EDITORIAL
TINY PATIENTS, BIG DATA: HOW ARTIFICIAL INTELLIGENCECAN TRANSFORM NEONATOLOGY

UDK: 616-053.31:004.8613.952:004.8


Zdraveska N.

 

1University Clinic for Pediatric Diseases, Faculty of Medicine,SsCyrilandMethodiusUniversityinSkopje, NorthMacedonia

 

 

Artificial intelligence (AI) has emerged as a transformative technology, becoming an integral part of our daily lives, particularly in healthcare. It has the potential to harness vast amounts of data, serving as a powerful tool to support clinical decisions, provide personalized care, deliver accurate prognostics, and enhance patient safety.

 

References:

 

  1. Rowe M. An introduction to machine learning for clinicians. Acad Med. 2019;94:1433–6.
  2. Hoodbhoy Z, Masroor Jeelani S, Aziz A, et al. Machinelearningforchildandadolescenthealth: asystematicr Pediatrics. 2021 Jan;147(1):e2020011833.
  3. Ostojic D, Guglielmini S, Moser V, et al. Reducingfalsealarmratesinneonatalintensivecare: anewmachinelearninga Adv Exp Med Biol. 2020;1232:285-290.
  4. Kumar N, Akangire G, Sullivan B, et al. Continuous vital sign analysis for predicting and preventing neonatal diseases in the twenty-first century: big data to the forefront. Pediatr Res. 2020 Jan;87(2):210-220.
  5. Cabrera-Quiros L, Kommers D, Wolvers MK, et al. Predictionoflate-onsetsepsisinpreterminfantsusingmonitoringsignalsandmachinel Crit Care Explor. 2021 Jan 27;3(1):e0302.
  6. Hicks JF, Fairchild K. HeRO monitoring in the NICU: sepsis detection and beyond. Infant 2013; 9(6): 187-91.
  7. Moorman JR, Carlo WA, Kattwinkel J, et al. Mortality reduction by heart rate characteristic monitoring in very low birth weight neonates: a randomized trial. J Pediatr. 2011 Dec;159(6):900-6.e1.
  8. Fairchild KD, Schelonka RL, Kaufman DA, et al. Septicemia mortality reduction in neonates in a heart rate characteristics monitoring trial. Pediatr Res. 2013 Nov;74(5):570-5.
  9. Verstraete EH, Blot K, Mahieu L, et al. Prediction models for neonatal health care-associated sepsis: a meta-analysis. Pediatrics. 2015 Apr;135(4):e1002-14.
  10. Chung HU, Rwei AY, Hourlier-Fargette A, et al. Skin-interfaced biosensors for advanced wireless physiological monitoring in neonatal and pediatric intensive-care units. Nat Med. 2020 Mar;26(3):418-429.
  11. Shellhaas RA. Continuous long-term electroencephalography: the gold standard for neonatal seizure diagnosis. Semin Fetal Neonatal Med. 2015 Jun;20(3):149-53.
  12. Pavel AM, Rennie JM, de Vries LS, et al. A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial. Lancet Child Adolesc Health. 2020 Oct;4(10):740-749.
  13. Raurale SA, Boylan GB, Mathieson SR, et al. Grading hypoxic-ischemic encephalopathy in neonatal EEG with convolutional neural networks and quadratic time-frequency distributions. J Neural Eng. 2021 Mar 19;18(4):046007.
  14. Mooney C, O’Boyle D, Finder M, et al. Predictive modelling of hypoxic ischaemic encephalopathy risk following perinatal asphyxia. Heliyon. 2021 Jun 29;7(7):e07411.
  15. O’Boyle DS, Dunn WB, O’Neill D, et al.. Improvementinthepredictionofneonatalhypoxic-ischemicencephalopathywiththeintegrationofumbilicalcordmetabolitesandcurrentclinicalm J Pediatr. 2021 Feb;229:175-181.e1.
  16. Vartdal G, RandebergLise L, Aune A, Kringstad A, Picterus AS. Imagebasedbilirubindetermination. EP patent EP 3393353 A4. 2019 2016/12/15.
  17. Taylor JA, Stout JW, de Greef L, et al. Useof a smartphoneapptoassessneonatalj Pediatrics. 2017 Sep;140(3):e20170312.
  18. Aune A, Vartdal G, Bergseng H, et al. Bilirubin estimates from smartphone images of newborn infants’ skin correlated highly to serum bilirubin levels. Acta Paediatr. 2020 Dec;109(12):2532-2538.
  19. Aune A, Jímenez-Díaz G, Gierman LM, et al. Smartphone-based screening of neonatal jaundice in three populations in low and middle-income countries: a cross-sectional study. BMJ Paediatr Open. 2025 May 8;9(1):e002242.
  20. Porras AR, Rosenbaum K, Tor-Diez C, et al. Development and evaluation of a machine learning-based point-of-care screening tool for genetic syndromes in children: a multinational retrospective study. Lancet Digit Health. 2021 Oct;3(10):e635-e643.