition

The primary outcome is dichotomous: correct or incorrect interpretation, defined hierarchically. An interpretation is classified as globally correct only when both criteria are met in comparison with the reference standard: the participant correctly identifies whether a pulmonary opacity is present or absent, and, when an opacity is present, correctly classifies the pattern as alveolar, interstitial or mixed. Secondary analyses evaluate separately the diagnostic accuracy of opacity detection (sensitivity and specificity), the accuracy of pattern classification, diagnostic confidence, and agreement with the reference standard.

Sample Size

The sample size was calculated from the expected proportion of pulmonary opacity detection with and without the expert system, assuming a two-sided significance level of 0.05, 90% power, a minimum clinically relevant difference of 5 percentage points and a within-subject standard deviation of 10 percentage points. The required sample size was 47 participants; allowing for 10% loss during data collection, the final sample size is 50 participants, each serving as their own control.

Each participant completes 16 interpretations without expert-system assistance and 16 interpretations with assistance, for a total of 32 readings. The image database contains 410 chest radiographs from hospitalized patients. For each participant the image set is balanced and includes 4 radiographs with an alveolar pattern, 4 with an interstitial pattern, 4 with a mixed pattern and 4 normal radiographs, so that every participant evaluates the different patterns under comparable conditions.

Data Collection

Chest radiographs and chest computed tomography examinations from hospitalized patients are evaluated retrospectively. Eligible cases are patients who underwent both studies within a maximum interval of 15 days, between January 2019 and May 2024. Data are collected from records available in the hospital database of Universidad de La Sabana using an electronic form designed for this study, which includes participants' sociodemographic variables and the variables required for classification of pulmonary opacities. Data collection is supervised by a general practitioner with experience in radiology, who trains participants on completion of the form.

Immediately after recording each interpretation, participants rate their confidence regarding the presence or absence of a pulmonary opacity on a 5-point ordinal Likert scale: 1, none (pure guess); 2, low (less than 50% confidence); 3, moderate (50-70%); 4, high (70-90%); 5, very high (90-100%).

Before the sessions begin, the correct functioning of the electronic form and of the expert-system software is verified. Participants receive an explanation of the study protocol and of the measures adopted to protect data confidentiality, and are blinded to the clinical diagnoses of the cases. One reading session is conducted per participant in each phase. Images are interpreted in a quiet, distraction-free room with ambient lighting appropriate for radiological interpretation, on calibrated monitors with adequate resolution and brightness. Each participant uses the same workstation, and mobile devices are not permitted during the sessions.

Bias Control

Observer bias is minimized by interpreting radiographs under blinded conditions, without access to clinical information or chest computed tomography findings. Selection bias is reduced by random sampling of eligible participants and by the crossover design, in which each participant serves as their own control. Verification bias is controlled by comparing all interpretations with the previously established reference standard; no new reference standard is generated and the images are not reinterpreted during the study. Interobserver variability is reduced through standardized training of participants and evaluators, and randomization of the image presentation order reduces bias related to learning effects or visual fatigue.

Randomization and Allocation Concealment

An independent investigator, not involved in recruitment or assessment, generates the allocation sequence for the 50 participants using R software version 2025.09.1+401; set.seed() ensures reproducibility and the sample() function assigns participants to Sequence 1 or Sequence 2.

Allocation is concealed using sequentially numbered, opaque, sealed envelopes. The same independent investigator prepares 50 identical envelopes, each containing a card with the assigned sequence. Envelopes are opaque, sealed and consecutively numbered from 1 to 50, and are kept by the principal investigator responsible for data management. Allocation occurs only after the participant has met all eligibility criteria and signed the informed consent form; the participant's name is written on the next consecutively numbered envelope, which is then opened to irreversibly reveal the assigned sequence. This procedure prevents the recruiter and the participant from knowing the assigned sequence in advance.

Each participant evaluates exactly the same chest radiographs in both phases, so that diagnostic difficulty is identical under both conditions and any observed difference can be attributed to the expert system. The order of image presentation within each phase is randomized independently for each participant using the sample() function in R.

Statistical Analysis

Data are analyzed with Stata version 17 (StataCorp LLC, College Station, TX, USA). A descriptive analysis of all variables is performed first. Categorical variables are summarized with absolute and relative frequencies; continuous variables are presented as mean and standard deviation when normally distributed and as median and interquartile range otherwise, with normality assessed by the Shapiro-Wilk test.

Because each participant serves as their own control, paired tests are used. McNemar's test compares, between the phase without and the phase with expert-system assistance, the proportion of globally correct interpretations, the proportion of correct detections and the proportion of correct pattern classifications. The Wilcoxon signed-rank test compares median diagnostic confidence scores between phases.

Percentage agreement between participant interpretations and the reference standard is calculated, and Cohen's kappa is estimated to assess diagnostic reproducibility, interpreted according to Landis and Koch as poor (<0.20), fair (0.21-0.40), moderate (0.41-0.60), substantial (0.61-0.80) and almost perfect (>0.80). Effect size is expressed as differences in proportions with 95% confidence intervals. Prevalence-adjusted and bias-adjusted kappa estimates are also calculated.

To explore carryover effects, a conditional logistic regression model including the order of exposure as a covariate is used; this model also allows adjustment for prior experience in radiograph interpretation, sex and level of medical training. Statistical significance is set at a two-sided p-value below 0.05. All analyses follow the intention-to-treat principle, including all participants assigned to the study regardless of whether they complete both phases.

Results will be reported in accordance with the CONSORT extension for randomized crossover trials, the STARD guidelines for diagnostic accuracy studies, and the GRRAS guidelines for reliability and agreement studies.

Being tested
Artificial intelligence expert system
Keywords
Chest Radiography, Pulmonary Opacity, Artificial Intelligence, Diagnostic Accuracy, Crossover Trial
Medical subject headings
Pneumonia

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • Artificial intelligence expert systemDevice

    Artificial intelligence-based expert system for computer-aided interpretation of chest radiographs. The system is an open-source deep-learning model that analyzes posteroanterior chest radiographs and reports the presence of pulmonary opacities. During the intervention phase, each participant interprets the same set of chest radiographs while the output of the expert system is displayed together with each image. Participants remain responsible for the final interpretation and record it on the study data form. The expert system is used only as a reading aid for study participants; it is not used for clinical care, and no diagnostic, treatment, or management decision for any patient is based on its output.

Study groups in NCT07744555
Study groupRoleWhat they receive
Group A without artificial intelligenceNo interventionReading condition without expert-system assistance (Phase A). Participants interpret the assigned set of chest radiographs on a calibrated workstation using their own judgement only, with no output from the artificial intelligence expert system and without access to clinical information or chest computed tomography findings. For each image, participants record whether a pulmonary opacity is present, classify the pattern as alveolar, interstitial or mixed, and rate their diagnostic confidence on a 5-point Likert scale.
Group B with artificial intelligenceActive comparatorReading condition with expert-system assistance (Phase B). Participants interpret the same set of chest radiographs on the same calibrated workstation, with the output of the artificial intelligence expert system displayed together with each image. For each image, participants record whether a pulmonary opacity is present, classify the pattern as alveolar, interstitial or mixed, and rate their diagnostic confidence on a 5-point Likert scale. Participants remain responsible for the final interpretation.
Receives: Device: Artificial intelligence expert system

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Proportion of globally correct interpretations of pulmonary opacities

    Proportion of chest radiographs interpreted correctly by the participant, compared with a previously established reference standard. An interpretation is counted as globally correct only when both criteria are met: (1) the participant correctly identifies whether a pulmonary opacity is present or absent, and (2) when an opacity is present, the participant correctly classifies the pattern as alveolar, interstitial or mixed. The outcome is expressed as the percentage of correct interpretations out of the 16 radiographs read in each condition, and is compared between the reading condition without expert-system assistance and the reading condition with expert-system assistance.

    Measured: Each of the two reading sessions, separated by a 6-week washout period

Secondary outcomes (4)
  1. Sensitivity and specificity for the detection of pulmonary opacities

    Diagnostic accuracy of participants for detecting the presence or absence of a pulmonary opacity on chest radiographs, expressed as sensitivity and specificity with 95% confidence intervals, using the previously established reference standard as the comparator. Values are calculated separately for the reading condition without expert-system assistance and the reading condition with expert-system assistance.

    Measured: Each of the two reading sessions, separated by a 6-week washout period

  2. Proportion of correctly classified pulmonary opacity patterns

    Among radiographs with a pulmonary opacity according to the reference standard, the proportion in which the participant correctly classifies the pattern as alveolar, interstitial or mixed, according to Fleischner Society terminology. Values are calculated separately for the reading condition without expert-system assistance and the reading condition with expert-system assistance.

    Measured: Each of the two reading sessions, separated by a 6-week washout period

  3. Diagnostic confidence score

    Self-reported confidence of the participant in the interpretation of each chest radiograph regarding the presence or absence of a pulmonary opacity, rated immediately after each reading on a 5-point ordinal Likert scale where 1 = none (pure guess), 2 = low, 3 = moderate, 4 = high and 5 = very high. Median scores are compared between the reading condition without expert-system assistance and the reading condition with expert-system assistance.

    Measured: Each of the two reading sessions, separated by a 6-week washout period

  4. Agreement between participant interpretation and the reference standard (Cohen's kappa)

    Level of agreement between the participant's interpretation and the previously established reference standard, estimated with Cohen's kappa coefficient. Kappa values are interpreted according to the Landis and Koch criteria as poor (<0.20), fair (0.21-0.40), moderate (0.41-0.60), substantial (0.61-0.80) and almost perfect (>0.80). Prevalence-adjusted and bias-adjusted kappa estimates are also calculated. Values are reported separately for the reading condition without expert-system assistance and the reading condition with expert-system assistance.

    Measured: Each of the two reading sessions, separated by a 6-week washout period

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted4 Aug 2026
  2. Study start1 Jul 2026
  3. Primary completionOct 2026 (estimated)
  4. Study completionDec 2026 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Interventional
Phase
Phase N/A
Primary purpose
Diagnosis
Allocation
Randomised
Intervention model
Crossover
Model description
Randomized, controlled, two-period crossover trial with a six-week washout period. Each participant serves as their own control and reads the same set of chest radiographs under two conditions: Phase A, interpretation without expert-system assistance, and Phase B, interpretation with expert-system assistance. The order of the phases is randomized: Sequence 1 begins with Phase A and, after the washout period, proceeds to Phase B; Sequence 2 begins with Phase B and, after the washout period, proceeds to Phase A. The washout period and the independently randomized image order in each phase minimize memory and learning effects.
Masking
None (open label)
Enrolment
50 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT07744555
Sponsor's protocol ID
20260301
Acronym
INTERPRET
Status last verified
Aug 2026
First submitted
1 Jul 2026
Last updated
4 Aug 2026
Results posted
No
Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
No

Publications

  • van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging. 2020 Aug 12;11(1):91. doi: 10.1186/s13244-020-00887-2.

    PubMed 32785796
  • McCague C, Ramlee S, Reinius M, Selby I, Hulse D, Piyatissa P, Bura V, Crispin-Ortuzar M, Sala E, Woitek R. Introduction to radiomics for a clinical audience. Clin Radiol. 2023 Feb;78(2):83-98. doi: 10.1016/j.crad.2022.08.149.

    PubMed 36639175
  • Gefter WB, Post BA, Hatabu H. Commonly Missed Findings on Chest Radiographs: Causes and Consequences. Chest. 2023 Mar;163(3):650-661. doi: 10.1016/j.chest.2022.10.039. Epub 2022 Dec 12.

    PubMed 36521560
  • Pesapane F, Gnocchi G, Quarrella C, Sorce A, Nicosia L, Mariano L, Bozzini AC, Marinucci I, Priolo F, Abbate F, Carrafiello G, Cassano E. Errors in Radiology: A Standard Review. J Clin Med. 2024 Jul 23;13(15):4306. doi: 10.3390/jcm13154306.

    PubMed 39124573
  • Lee CS, Nagy PG, Weaver SJ, Newman-Toker DE. Cognitive and system factors contributing to diagnostic errors in radiology. AJR Am J Roentgenol. 2013 Sep;201(3):611-7. doi: 10.2214/AJR.12.10375.

    PubMed 23971454
  • Itri JN, Tappouni RR, McEachern RO, Pesch AJ, Patel SH. Fundamentals of Diagnostic Error in Imaging. Radiographics. 2018 Oct;38(6):1845-1865. doi: 10.1148/rg.2018180021.

    PubMed 30303801
  • Zhang L, Wen X, Li JW, Jiang X, Yang XF, Li M. Diagnostic error and bias in the department of radiology: a pictorial essay. Insights Imaging. 2023 Oct 2;14(1):163. doi: 10.1186/s13244-023-01521-7.

    PubMed 37782396
  • Govindarajan A, Govindarajan A, Tanamala S, Chattoraj S, Reddy B, Agrawal R, Iyer D, Srivastava A, Kumar P, Putha P. Role of an Automated Deep Learning Algorithm for Reliable Screening of Abnormality in Chest Radiographs: A Prospective Multicenter Quality Improvement Study. Diagnostics (Basel). 2022 Nov 7;12(11):2724. doi: 10.3390/diagnostics12112724.

    PubMed 36359565
  • Test M, Shah SS, Monuteaux M, Ambroggio L, Lee EY, Markowitz RI, Bixby S, Diperna S, Servaes S, Hellinger JC, Neuman MI. Impact of clinical history on chest radiograph interpretation. J Hosp Med. 2013 Jul;8(7):359-64. doi: 10.1002/jhm.1991. Epub 2012 Nov 26.

    PubMed 23184766
  • Muthukrishnan N, Maleki F, Ovens K, Reinhold C, Forghani B, Forghani R. Brief History of Artificial Intelligence. Neuroimaging Clin N Am. 2020 Nov;30(4):393-399. doi: 10.1016/j.nic.2020.07.004. Epub 2020 Sep 18.

    PubMed 33038991
  • Liu R, Rong Y, Peng Z. A review of medical artificial intelligence. Glob Health J. 2020;4:42-5.

  • Daye D, Wiggins WF, Lungren MP, Alkasab T, Kottler N, Allen B, Roth CJ, Bizzo BC, Durniak K, Brink JA, Larson DB, Dreyer KJ, Langlotz CP. Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How? Radiology. 2022 Dec;305(3):555-563. doi: 10.1148/radiol.212151. Epub 2022 Aug 2.

    PubMed 35916673
  • Chang JY, Makary MS. Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging. Diagnostics (Basel). 2024 Jul 8;14(13):1456. doi: 10.3390/diagnostics14131456.

    PubMed 39001346
  • Hao J, Wong LM, Shan Z, Ai QYH, Shi X, Tsoi JKH, Hung KF. A Semi-Supervised Transformer-Based Deep Learning Framework for Automated Tooth Segmentation and Identification on Panoramic Radiographs. Diagnostics (Basel). 2024 Sep 3;14(17):1948. doi: 10.3390/diagnostics14171948.

    PubMed 39272733
  • Mese I, Taslicay CA, Sivrioglu AK. Improving radiology workflow using ChatGPT and artificial intelligence. Clin Imaging. 2023 Nov;103:109993. doi: 10.1016/j.clinimag.2023.109993. Epub 2023 Oct 6.

    PubMed 37812965

Questions about this trial

What is this clinical trial studying?

A study testing Artificial intelligence expert system in people with Pneumonia and Radiography, run by Universidad de la Sabana at 1 site in Colombia.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 5 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: under way, not looking for participants, as of the sponsor's last update on 4 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in Colombia. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

This study is still under way. Results are not available in the source record yet.

How can a doctor refer a patient or contact the research team?

The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Proportion of globally correct interpretations of pulmonary opacities (time frame: Each of the two reading sessions, separated by a 6-week washout period). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT07744555, which also lists 37 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Aug 2026.

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