Early Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Clinical Data

NCT07525167 | Not Yet Recruiting

• 1 other identifier: KSH_MB_2026_1
• Study Type: observational
• Target: 108
• Locations: 1 country
• Sites: 1

Brief 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 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.

Conditions

Bronchopulmonary Dysplasia

Keywords

Bronchopulmonary Dysplasia; Machine Learning; Artificial Intelligence; preterm infant

Outcome Measures

Primary Outcomes (1)

• 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.

  Postnatal weeks 1, 2, and 3

Secondary Outcomes (3)

• Sensitivity and specificity of AI predictions

  Postnatal weeks 1, 2, and 3

• Comparison of prediction accuracy across postnatal weeks

  Postnatal weeks 1, 2, and 3

• Comparative performance of different AI models

  Postnatal weeks 1, 2, and 3

Study Arms (1)

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.

Other: Artificial Intelligence-Based Risk Prediction

Interventions

Artificial Intelligence-Based Risk Prediction OTHER

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.

Preterm Infants Cohort (<32 weeks or <1500 g)

Eligibility Criteria

• Age: 0 Days - 28 Days
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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

You may not qualify if:

• Presence of major congenital anomalies
• Incomplete or missing clinical data
• Death shortly after birth with insufficient follow-up data to determine BPD status

Sponsors & Collaborators

• Konya City Hospital: lead

Study Sites (1)

Konya City Hospital, İstiklal, Adana Çevre Yolu Cd. No:135/1

Konya, 42020, Turkey (Türkiye)

Location

Related Publications (1)

• 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.

  BACKGROUND

MeSH Terms

Conditions

Bronchopulmonary Dysplasia; Premature Birth

Condition Hierarchy (Ancestors)

Ventilator-Induced Lung Injury; Lung Injury; Lung Diseases; Respiratory Tract Diseases; Infant, Premature, Diseases; Infant, Newborn, Diseases; Congenital, Hereditary, and Neonatal Diseases and Abnormalities; Obstetric Labor, Premature; Obstetric Labor Complications; Pregnancy Complications; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases

Central Study Contacts

Melek Büyükeren, Assoc. Prof. Dr.

CONTACT

+90532-780-30-78; melekbuyukeren@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 3 Weeks
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor, Department of Neonatology, Konya City Hospital

Study Record Dates

• First Submitted: April 6, 2026
• First Posted: April 13, 2026
• Study Start: May 1, 2026
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: April 15, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)