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LSTM analysis of infant tidal breathing detects bronchopulmonary dysplasia with 97% accuracyNew AI tool reads baby breath to spot lung disease early

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Key Takeaway
Consider that LSTM analysis of tidal breathing may help detect BPD and predict lung function in infants and children.

This cohort study from the Basel-Bern Infant Lung Development cohort included 329 infants for bronchopulmonary dysplasia (BPD) detection and 135 school-age children for forced expiratory volume in one second (FEV1) prediction. The intervention analyzed tidal breathing flow time series using a recurrent neural network (LSTM), with individuals without respiratory disease as the comparator.

For BPD detection, the model achieved 97.0% accuracy, 100% specificity, 91.7% sensitivity, 100% precision, and an F1 score of 95.7%. For FEV1 prediction, the mean bias was -0.009 L (95% CI -0.091 to 0.074), with limits of agreement from -0.416 L to 0.399 L and a mean relative prediction error of 13.7%.

No safety or tolerability data were reported. The study had no reported limitations, funding, or conflicts of interest.

The practice relevance suggests that machine learning applied to tidal breathing measurements may provide a low-burden, minimal-cooperation approach for early respiratory disease detection and functional assessment across early life stages. However, this is observational evidence, and causal claims are not supported.

Imagine holding your newborn close and listening to their quiet breaths. You might not know it yet, but those simple sounds hold a secret. A new computer program can listen to these rhythms and find serious lung problems.

This matters because checking lungs in babies is very hard. Standard tests need big machines and a lot of help from the child. Many infants cannot hold their breath or follow instructions. This makes getting accurate results difficult for doctors.

But here is the twist. Researchers found that a computer can learn from simple breathing data. It does not need expensive gear or a calm baby. The technology works on normal air flow measurements taken over time.

Think of the breathing signal like a song. Each breath is a note in the melody. A healthy baby sings a steady tune. A baby with lung trouble sings a different song. The computer learns to hear the difference between these two melodies.

The team tested this idea on two groups of children. First, they looked at 329 infants born with lung issues. Next, they checked 135 older children to see if they could predict lung strength.

For the babies, the computer guessed correctly 97 out of 100 times. It never missed a case where the lung disease was present. It also never said a healthy baby had a problem. This means the tool is very precise.

For the older kids, the computer guessed how much air they could push out. The guess was very close to the real number. The difference was less than a cup of water. This shows the tool works for different ages.

This does not mean this treatment is available yet.

Doctors say this fits into the bigger picture of lung health. It gives a new way to check on kids who cannot do standard tests. It helps catch problems when they are small and easier to fix.

Parents can talk to their doctor about this new option. It might become a standard part of checking on sick babies soon. It could also help find problems in children who are too young for big tests.

The study had some limits. It used data from one specific group of children. The computer was trained on this group first. It worked well on new data, but more testing is needed.

More research will follow in the coming years. Scientists will test the tool in different hospitals. They will also check if it works for other lung conditions. This step-by-step process ensures the tool is safe and useful.

The future looks bright for early lung care. Simple breathing data can now tell a big story. Doctors will have a powerful new tool to help families.

Study Details

Study typeCohort
EvidenceLevel 3
PublishedApr 2026
View Original Abstract ↓
Objective: To test whether machine learning (ML) models trained on tidal breathing flow time series can discriminate between individuals with and without respiratory disease and predict lung function indices obtained from conventional pulmonary function testing. Background: Accurate assessment of respiratory function in infants and young children is challenging because conventional pulmonary function testing requires sophisticated equipment and/or active patient cooperation. Tidal breathing measurements, in contrast, can be obtained non-invasively with little or no patient cooperation and at low cost, yet their clinical utility has been limited. We hypothesized that sufficiently long tidal breathing flow time series contain clinically relevant information that can be extracted using a recurrent neural network known as a long short-term memory (LSTM) network. Approach: We evaluated LSTM models in two scenarios within the Basel-Bern Infant Lung Development cohort. First, we assessed the ability of a model trained on flow and derived volume time series to detect bronchopulmonary dysplasia (BPD) in 329 infants. Second, we examined whether a model trained on tidal breathing flow alone could predict forced expiratory volume in one second (FEV1) in 135 school-age children. Signals were filtered and normalized prior to model training, and performance was evaluated on held-out test datasets. Main results: For BPD detection, the model achieved 97.0% accuracy, 100% specificity, 91.7% sensitivity, 100% precision, and 95.7% F1 score. For FEV1 prediction, Bland-Altman analysis showed a mean bias of -0.009 L (95% CI -0.091 to 0.074), with limits of agreement of -0.416 L and 0.399 L. The mean relative prediction error was 13.7%. Significance: These findings demonstrate that temporal patterns in tidal breathing flow signals contain diagnostically and functionally relevant information. ML applied to tidal breathing measurements may provide a low-burden, minimal-cooperation approach for early respiratory disease detection and functional assessment across early life stages.
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