Bronchopulmonary dysplasia predicted at birth by artificial intelligence

Aim To develop a fast bedside test for prediction and early targeted intervention of bronchopulmonary dysplasia (BPD) to improve the outcome. Methods In a multicentre study of preterm infants with gestational age 24-31 weeks, clinical data present at birth were combined with spectral data of gastric aspirate samples taken at birth and analysed using artificial intelligence. The study was designed to develop an algorithm to predict development of BPD. The BPD definition used was the consensus definition of the US National Institutes of Health: Requirement of supplemental oxygen for at least 28 days with subsequent assessment at 36 weeks postmenstrual age. Results Twenty-six (43%) of the 61 included infants developed BPD. Spectral data analysis of the gastric aspirates identified the most important wave numbers for classification and surfactant treatment, and birth weight and gestational age were the most important predictive clinical data. By combining these data, the resulting algorithm for early diagnosis of BPD had a sensitivity of 88% and a specificity of 91%. Conclusion A point-of-care test to predict subsequent development of BPD at birth has been developed using a new software algorithm allowing early targeted intervention of BPD which could improve the outcome.

Automated summary

By combining clinical data with spectral data analysis of gastric aspirates, a new algorithm has been developed for early diagnosis of bronchopulmonary dysplasia, achieving high sensitivity and specificity in predicting the development of BPD at birth.