ESR Congress 2024 Highlights: AI Could Aid In Detecting Lung Disease in Premature Infants - Video

Artificial Neural Networks (ANNs) can be trained to detect lung disease in premature babies by analysing 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.

Artificial Neural Networks are mathematical models used for classification and prediction. In order to make accurate predictions, an Artificial Neural Networks needs to first be trained with a large amount of data, which presents a problem when it comes to BPD.

Professor Delgado-Eckert’s team studied a group of 139 full term and 190 premature infants who had been assessed for BPD, recording their breathing for ten minutes while they slept. For each baby, 100 consecutive regular breaths, carefully inspected to exclude sighs or other artefacts, were used to train, validate, and test a type of ANN called a Long Short-Term Memory model (LSTM), which is particularly effective at classifying sequential data such as tidal breathing.

The team used 60% of the data to teach the network how to recognise BPD, 20% to validate the model (to ensure it wasn’t too fixed on the training data), and then fed the remaining 20% of the data to the model, unseen, to see if it could correctly identify those babies with BPD.

The LSTM model was able to classify a series of flow values in the unseen test data set as one that belonged to a patient who was diagnosed with BPD or not with 96% accuracy.

Professor Delgado-Eckert added: “Our research delivers, for the first time, a comprehensive way of analysing the breathing of infants, and allows us to detect which babies have BPD as early as one month of corrected age – the age they would be if they had been born on their due date – by using the ANN to identify abnormalities in their breathing patterns.

"Our non-invasive test is less distressing for the baby and their parents, means they can access treatment more quickly, and may also be relevant for their long-term prognosis"(10)

Reference: Abstract no: OA4655 “Detection of bronchopulmonary dysplasia (BPD) in preterm infants with an artificial neural network (ANN) trained using air flow time series (TS) measured during tidal breathing (Tb)”, by Edgar Delgado-Eckert et al; Presented in session, “Assessment of ventilation in awake and sleeping children” at 11:00-12:15 CEST on Tuesday 10 September 2024.

[https://k4.ersnet.org/prod/v2/Front/Program/Session?e=549&session=17949]