bronchopulmonary dysplasia treatment :: Article CreatorAI Can Be Trained To Detect Lung Disease In Premature Babies, Research Suggests
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 disease in infants, because it is so difficult to assess their lung function.
"But there is an alternative. We can measure a baby's breathing while they sleep. All this needs is a soft face mask, with a sensor that can measure the air flow and volume entering and leaving the infant's nose. This equipment is cheap and available at any clinical facility.
"Such measurements of several consecutive breaths—what we call tidal breathing—can yield large amounts of good quality sequential flow data. We wanted to try and use this data to train an ANN to detect 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 artifacts, 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 recognize 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 analyzing 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"
The team now hope to investigate whether the model could also be used to test babies just a few weeks after birth, to analyze lung function and predict symptoms in older, school-age children, and to test for other conditions, such as asthma.
Professor Angela Zacharasiewicz is Chair of the ERS Pediatric Asthma and Allergy Group and Head of the Department of Pediatrics, Klinik Ottakring, and was not involved in the research.
She said, "Testing the function of the lung in premature babies using new techniques will improve therapeutical decision making. The earlier we can confirm BPD in a premature infant, the quicker we can make an informed decision about the best form of respiratory support to give them during their first weeks of life. It could also allow for the earlier planning of follow-up assessments and potential interventions, reducing stress for parents and their children.
"This research shows the huge potential AI has in simplifying this process. This technique could be used for testing larger numbers of babies and could also be applied to other diseases, such as asthma.
"It's exciting to see how AI tools like these can potentially support our health services."
More information: 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.[k4.Ersnet.Org/prod/v2/Front/Pr … ?E=549&session=17949]
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Medical Bulletin 11/ September/ 2024
Study Finds Connection Between Pregnancy Complications and Future Cardiovascular Risk In Menopause
Pregnancy-related hypertension has already been proven to lead to a number of negative health outcomes later in life, including more bothersome menopause symptoms like hot flashes, the risk of dementia, kidney problems, and stroke. A new study suggests it can also lead to cardiovascular disease during menopause. Results of the study were presented at the 2024 Annual Meeting of The Menopause Society in Chicago.
In the new study involving nearly 400 women with a mean age of 81.6 years, researchers sought to assess the association between a self-reported history of preeclampsia or eclampsia, gestational hypertension, and gestational diabetes with cardiovascular outcomes in postmenopausal women.
What they found is that a self-reported history of gestational hypertension was associated with cardiovascular disease. Women with preeclampsia or all three adverse pregnancy outcomes also had a numerically higher prevalence of heart disease, but it did not meet the standards of statistical significance. No association was found between gestational diabetes and heart disease.
"Future research based on a larger sample size is needed to better understand the role adverse pregnancy outcomes may have in cardiovascular disease development and risk stratification," says Marie Tan, lead author from Drexel University College of Medicine in Philadelphia.
Reference: 2024 Annual Meeting of The Menopause Society in Chicago September 10-14. Https://menopause.Org/press-releases/some-adverse-pregnancy-outcomes-may-increase-risk-of-heart-disease-later-in-life
Breast Arterial Calcification a Key Risk Factor For Cardiovascular Disease: Annual Menopause Society Meeting Highlights
Heart disease risk assessment tools specific to women remain lacking, despite the fact cardiovascular disease is the leading cause of mortality in women. A new study suggests that mammograms may pinpoint a key risk factor, demonstrating an association between breast arterial calcifications and cardiovascular disease. Results of the study were presented at the 2024 Annual Meeting of The Menopause Society in Chicago.
The results of a new study, though, suggest there could be a common risk factor found through a very routine medical tool—a mammogram. The study, which followed nearly 400 women for 18 years, demonstrates a potential association between breast arterial calcifications (which show up as white parallel lines on a mammogram and are typically benign) observed on mammography and cardiovascular disease.
More specifically, women with breast arterial calcifications were more likely to experience atherosclerotic cardiovascular disease (a type of heart disease that occurs when plaque builds up in the walls of the arteries to limit blood flow to the organs)—23% in those with breast arterial calcifications compared to 13.9% in those without.
"Prior research has already suggested this type of association," says Hannah Daley, lead author from Drexel University College of Medicine in West Reading, Pennsylvania. "But this study aimed to assess the longitudinal association between breast arterial calcifications and atherosclerotic heart disease. Based on what we found, we believe the presence of breast arterial calcifications on a mammogram should be routinely reported."
"Studies like this one are encouraging because they provide information on a tool that healthcare professionals could use to determine the risk of heart disease in women," says Dr. Stephanie Faubion, medical director for The Menopause Society. "In addition, it reinforces that the risk factors for cardiovascular disease in women are different than for men."
Reference: 2024 Annual Meeting of The Menopause Society in Chicago September 10-14. Https://menopause.Org/press-releases/breast-arterial-calcification-could-be-warning-sign-of-heart-disease
ESR Congress 2024 Highlights: AI Could Aid In Detecting Lung Disease in Premature Infants
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]
New Meta-Analysis Finds Targeted Vitamin D Intervention Strategies Effective for Cardiometabolic Health
A latest systematic review and meta-analysis conducted by scientists from institutions across China and the United States has uncovered promising insights into how vitamin D supplementation can significantly impact cardiometabolic health. The study, which was published in Engineering, has implications for tailored therapeutic strategies targeting cardiovascular diseases and related risks.
The comprehensive review analyzed 99 randomized controlled trials (RCTs) involving a total of 17 656 participants. The analysis revealed that vitamin D supplementation, with a median dose of 3320 International Units (IU) per day, was associated with favourable effects on various cardiometabolic risk factors, including reductions in systolic and diastolic blood pressure, total cholesterol, fasting blood glucose, haemoglobin A1C, and fasting blood insulin.
Significantly, the researchers discovered that the benefits of vitamin D supplementation were most pronounced in specific groups: non-Western populations, individuals with baseline 25-hydroxyvitamin D levels below 15.0 ng·mL−1, those with a body mass index (BMI) below 30 kg·m−2, and older individuals aged 50 years or above.
This research underscores the need for personalized vitamin D intervention strategies, taking into account individual characteristics such as ethnocultural background, age, BMI, and baseline vitamin D levels. The findings highlight the potential of longer intervention durations (three months or more) and higher doses to optimize cardiometabolic health outcomes in specific populations.
These findings could lead to significant advancements in preventive medicine and nutritional sciences, potentially leading to the development of more effective public health strategies. By tailoring vitamin D supplementation based on individual characteristics, healthcare providers may improve intervention efficacy and reduce the prevalence of cardiometabolic diseases.
The authors suggest that future research should focus on elucidating the mechanisms behind these observed effects and the potential benefits of vitamin D supplementation on chronic diseases such as cardiovascular diseases.
Reference: An, P., Wan, S., Wang, L., Xu, T., Xu, T., Wang, Y., Liu, J., Li, K., Wang, X., He, J., & Liu, S. (2024). Modifiers of the effects of vitamin D supplementation on cardiometabolic risk factors: A systematic review and meta-analysis. Engineering, 16, 120-130. Https://doi.Org/10.1016/j.Eng.2024.07.010
Artificial Intelligence Can Be Trained To Detect Lung Disease In Premature Babies
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 [1].
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 disease in infants, because it is so difficult to assess their lung function.
"But there is an alternative. We can measure a baby's breathing while they sleep. All this needs is a soft face mask, with a sensor that can measure the air flow and volume entering and leaving the infant's nose. This equipment is cheap and available at any clinical facility.
"Such measurements of several consecutive breaths - what we call tidal breathing - can yield large amounts of good quality sequential flow data. We wanted to try and use this data to train an ANN to detect 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"
The team now hope to investigate whether the model could also be used to test babies just a few weeks after birth, to analyse lung function and predict symptoms in older, school age children, and to test for other conditions, such as asthma.
Professor Angela Zacharasiewicz is Chair of the ERS Paediatric Asthma and Allergy Group and Head of the Department of Paediatrics, Klinik Ottakring, and was not involved in the research. She said: "Testing the function of the lung in premature babies using new techniques will improve therapeutical decision making. The earlier we can confirm BPD in a premature infant, the quicker we can make an informed decision about the best form of respiratory support to give them during their first weeks of life. It could also allow for the earlier planning of follow-up assessments and potential interventions, reducing stress for parents and their children.
"This research shows the huge potential AI has in simplifying this process. This technique could be used for testing larger numbers of babies and could also be applied to other diseases, such as asthma.
"It's exciting to see how AI tools like these can potentially support our health services."
(ends)
Method of Research
Observational study
Subject of Research
People
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