Artificial Intelligence–Driven Respiratory Distress Syndrome Prediction for Very Low Birth Weight Infants: Korean Multicenter Prospective Cohort Study
BackgroundRespiratory distress syndrome (RDS) is a disease that commonly affects premature infants whose lungs are not fully developed. RDS results from a lack of surfactant in the lungs. The more premature the infant is, the greater is the likelihood of having RDS. However,...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2023-07-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2023/1/e47612 |
| _version_ | 1797734046179000320 |
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| author | Woocheol Jang Yong Sung Choi Ji Yoo Kim Dong Keon Yon Young Joo Lee Sung-Hoon Chung Chae Young Kim Seung Geun Yeo Jinseok Lee |
| author_facet | Woocheol Jang Yong Sung Choi Ji Yoo Kim Dong Keon Yon Young Joo Lee Sung-Hoon Chung Chae Young Kim Seung Geun Yeo Jinseok Lee |
| author_sort | Woocheol Jang |
| collection | DOAJ |
| description |
BackgroundRespiratory distress syndrome (RDS) is a disease that commonly affects premature infants whose lungs are not fully developed. RDS results from a lack of surfactant in the lungs. The more premature the infant is, the greater is the likelihood of having RDS. However, even though not all premature infants have RDS, preemptive treatment with artificial pulmonary surfactant is administered in most cases.
ObjectiveWe aimed to develop an artificial intelligence model to predict RDS in premature infants to avoid unnecessary treatment.
MethodsIn this study, 13,087 very low birth weight infants who were newborns weighing less than 1500 grams were assessed in 76 hospitals of the Korean Neonatal Network. To predict RDS in very low birth weight infants, we used basic infant information, maternity history, pregnancy/birth process, family history, resuscitation procedure, and test results at birth such as blood gas analysis and Apgar score. The prediction performances of 7 different machine learning models were compared, and a 5-layer deep neural network was proposed in order to enhance the prediction performance from the selected features. An ensemble approach combining multiple models from the 5-fold cross-validation was subsequently developed.
ResultsOur proposed ensemble 5-layer deep neural network consisting of the top 20 features provided high sensitivity (83.03%), specificity (87.50%), accuracy (84.07%), balanced accuracy (85.26%), and area under the curve (0.9187). Based on the model that we developed, a public web application that enables easy access for the prediction of RDS in premature infants was deployed.
ConclusionsOur artificial intelligence model may be useful for preparations for neonatal resuscitation, particularly in cases involving the delivery of very low birth weight infants, as it can aid in predicting the likelihood of RDS and inform decisions regarding the administration of surfactant. |
| first_indexed | 2024-03-12T12:38:34Z |
| format | Article |
| id | doaj.art-9dda4388bf13467e8c30994ded53f196 |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-12T12:38:34Z |
| publishDate | 2023-07-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-9dda4388bf13467e8c30994ded53f1962023-08-29T00:02:41ZengJMIR PublicationsJournal of Medical Internet Research1438-88712023-07-0125e4761210.2196/47612Artificial Intelligence–Driven Respiratory Distress Syndrome Prediction for Very Low Birth Weight Infants: Korean Multicenter Prospective Cohort StudyWoocheol Janghttps://orcid.org/0000-0003-4728-5448Yong Sung Choihttps://orcid.org/0000-0001-9181-7849Ji Yoo Kimhttps://orcid.org/0009-0007-7154-4629Dong Keon Yonhttps://orcid.org/0000-0003-1628-9948Young Joo Leehttps://orcid.org/0000-0001-5294-7368Sung-Hoon Chunghttps://orcid.org/0000-0002-0352-9722Chae Young Kimhttps://orcid.org/0000-0002-9905-6519Seung Geun Yeohttps://orcid.org/0000-0001-8021-1024Jinseok Leehttps://orcid.org/0000-0002-8580-490X BackgroundRespiratory distress syndrome (RDS) is a disease that commonly affects premature infants whose lungs are not fully developed. RDS results from a lack of surfactant in the lungs. The more premature the infant is, the greater is the likelihood of having RDS. However, even though not all premature infants have RDS, preemptive treatment with artificial pulmonary surfactant is administered in most cases. ObjectiveWe aimed to develop an artificial intelligence model to predict RDS in premature infants to avoid unnecessary treatment. MethodsIn this study, 13,087 very low birth weight infants who were newborns weighing less than 1500 grams were assessed in 76 hospitals of the Korean Neonatal Network. To predict RDS in very low birth weight infants, we used basic infant information, maternity history, pregnancy/birth process, family history, resuscitation procedure, and test results at birth such as blood gas analysis and Apgar score. The prediction performances of 7 different machine learning models were compared, and a 5-layer deep neural network was proposed in order to enhance the prediction performance from the selected features. An ensemble approach combining multiple models from the 5-fold cross-validation was subsequently developed. ResultsOur proposed ensemble 5-layer deep neural network consisting of the top 20 features provided high sensitivity (83.03%), specificity (87.50%), accuracy (84.07%), balanced accuracy (85.26%), and area under the curve (0.9187). Based on the model that we developed, a public web application that enables easy access for the prediction of RDS in premature infants was deployed. ConclusionsOur artificial intelligence model may be useful for preparations for neonatal resuscitation, particularly in cases involving the delivery of very low birth weight infants, as it can aid in predicting the likelihood of RDS and inform decisions regarding the administration of surfactant.https://www.jmir.org/2023/1/e47612 |
| spellingShingle | Woocheol Jang Yong Sung Choi Ji Yoo Kim Dong Keon Yon Young Joo Lee Sung-Hoon Chung Chae Young Kim Seung Geun Yeo Jinseok Lee Artificial Intelligence–Driven Respiratory Distress Syndrome Prediction for Very Low Birth Weight Infants: Korean Multicenter Prospective Cohort Study Journal of Medical Internet Research |
| title | Artificial Intelligence–Driven Respiratory Distress Syndrome Prediction for Very Low Birth Weight Infants: Korean Multicenter Prospective Cohort Study |
| title_full | Artificial Intelligence–Driven Respiratory Distress Syndrome Prediction for Very Low Birth Weight Infants: Korean Multicenter Prospective Cohort Study |
| title_fullStr | Artificial Intelligence–Driven Respiratory Distress Syndrome Prediction for Very Low Birth Weight Infants: Korean Multicenter Prospective Cohort Study |
| title_full_unstemmed | Artificial Intelligence–Driven Respiratory Distress Syndrome Prediction for Very Low Birth Weight Infants: Korean Multicenter Prospective Cohort Study |
| title_short | Artificial Intelligence–Driven Respiratory Distress Syndrome Prediction for Very Low Birth Weight Infants: Korean Multicenter Prospective Cohort Study |
| title_sort | artificial intelligence driven respiratory distress syndrome prediction for very low birth weight infants korean multicenter prospective cohort study |
| url | https://www.jmir.org/2023/1/e47612 |
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