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PD(AI): the role of artificial intelligence in the management of patent ductus arteriosus

Journal of Perinatology volume 43, pages 257–258 (2023)Cite this article

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We read with great interest the recent systemic review by McAdams at et al. on the promise of artificial intelligence (AI) in the management of common neonatal conditions (e.g., necrotizing enterocolitis, bronchopulmonary dysplasia, sepsis, etc.) ref. [1]. Neonatal hemodynamics represents another area where AI may be impactful, but was not discussed in this review. A hemodynamically significant patent ductus arteriosus (hsPDA) in premature infants presents significant challenges for neonatalogists and machine learning (ML) may prove beneficial in deciphering complicated decision pathways and predicting clinical outcomes, especially around the pharmacological approach to a hsPDA [2].

Despite the prevalence and potential morbidity of patent ductus arteriosus (PDA), there is no universal approach to closure when hemodynamic significance is appreciated. There have been over 70 randomized controlled trials assessing different pharmalogic strategies (prophylactic, pre-symptomatic, or symptomatic) and agents (Ibuprofen, Indomethacin, or Tylenol) ref. [3]. However, there still exists mixed consensus regarding pharmacotherapy use in hsPDAs and their relationship to clinical outcomes. Due to the uncertainty that exists surrounding the pharmacologic management of the PDA, clinicians and investigators have turned to AI as a possible solution [4, 5].

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References

  1. McAdams RM, Kaur R, Sun Y, Bindra H, Cho SJ, Singh H. Predicting clinical outcomes using artificial intelligence and machine learning in neonatal intensive care units: a systematic review. J Perinatol. 2022;42:1561–75.

    Article  Google Scholar 

  2. Hamrick SEG, Sallmon H, Rose AT, Porras D, Shelton EL, Reese J. et al. Patent ductus arteriosus of the preterm infant. Pediatrics. 2020;146:e20201209.

    Article  Google Scholar 

  3. Hundschied T, El-Khuffash A, McNamara PJ, de Boode WP. Survey highlighting the lack of consensus on diagnosis and treatment of patent ductus arteriosus in prematurity. Eur J Pediatr. 2022;181:2459–68.

    Article  Google Scholar 

  4. Na JY, Kim D, Kwon AM, Jeon JY, Kim H, Kim C, et al. Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort. Sci Rep. 2021;11:22353.

    Article  CAS  Google Scholar 

  5. Lei H, Ashrafi A, Chang P, Chang A, Lai W. Patent ductus arteriosus (PDA) detection in echocardiograms using deep learning. Intell-Based Med. 2022;6:100054.

    Article  Google Scholar 

  6. Ghorbani A, Ouyang D, Abid A, He B, Chen JH, Harrington RA, et al. Deep learning interpretation of echocardiograms. NPJ Digit Med. 2020;3:10.

    Article  Google Scholar 

  7. El-Khuffash A, James AT, Corcoran JD, Dicker P, Franklin O, Elsayed YN, et al. A patent ductus arteriosus severity score predicts chronic lung disease or death before discharge. J Pediatr. 2015;167:1354–61.

    Article  Google Scholar 

  8. Umapathi KK, Muller B, Sosnowski C, Thavamani A, Murphy J, Awad S, et al. A novel patent ductus arteriosus severity score to predict clinical outcomes in premature neonates. J Cardiovasc Dev Dis. 2022;9:114.

    Article  Google Scholar 

  9. Gopal G, Suter-Crazzolara C, Toldo L, Eberhardt W. Digital transformation in healthcare – architectures of present and future information technologies. Clin Chem Lab Med. 2019;57:328–35.

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Division of Newborn Medicine, Boston Children’s Hospital, Boston, MA, USA

    Puneet Sharma & Philip Levy

  2. Department of Neonatology, Beth Israel Deaconess Medical Center, Boston, MA, USA

    Kristyn Beam

  3. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Andrew L. Beam

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  1. Puneet Sharma
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  2. Kristyn Beam
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PS conducted the literature review and primarily drafted the manuscript. KB, PL, and AB helped develop the concept of the manuscript and assisted in its drafting. All authors reviewed and approved the final manuscript.

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Correspondence to Puneet Sharma.

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Sharma, P., Beam, K., Levy, P. et al. PD(AI): the role of artificial intelligence in the management of patent ductus arteriosus. J Perinatol 43, 257–258 (2023). https://doi.org/10.1038/s41372-023-01606-7

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