Early Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Clinical Data

Condition(s) studied

Investigational drug(s) / intervention(s)

Artificial Intelligence-Based Risk Prediction: 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 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.

Eligibility

Primary outcome measure(s)

• Accuracy of bronchopulmonary dysplasia (BPD) risk prediction by artificial intelligence (AI) models in preterm infants. — 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.

Trial sites (1)

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