Bronchopulmonary DysplasiaNCT07525167
Early Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Clinical Data
An observational study of Bronchopulmonary Dysplasia, run by Konya City Hospital at 1 site in Turkey (Türkiye).
Trial summary
Early Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Clinical Data from the First Three Postnatal Weeks with Large Language Models: A Retrospective Study This retrospective, observational study aims to evaluate the early prediction of bronchopulmonary dysplasia (BPD) in preterm infants using… Read more about this study
- Who is it for?
- Ages 0 to 28 days
- Conditions the study is for
- Bronchopulmonary Dysplasia
- What is being tested?
- Artificial Intelligence-Based Risk Prediction
- How many people
- 108 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Oct 2026to 31 Dec 2026
- How long will I take part?
- 3 Weeks
- 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 0 to 28 days
- All sexes
- No healthy volunteers
- Child
Study population: A retrospective cohort of preterm infants (<32 weeks gestation or <1,500 g) admitted to the NICU of Konya City Hospital. Only infants with complete clinical data and documented BPD status are included. Both sexes are included.
Who may be able to join
- Preterm infants born before 32 weeks of gestation or with birth weight <1,500 grams
- Admitted and followed in the Neonatal Intensive Care Unit (NICU) of Konya City Hospital
- Availability of complete clinical data in hospital records
- Documented bronchopulmonary dysplasia (BPD) outcome status
Who cannot join
- Presence of major congenital anomalies
- Incomplete or missing clinical data
- Death shortly after birth with insufficient follow-up data to determine BPD status
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Turkey (Türkiye)1 site
Konya City Hospital, İstiklal, Adana Çevre Yolu Cd. No:135/1
Konya, 42020
About this study
Official title: Early Prediction of Bronchopulmonary Dysplasia Using Clinical Data From the First Three Postnatal Weeks in Preterm Infants: A Retrospective Study With Large Language Models
Early Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Clinical Data from the First Three Postnatal Weeks with Large Language Models: A Retrospective Study This retrospective, observational study aims to evaluate the early prediction of bronchopulmonary dysplasia (BPD) in preterm infants using clinical data from the first, second, and third postnatal weeks. The study includes infants born before 32 weeks of gestation or weighing less than 1,500 grams, followed at the Neonatal Intensive Care Unit of Konya City Hospital.
The study will compare the performance of different large language models (LLMs), including ChatGPT, Gemini, and Claude, in predicting BPD development. Clinical variables such as gestational age, birth weight, respiratory support, oxygen requirement, mechanical ventilation duration, and infection status will be used.
Read more
Primary outcome: Accuracy of BPD risk prediction by each AI model compared to actual clinical outcomes. Secondary outcomes: Sensitivity and specificity of predictions, weekly prediction performance, and comparative performance among AI models.
The results will provide insight into the potential clinical utility of AI-based approaches for early BPD risk assessment in preterm infants.
Read the detailed description
Premature birth remains a major risk factor for neonatal morbidity and mortality, with bronchopulmonary dysplasia (BPD) representing one of the most significant chronic pulmonary complications in very preterm infants. Despite advances in neonatal intensive care, early and accurate prediction of BPD remains challenging due to the multifactorial nature of its pathophysiology, involving respiratory support requirements, oxygen exposure, infection burden, and perinatal factors.
This retrospective study evaluates the feasibility of using large language models (LLMs) for early prediction of BPD based on structured clinical data extracted from neonatal intensive care unit (NICU) records. Clinical variables are organized into weekly datasets corresponding to the first, second, and third postnatal weeks to capture the dynamic evolution of respiratory status and clinical condition over time.
Standardized and anonymized patient-level datasets are formatted into structured prompts and provided to multiple LLMs (ChatGPT, Gemini, and Claude). Each model receives identical input variables to ensure comparability. The models are instructed to generate categorical risk stratification (low, medium, high) along with corresponding probability estimates for BPD development.
To ensure methodological consistency, prompt engineering is standardized across all models and time points. Outputs are recorded for each weekly time window, allowing temporal comparison of predictive performance and assessment of how early postnatal data influences model accuracy.
Model outputs are subsequently compared with confirmed clinical outcomes of BPD development in the study population. Performance evaluation focuses on discriminative ability and calibration of predictions across different time points and models.
This design enables a systematic assessment of the potential role of LLM-based approaches in neonatal risk stratification and provides insight into their applicability as supportive clinical decision-making tools in neonatal intensive care settings.
- Conditions
- Bronchopulmonary Dysplasia
- Being tested
- Artificial Intelligence-Based Risk Prediction
- Keywords
- Bronchopulmonary Dysplasia, Machine Learning, Artificial Intelligence, preterm infant
- Medical subject headings
- Bronchopulmonary Dysplasia, Premature Birth
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence-Based Risk PredictionOther
Different large language models (ChatGPT, Gemini, Claude) will analyze retrospective clinical data to predict the risk of bronchopulmonary dysplasia (BPD). This is an observational evaluation; no experimental treatment or therapy is administered.
| Study group | Role | What they receive |
|---|---|---|
| Preterm Infants Cohort (<32 weeks or <1500 g) | This cohort includes preterm infants born before 32 weeks of gestation or weighing less than 1,500 grams, followed at the Neonatal Intensive Care Unit of Konya City Hospital. Clinical data from the first, second, and third postnatal weeks are retrospectively collected for analysis. No interventions are applied; AI models are used to predict BPD risk based on existing clinical data. Receives: Other: Artificial Intelligence-Based Risk Prediction |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of bronchopulmonary dysplasia (BPD) risk prediction by artificial intelligence (AI) models in preterm infants.
The primary outcome is the accuracy of different large language models (ChatGPT, Gemini, Claude) in predicting BPD development. AI-generated risk predictions will be compared to actual clinical outcomes to assess prediction correctness.
Measured: Postnatal weeks 1, 2, and 3
Secondary outcomes (3)
Sensitivity and specificity of AI predictions
Evaluate the true positive rate (sensitivity) and true negative rate (specificity) of each AI model's BPD risk predictions compared to actual outcomes.
Measured: Postnatal weeks 1, 2, and 3
Comparison of prediction accuracy across postnatal weeks
Compare AI model performance at different postnatal weeks to determine if prediction accuracy improves as more clinical data becomes available.
Measured: Postnatal weeks 1, 2, and 3
Comparative performance of different AI models
Compare AI model performance at different postnatal weeks to determine if prediction accuracy improves as more clinical data becomes available.
Measured: Postnatal weeks 1, 2, and 3
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Apr 2026
- Study start1 Oct 2026 (estimated)
- Primary completion1 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Bronchopulmonary DysplasiaStudies of the same condition, open and under way.
- 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
- Prospective
- Enrolment
- 108 participants (estimated)
- Target duration
- 3 Weeks
Sponsor and investigators
- Lead sponsor
- Konya City Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Melek Buyukeren, Konya City Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07525167
- Sponsor's protocol ID
- KSH_MB_2026_1
- Status last verified
- Jul 2026
- First submitted
- 6 Apr 2026
- Last updated
- 9 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: This study uses retrospective, de-identified clinical data from preterm infants admitted to the Neonatal Intensive Care Unit of Konya City Hospital. All patient information has been anonymized to protect privacy and confidentiality. Due to the sensitive nature of neonatal health data and institutional regulations, individual participant data (IPD) will not be shared with other researchers. The study results will be reported in aggregate form only, ensuring that no identifiable information is disclosed.
Publications
1. Dai D, Chen H, Dong X, Chen J, Mei M, Lu Y, et al. Bronchopulmonary dysplasia predicted by developing a machine learning model of genetic and clinical information. Front Genet. 2021;12:689071. 2. Choi HJ, Lee G, Shin SH, Lee SM, Lee HC, Sohn JA, et al. Development and external validation of a machine learning model to predict bronchopulmonary dysplasia using dynamic factors. Sci Rep. 2025;15:13620. 3. Chen Y, Ma H, Liu X. Clinical and imaging data-based machine learning for early diagnosis of bronchopulmonary dysplasia: A meta-analysis. Curr Med Imaging. 2025;21:e15734056421036. 4. Özçelik G, Erol S, Korkut S, Köse Çetinkaya A, Özçelik H. Prediction of bronchopulmonary dysplasia using machine learning from chest X-rays of premature infants in the neonatal intensive care unit. Medicine (Baltimore).2025;104:e44322. 5. Akila K, Aravind Babu LR. Deep learning-driven early prediction of bronchopulmonary dysplasia using chest X-rays and clinical data. Electronics Communications and Computing Summit. 2025;3(3):90-97. 6. Zhang X, Wang Y, Li J, et al. Development and validation of machine learning models for predicting bronchopulmonary dysplasia risk in preterm neonates based on antenatal determinants. BMC Pediatr. 2025. 7. Li Y, Wang L, Chen Z, et al. Machine learning models combining oversampling techniques for prediction of bronchopulmonary dysplasia-associated pulmonary hypertension in very preterm infants. Respir Res. 2024;25:199.
Study at a glance
From the registry record, last updated by the sponsor on 9 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Early Prediction of Bronchopulmonary Dysplasia Using Clinical Data From the First Three Postnatal Weeks in Preterm Infants: A Retrospective Study With Large Language Models
- ClinicalTrials.gov ID
- NCT07525167
- Acronym / sponsor ID
- KSH_MB_2026_1
- Conditions
- Bronchopulmonary Dysplasia
- Keywords
- Bronchopulmonary Dysplasia, Machine Learning, Artificial Intelligence, preterm infant
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 108 participants (estimated)
- Lead sponsor
- Konya City HospitalUniversity, hospital or other
- Study start
- 1 Oct 2026 (estimated)
- Primary completion
- 1 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 13 Apr 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.
Early Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Clinical Data from the First Three Postnatal Weeks with Large Language Models: A Retrospective Study This retrospective, observational study aims to evaluate the early prediction of bronchopulmonary dysplasia (BPD) in preterm infants using clinical data from the first, second, and third postnatal weeks. The study includes infants born before 32 weeks of gestation or weighing less than 1,500 grams, followed at the Neonatal Intensive Care Unit of Konya City Hospital.
The study will compare the performance of different large language models (LLMs), including ChatGPT, Gemini, and Claude, in predicting BPD development. Clinical variables such as gestational age, birth weight, respiratory support, oxygen requirement, mechanical ventilation duration, and infection status will be used.
Primary outcome: Accuracy of BPD risk prediction by each AI model compared to actual clinical outcomes. Secondary outcomes: Sensitivity and specificity of predictions, weekly prediction performance, and comparative performance among AI models.
The results will provide insight into the potential clinical utility of AI-based approaches for early BPD risk assessment in preterm infants.
Detailed description
Premature birth remains a major risk factor for neonatal morbidity and mortality, with bronchopulmonary dysplasia (BPD) representing one of the most significant chronic pulmonary complications in very preterm infants. Despite advances in neonatal intensive care, early and accurate prediction of BPD remains challenging due to the multifactorial nature of its pathophysiology, involving respiratory support requirements, oxygen exposure, infection burden, and perinatal factors.
This retrospective study evaluates the feasibility of using large language models (LLMs) for early prediction of BPD based on structured clinical data extracted from neonatal intensive care unit (NICU) records. Clinical variables are organized into weekly datasets corresponding to the first, second, and third postnatal weeks to capture the dynamic evolution of respiratory status and clinical condition over time.
Standardized and anonymized patient-level datasets are formatted into structured prompts and provided to multiple LLMs (ChatGPT, Gemini, and Claude). Each model receives identical input variables to ensure comparability. The models are instructed to generate categorical risk stratification (low, medium, high) along with corresponding probability estimates for BPD development.
To ensure methodological consistency, prompt engineering is standardized across all models and time points. Outputs are recorded for each weekly time window, allowing temporal comparison of predictive performance and assessment of how early postnatal data influences model accuracy.
Model outputs are subsequently compared with confirmed clinical outcomes of BPD development in the study population. Performance evaluation focuses on discriminative ability and calibration of predictions across different time points and models.
This design enables a systematic assessment of the potential role of LLM-based approaches in neonatal risk stratification and provides insight into their applicability as supportive clinical decision-making tools in neonatal intensive care settings.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 3 Weeks
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Preterm Infants Cohort (<32 weeks or <1500 g) | – | Other: Artificial Intelligence-Based Risk PredictionThis cohort includes preterm infants born before 32 weeks of gestation or weighing less than 1,500 grams, followed at the Neonatal Intensive Care Unit of Konya City Hospital. Clinical data from the first, second, and third postnatal weeks are retrospectively collected for analysis. No interventions are applied; AI models are used to predict BPD risk based on existing clinical data. |
Who may qualify
- Ages
- 0 to 28 days
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child
- Study population
- A retrospective cohort of preterm infants (<32 weeks gestation or <1,500 g) admitted to the NICU of Konya City Hospital. Only infants with complete clinical data and documented BPD status are included. Both sexes are included.
Key inclusion criteria (4 listed)
- Preterm infants born before 32 weeks of gestation or with birth weight <1,500 grams
- Admitted and followed in the Neonatal Intensive Care Unit (NICU) of Konya City Hospital
- Availability of complete clinical data in hospital records
- Documented bronchopulmonary dysplasia (BPD) outcome status
Key exclusion criteria (3 listed)
- Presence of major congenital anomalies
- Incomplete or missing clinical data
- Death shortly after birth with insufficient follow-up data to determine BPD status
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Intelligence-Based Risk PredictionDifferent large language models (ChatGPT, Gemini, Claude) will analyze retrospective clinical data to predict the risk of bronchopulmonary dysplasia (BPD). This is an observational evaluation; no experimental treatment or therapy is administered. | Other | Preterm Infants Cohort (<32 weeks or <1500 g) |
Primary outcome
Accuracy of bronchopulmonary dysplasia (BPD) risk prediction by artificial intelligence (AI) models in preterm infants.
Time frame: Postnatal weeks 1, 2, and 3
The primary outcome is the accuracy of different large language models (ChatGPT, Gemini, Claude) in predicting BPD development. AI-generated risk predictions will be compared to actual clinical outcomes to assess prediction correctness.
3 secondary outcomes
Sensitivity and specificity of AI predictions
Time frame: Postnatal weeks 1, 2, and 3
Comparison of prediction accuracy across postnatal weeks
Time frame: Postnatal weeks 1, 2, and 3
Comparative performance of different AI models
Time frame: Postnatal weeks 1, 2, and 3
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 9 Jul 2026.
- Background1. Dai D, Chen H, Dong X, Chen J, Mei M, Lu Y, et al. Bronchopulmonary dysplasia predicted by developing a machine learning model of genetic and clinical information. Front Genet. 2021;12:689071. 2. Choi HJ, Lee G, Shin SH, Lee SM, Lee HC, Sohn JA, et al. Development and external validation of a machine learning model to predict bronchopulmonary dysplasia using dynamic factors. Sci Rep. 2025;15:13620. 3. Chen Y, Ma H, Liu X. Clinical and imaging data-based machine learning for early diagnosis of bronchopulmonary dysplasia: A meta-analysis. Curr Med Imaging. 2025;21:e15734056421036. 4. Özçelik G, Erol S, Korkut S, Köse Çetinkaya A, Özçelik H. Prediction of bronchopulmonary dysplasia using machine learning from chest X-rays of premature infants in the neonatal intensive care unit. Medicine (Baltimore).2025;104:e44322. 5. Akila K, Aravind Babu LR. Deep learning-driven early prediction of bronchopulmonary dysplasia using chest X-rays and clinical data. Electronics Communications and Computing Summit. 2025;3(3):90-97. 6. Zhang X, Wang Y, Li J, et al. Development and validation of machine learning models for predicting bronchopulmonary dysplasia risk in preterm neonates based on antenatal determinants. BMC Pediatr. 2025. 7. Li Y, Wang L, Chen Z, et al. Machine learning models combining oversampling techniques for prediction of bronchopulmonary dysplasia-associated pulmonary hypertension in very preterm infants. Respir Res. 2024;25:199.
- Individual participant dataSharing planned: No. This study uses retrospective, de-identified clinical data from preterm infants admitted to the Neonatal Intensive Care Unit of Konya City Hospital. All patient information has been anonymized to protect privacy and confidentiality. Due to the sensitive nature of neonatal health data and institutional regulations, individual participant data (IPD) will not be shared with other researchers. The study results will be reported in aggregate form only, ensuring that no identifiable information is disclosed.
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Bronchopulmonary Dysplasia, run by Konya City Hospital at 1 site in Turkey (Türkiye).
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 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: opening soon, as of the sponsor's last update on 9 Jul 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 Turkey (Türkiye). 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?
Contact Melek Büyükeren, Assoc. Prof. Dr. (+90532-780-30-78, melekbuyukeren@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of bronchopulmonary dysplasia (BPD) risk prediction by artificial intelligence (AI) models in preterm infants. (time frame: Postnatal weeks 1, 2, and 3). 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 NCT07525167, which also lists 1 linked publication.
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 9 Jul 2026.