Artificial intelligence in bronchopulmonary dysplasia- current research and unexplored frontiers

Geetha, O. et al. New Bpd-prevalence and risk factors for bronchopulmonary dysplasia/mortality in extremely low gestational age infants ≤ 28 weeks. J. Perinatol. 41, 1943–1950 (2021).

Article  PubMed  PubMed Central  Google Scholar 

Bonadies, L. et al. Present and future of bronchopulmonary dysplasia. J. Clin. Med. 9, 1539 (2020).

Rysavy, M. A. et al. Assessment of an updated neonatal research network extremely preterm birth outcome model in the vermont oxford network. JAMA Pediatr. 174, e196294 (2020).

Article  PubMed  PubMed Central  Google Scholar 

Northway, W. H., Rosan, R. C. & Porter, D. Y. Pulmonary disease following respirator therapy of hyaline-membrane disease. N. Engl. J. Med. 276, 357–368 (1967).

Article  PubMed  Google Scholar 

Patel, A. R., Patel, A. R., Singh, S., Singh, S. & Khawaja, I. Global initiative for chronic obstructive lung disease: the changes made. Cureus 11, e4985 (2019).

PubMed  PubMed Central  Google Scholar 

Iheanacho, I., Zhang, S., King, D., Rizzo, M. & Ismaila, A. S. Economic burden of chronic obstructive pulmonary disease (Copd): a systematic literature review. Int J. Chron. Obstruct Pulmon Dis. 15, 439–460 (2020).

Article  PubMed  PubMed Central  Google Scholar 

Jobe, A. H. The new bronchopulmonary dysplasia. Curr. Opin. Pediatr. 23, 167–172 (2011).

Article  PubMed  PubMed Central  Google Scholar 

Bhandari, V. Designing a better definition of bronchopulmonary dysplasia. Pediatr. Pulmonol. 54, 678–679 (2019).

Article  PubMed  Google Scholar 

Isayama, T. et al. Revisiting the definition of bronchopulmonary dysplasia. JAMA Pediatrics 171, 271 (2017).

Article  PubMed  Google Scholar 

Higgins, R. D. et al. Bronchopulmonary dysplasia: executive summary of a workshop. J. Pediatr. 197, 300–308 (2018).

Article  PubMed  PubMed Central  Google Scholar 

Smith, V. C. et al. Rehospitalization in the first year of life among infants with bronchopulmonary dysplasia. J. Pediatrics 144, 799–803 (2004).

Google Scholar 

Lapcharoensap, W., Lee, H. C., Nyberg, A. & Dukhovny, D. Health care and societal costs of bronchopulmonary dysplasia. Neoreviews 19, e211–e223 (2018).

Article  PubMed  PubMed Central  Google Scholar 

Van Marter, L. J. et al. Does bronchopulmonary dysplasia contribute to the occurrence of cerebral palsy among infants born before 28 weeks of gestation? Arch. Dis. Child Fetal Neonatal Ed. 96, F20–F29 (2011).

Article  PubMed  Google Scholar 

Twilhaar, E. S. et al. Cognitive outcomes of children born extremely or very preterm since the 1990s and associated risk factors: a meta-analysis and meta-regression. JAMA Pediatr. 172, 361–367 (2018).

Article  PubMed  PubMed Central  Google Scholar 

Morag, I. et al. Predictors of developmental and respiratory outcomes among preterm infants with bronchopulmonary dysplasia. Front Pediatr. 9, 780518 (2021).

Jeon, G. W., Oh, M. & Chang, Y. S. Definitions of bronchopulmonary dysplasia and long-term outcomes of extremely preterm infants in korean neonatal network. Sci. Rep. 11, 24349 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hintz, S. R., Kendrick, D. E., Vohr, B. R., Poole, W. K. & Higgins, R. D. Changes in neurodevelopmental outcomes at 18 to 22 months’ corrected age among infants of less than 25 weeks’ gestational age born in 1993–1999. Pediatrics 115, 1645–1651 (2005).

Article  PubMed  Google Scholar 

Doyle, L. W. et al. Bronchopulmonary dysplasia in very low birth weight subjects and lung function in late adolescence. Pediatrics 118, 108–113 (2006).

Article  PubMed  Google Scholar 

Choi, E. K., Shin, S. H., Kim, E. K. & Kim, H. S. Developmental outcomes of preterm infants with bronchopulmonary dysplasia-associated pulmonary hypertension at 18–24 months of corrected age. BMC Pediatr. 19, 26 (2019).

Han, Y. S., Kim, S. H. & Sung, T. J. Impact of the definition of bronchopulmonary dysplasia on neurodevelopmental outcomes. Sci. Rep. 11, 22589 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Saengrat, P. & Limrungsikul, A. predictive ability of the new bronchopulmonary dysplasia definition on pulmonary outcomes at 20 to 24 months’ corrected age of preterm infants. Am. J. Perinatol. https://doi.org/10.1055/s-0041-1735219 (2021).

Vyas-Read, S. et al. A comparison of newer classifications of bronchopulmonary dysplasia: findings from the children’s hospitals neonatal consortium severe Bpd group. J. Perinatol. 42, 58–64 (2022).

Article  PubMed  Google Scholar 

Laughon, M. M. et al. Prediction of bronchopulmonary dysplasia by postnatal age in extremely premature infants. Am. J. Respir. Crit. Care Med 183, 1715–1722 (2011).

Article  PubMed  PubMed Central  Google Scholar 

Saglani, S. & Custovic, A. Childhood asthma: advances using machine learning and mechanistic studies. Am. J. Respiratory Crit. Care Med. 199, 414–422 (2019).

Article  CAS  Google Scholar 

Verder, H. et al. Bronchopulmonary dysplasia predicted at birth by artificial intelligence. Acta Paediatr. 110, 503–509 (2021).

Article  CAS  PubMed  Google Scholar 

Dai, D. et al. Bronchopulmonary dysplasia predicted by developing a machine learning model of genetic and clinical information. Front Genet. 12, 689071 (2021).

Ochab, M. & Wajs, W. Expert system supporting an early prediction of the bronchopulmonary dysplasia. Comput Biol. Med 69, 236–244 (2016).

Article  PubMed  Google Scholar 

Shepherd, E. G. et al. Infant pulmonary function testing and phenotypes in severe bronchopulmonary dysplasia. Pediatrics 141, e20173350 (2018).

Article  PubMed  Google Scholar 

Jensen, E. A. & Schmidt, B. Epidemiology of bronchopulmonary dysplasia. Birth Defects Res. Part A: Clin. Mol. Teratol. 100, 145–157 (2014).

Article  CAS  Google Scholar 

Ochiai, M. et al. A new scoring system for computed tomography of the chest for assessing the clinical status of bronchopulmonary dysplasia. J. Pediatrics 152, 90–95.e93 (2008).

Article  Google Scholar 

Yoder, L. M. et al. Elevated lung volumes in neonates with bronchopulmonary dysplasia measured via Mri. Pediatr. Pulmonol. 54, 1311–1318 (2019).

PubMed  Google Scholar 

Ying, X. An overview of overfitting and its solutions. J. Phys.: Conf. Ser. 1168, 022022 (2019).

Google Scholar 

Khurshid, F. et al. Comparison of multivariable logistic regression and machine learning models for predicting bronchopulmonary dysplasia or death in very preterm infants. Front Pediatr. 9, 759776 (2021).

Dhindsa, K., Bhandari, M. & Sonnadara, R. R. What’s holding up the big data revolution in healthcare? BMJ 363, k5357 (2018).

Article  PubMed  Google Scholar 

Castelvecchi, D. Can we open the black box of Ai? Nature 538, 20–23 (2016).

Article  CAS  PubMed  Google Scholar 

Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206–215 (2019).

Article  PubMed  PubMed Central  Google Scholar 

Quinn, T. P., Jacobs, S., Senadeera, M., Le, V. & Coghlan, S. The three ghosts of medical Ai: can the black-box present deliver? Artif. Intell. Med. 124, 102158 (2022).

Article  PubMed  Google Scholar 

Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G. & King, D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 17, 195 (2019).

Article  PubMed  PubMed Central  Google Scholar 

Guo, L. L. et al. Systematic review of approaches to preserve machine learning performance in the presence of temporal dataset shift in clinical medicine. Appl Clin. Inf. 12, 808–815 (2021).

Article  Google Scholar 

留言 (0)

沒有登入
gif