Artificial Neural Networks (ANNs) can be trained to detect lung disease in premature babies by analyzing their breathing patterns while they sleep, according to research presented at the European Respiratory Society (ERS) Congress in Vienna, Austria. The study was presented by Edgar Delgado-Eckert, adjunct professor at the Department of Biomedical Engineering at the University of Basel, and a research group leader at the University Children's Hospital, Switzerland. Bronchopulmonary dysplasia (BPD) is a breathing problem that can affect premature babies .
When a newborn's lungs are undeveloped at birth, they often need support from a ventilator or oxygen therapy—treatment which can stretch and inflame their lungs, causing BPD. But identifying BPD is difficult. Lung function tests usually require an adult to blow out on request—something babies cannot do—so current techniques require sophisticated equipment to measure an infant's lung ventilation characteristics.
As a result, BPD is one of only a few diseases that is typically diagnosed by the presence of one of its main causes, prematurity and respiratory support. ANNs are mathematical models used for classification and prediction. In order to make accurate predictions, an ANN needs to first be trained with a large amount of data, which presents a problem when it comes to BPD.
Professor Delgado-Eckert explains, "Until recently, this need for large amounts of data has hindered efforts to create accurate models for lung di.