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.
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
Presence of major congenital anomalies
Incomplete or missing clinical data
Death shortly after birth with insufficient follow-up data to determine BPD status
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.
Cohort
Prospective
Artificial Intelligence-Based Risk Prediction
Other interventionDifferent 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.
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.
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.
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.
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.
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