Thoracic DiseasesNCT07834879
AI-Based Assessment of Chest Diseases on Chest X-ray Imaging Using DenseNet-121 and a CheXpert-Trained Model
An observational study of Thoracic Diseases and Lung Diseases, run by Assiut University.
Trial summary
Chest X-rays are widely used to detect thoracic and lung conditions, but reviewing high volumes of radiographs can lead to workload strain and variation between interpreters. Artificial intelligence (AI), particularly deep learning neural networks like DenseNet-121, has shown strong potential to assist clinicians with… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Thoracic Diseases, Lung Diseases
- How many people
- 100 participants
- Where
- No sites listed yet
- Study dates
- Oct 2026to Nov 2027
- Phase
- Observational
- 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
- Adult, Older adult
Study population: The study population comprises adult patients aged 18 years and older who undergo frontal chest radiography within institutional radiology departments. Eligible subjects have de-identified, diagnostic-quality frontal chest X-ray images with available reference diagnostic labels verified through expert radiologist consensus and chest CT adjudication. Pediatric cases, non-diagnostic images, incomplete metadata, and duplicate scans are excluded.
Who may be able to join
- Adult patients aged 18 years or older.
- Undergoing frontal chest radiography.
- Diagnostic-quality frontal chest radiographs.
- Availability of reference diagnostic labels (verified by expert radiologist consensus or chest CT).
Who cannot join
- Pediatric patients (under 18 years of age).
- Non-diagnostic or poor-quality chest radiographs.
- Incomplete clinical or imaging metadata.
- Duplicate radiographic images or repeat patient entries.
About this study
Official title: AI-Based Assessment of Chest Diseases on Chest X-ray Imaging Using DenseNet-121 and a CheXpert-Trained Model: A Comparative Diagnostic Accuracy Study
Chest X-rays are widely used to detect thoracic and lung conditions, but reviewing high volumes of radiographs can lead to workload strain and variation between interpreters. Artificial intelligence (AI), particularly deep learning neural networks like DenseNet-121, has shown strong potential to assist clinicians with automated image interpretation. However, AI models trained on large international datasets, such as CheXpert, may perform differently across distinct patient populations due to variations in imaging technique, patient demographics, and disease presentation.
The primary purpose of this study is to compare the diagnostic accuracy of a DenseNet-121 model trained or fine-tuned on local data against a DenseNet-121 model pretrained on the CheXpert dataset for identifying thoracic pathologies. Both models will evaluate de-identified frontal chest radiographs from adult patients. Model predictions will be compared against a reference standard established by expert radiologist consensus, with discordant findings resolved using chest computed tomography (CT). Findings will evaluate whether local model adaptation improves diagnostic precision and workflow efficiency in clinical settings.
- Conditions
- Thoracic Diseases, Lung Diseases
- Keywords
- DenseNet-121, CheXpert, Chest X-ray, Deep Learning, Artificial Intelligence, Diagnostic Accuracy, Convolutional Neural Network
- Medical subject headings
- Thoracic Diseases, Lung Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Adult Frontal Chest Radiography Cohort | This cohort includes adult patients aged 18 years and older who undergo diagnostic-quality frontal chest radiography with available reference diagnostic labels. De-identified chest radiographs from this group are evaluated by two deep learning architectures: a study-trained/fine-tuned DenseNet-121 convolutional neural network and a CheXpert-pretrained DenseNet-121 model. Model predictions are evaluated against reference standards derived from expert radiologist consensus, with discordant cases adjudicated via chest computed tomography (CT). |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under the Receiver Operating Characteristic Curve (AUROC)
AUROC will be calculated to assess and compare the diagnostic performance of the study-trained DenseNet-121 model versus the CheXpert-pretrained DenseNet-121 model in detecting thoracic pathologies on frontal chest radiographs. AI predictions will be compared against the reference standard of expert radiologist consensus, with discordant findings adjudicated by chest CT. AUROC values range from 0.5 (no discrimination) to 1.0 (perfect discrimination).
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Sep 2026
- Study startOct 2026 (estimated)
- Primary completionOct 2027 (estimated)
- Study completionNov 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Thoracic DiseasesStudies of the same condition, open and under way.
- More trials run by Assiut UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Assiut University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ibrahim Mohamed Ibrahim, Assiut University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07834879
- Sponsor's protocol ID
- AI Chest X-Ray Assessment
- Status last verified
- Sep 2026
- First submitted
- 17 Sep 2026
- Last updated
- 22 Sep 2026
- Results posted
- No
Study at a glance
From the registry record, last updated by the sponsor on 22 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-Based Assessment of Chest Diseases on Chest X-ray Imaging Using DenseNet-121 and a CheXpert-Trained Model: A Comparative Diagnostic Accuracy Study
- ClinicalTrials.gov ID
- NCT07834879
- Acronym / sponsor ID
- AI Chest X-Ray Assessment
- Conditions
- Thoracic Diseases, Lung Diseases
- Keywords
- DenseNet-121, CheXpert, Chest X-ray, Deep Learning, Artificial Intelligence, Diagnostic Accuracy, Convolutional Neural Network
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Assiut UniversityUniversity, hospital or other
- Study start
- Oct 2026 (estimated)
- Primary completion
- Oct 2027 (estimated)
- Study completion
- Nov 2027 (estimated)
- First posted
- 22 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Chest X-rays are widely used to detect thoracic and lung conditions, but reviewing high volumes of radiographs can lead to workload strain and variation between interpreters. Artificial intelligence (AI), particularly deep learning neural networks like DenseNet-121, has shown strong potential to assist clinicians with automated image interpretation. However, AI models trained on large international datasets, such as CheXpert, may perform differently across distinct patient populations due to variations in imaging technique, patient demographics, and disease presentation.
The primary purpose of this study is to compare the diagnostic accuracy of a DenseNet-121 model trained or fine-tuned on local data against a DenseNet-121 model pretrained on the CheXpert dataset for identifying thoracic pathologies. Both models will evaluate de-identified frontal chest radiographs from adult patients. Model predictions will be compared against a reference standard established by expert radiologist consensus, with discordant findings resolved using chest computed tomography (CT). Findings will evaluate whether local model adaptation improves diagnostic precision and workflow efficiency in clinical settings.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Adult Frontal Chest Radiography Cohort | – | –This cohort includes adult patients aged 18 years and older who undergo diagnostic-quality frontal chest radiography with available reference diagnostic labels. De-identified chest radiographs from this group are evaluated by two deep learning architectures: a study-trained/fine-tuned DenseNet-121 convolutional neural network and a CheXpert-pretrained DenseNet-121 model. Model predictions are evaluated against reference standards derived from expert radiologist consensus, with discordant cases adjudicated via chest computed tomography (CT). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- The study population comprises adult patients aged 18 years and older who undergo frontal chest radiography within institutional radiology departments. Eligible subjects have de-identified, diagnostic-quality frontal chest X-ray images with available reference diagnostic labels verified through expert radiologist consensus and chest CT adjudication. Pediatric cases, non-diagnostic images, incomplete metadata, and duplicate scans are excluded.
Key inclusion criteria (4 listed)
- Adult patients aged 18 years or older.
- Undergoing frontal chest radiography.
- Diagnostic-quality frontal chest radiographs.
- Availability of reference diagnostic labels (verified by expert radiologist consensus or chest CT).
Key exclusion criteria (4 listed)
- Pediatric patients (under 18 years of age).
- Non-diagnostic or poor-quality chest radiographs.
- Incomplete clinical or imaging metadata.
- Duplicate radiographic images or repeat patient entries.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Area Under the Receiver Operating Characteristic Curve (AUROC)
Time frame: Baseline
AUROC will be calculated to assess and compare the diagnostic performance of the study-trained DenseNet-121 model versus the CheXpert-pretrained DenseNet-121 model in detecting thoracic pathologies on frontal chest radiographs. AI predictions will be compared against the reference standard of expert radiologist consensus, with discordant findings adjudicated by chest CT. AUROC values range from 0.5 (no discrimination) to 1.0 (perfect discrimination).
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Sep 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Thoracic Diseases and Lung Diseases, run by Assiut University.
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 4 inclusion and 4 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: opening soon, as of the sponsor's last update on 22 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
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?
Area Under the Receiver Operating Characteristic Curve (AUROC) (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 22 Sep 2026.