Unlocking Neonatal Care: Innovative Technology’s Promise in Low-to-Middle Income Countries
Keywords:
Neonatal care, Artificial intelligence, Virtual reality, Sepsis, AsphyxiaAbstract
There is a critical role of artificial intelligence (AI) and virtual reality (VR) in revolutionising neonatal care and reducing neonatal mortality rates. AI has the potential, through machine learning and data analytics, to assist healthcare professionals in early identification and precise diagnosis of critical conditions, ultimately leading to improved outcomes. Additionally, AI and VR both offer opportunities in remote monitoring, telemedicine, and real-time decision support. This is especially crucial in low and middle income countries (LMICs) as it provides accessibility to healthcare and cost effective solutions. This essay delves into specific case studies, including predictive models for neonatal sepsis, immersive VR for training, and AI-driven analysis of infant cries to diagnose asphyxia. This essay will discuss the benefits of AI and VR in neonatal care, from early detection to resuscitation in LMICs. However, the limitations and challenges of AI implementation, including the need for high-quality data, potential biases, and ethical concerns are also acknowledged. The importance of a balanced approach, combining technology's capabilities with personalized care to advance neonatal health, improve outcomes, and reduce neonatal mortality rates worldwide must be underscored. This is because while AI and VR technologies offer valuable tools for improving healthcare delivery and outcomes, they cannot replace the personalized care provided by healthcare professionals. A balanced approach that integrates AI and VR with personalized care ensures that neonates receive comprehensive and holistic care that addresses their individual needs and circumstances.
Downloads
References
1. A child or youth died once every 4.4 seconds in 2021 – UN report [Internet]. World Health Organization; [cited 2023 Sept 1]. Available from: https://www.who.int/news/item/10-01-2023-a-child-or-youth-died-once-every-4.4-seconds-in-2021---un-report
2. Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: A systematic analysis for the global burden of disease study 2019. The Lancet. 2020;396(10258):1204–22. Doi:10.1016/s0140-6736(20)30925-9
3. Rewriting the Future for Children. Save the Children; 2007 [cited 2023 Sept 1]. Available from: https://www.savethechildren.org/content/dam/usa/reports/annual-report/annual-report/sc-2007-annualreport.pdf
4. Camacho-Gonzalez A, Spearman PW, Stoll BJ. Neonatal Infectious Diseases. Pediat Clin North Am. 2013 Apr;60(2):367–89.
5. Rosa-Mangeret F, Benski AC, Golaz A, Zala PZ, Kyokan M, Wagner LM, et al. 2.5 Million Annual Deaths—Are Neonates in Low- and Middle-Income Countries Too Small to Be Seen? A Bottom-Up Overview on Neonatal Morbi-Mortality. Trop Med Infect Dis. 2022;7(5):64. doi:https://doi.org/10.3390/tropicalmed7050064
6. Mani S, Shankle WR, Dick MB, Pazzani MJ. Two-stage machine learning model for guideline development. AI in Med. 1999;16(1):51–71. Doi:10.1016/s0933-3657(98)00064-5
7. Mani S, Ozdas A, Aliferis C, et al. Medical decision support using machine learning for early detection of late-onset neonatal sepsis. JAMIA. 2014;21(2):326–36. Doi:10.1136/amiajnl-2013-001854
8. Obladen M, Sachsenweger M, Stahnke M. Blood sampling in very low birth weight infants receiving different levels of Intensive Care. Eur J Pediat. 1988;147(4):399–404. doi:10.1007/bf00496419
9. Anand KJS. Clinical importance of pain and stress in preterm neonates. Neonatol. 1997;73(1):1–9. doi:10.1159/000013953
10. Eckstein Grunau R, Oberlander TF, Whitfield MF, Fitzgerald C, Morison SJ, Philip Saul J. Pain reactivity in former extremely low birth weight infants at corrected age 8 months compared with term born controls. Infant Behav Dev 2001 Jan;24(1):41–55.
11. Grunau RE, Holsti L, Peters JWB. Long-term consequences of pain in human neonates. Semin Fetal Neonatal Med. 2006;11(4):268–75.
12. Mani S, Ozdas A, Aliferis C, et al. Medical decision support using machine learning for early detection of late-onset neonatal sepsis. J Am Med Inform Assoc. 2014;21(2):326–336. doi:10.1136/amiajnl-2013-001854
13. Anand KJS, Scalzo FM. Can Adverse Neonatal Experiences Alter Brain Development and Subsequent Behavior? Neonatol. 2000;77(2):69–82.
14. Masino AJ, Harris MC, Forsyth D, et al. Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data. PLoS One. 2019;14(2):e0212665. doi:10.1371/journal.pone.0212665
15. Smith SJ, Farra S, Ulrich DL, Hodgson E, Nicely S, Matcham W. Learning and retention using virtual reality in a decontamination simulation. Nurs Educ Perspect. 2016;37(4):210–4. Doi:10.1097/01.nep.0000000000000035
16. Chang C-Y, Sung H-Y, Guo J-L, Chang B-Y, Kuo F-R. Effects of spherical video-based Virtual Reality on nursing students’ learning performance in childbirth education training. Interact Learn Environ. 2019;30(3):400–16. Doi:10.1080/10494820.2019.1661854
17. Adhikari R, Kydonaki C, Lawrie J, et al. A mixed-methods feasibility study to assess the acceptability and applicability of immersive virtual reality sepsis game as an adjunct to nursing education. Nurse Educ Today. 2021;103:104944. doi:10.1016/j.nedt.2021.104944
18. Meara JG, Leather AJM, Hagander L, Alkire BC, Alonso N, Ameh EA, et al. Global Surgery 2030: evidence and solutions for achieving health, welfare, and economic development. Int J Obstet Anesth. 2016 Feb;25:75–8.
19. Pears M, Konstantinidis S. The Future of Immersive Technology in Global Surgery Education. Indian J Surg. 2021 Jul 1.
20. Adhikari R, Kydonaki C, Lawrie J, O’Reilly M, Ballantyne B, Whitehorn J, et al. A mixed-methods feasibility study to assess the acceptability and applicability of immersive virtual reality sepsis game as an adjunct to nursing education. Nurse Educ Today. 2021;103:104944. Doi:10.1016/j.nedt.2021.104944
21. Pears, M. and Konstantinidis, S. (2021). The Future of Immersive Technology in Global Surgery Education. Indian J Surg. doi:https://doi.org/10.1007/s12262-021-02998-6
22. Mwita S, Jande M, Katabalo D, Kamala B, Dewey D. Reducing neonatal mortality and respiratory distress syndrome associated with preterm birth: a scoping review on the impact of antenatal corticosteroids in low- and middle-income countries. World J Pediatr. 2021;17(2):131–140. doi:10.1007/s12519-020-00398-6
23. CDC (2019). Infant mortality. CDC. [online] Available at: https://www.cdc.gov/maternal-infant-health/infant-mortality/index.html
24. Roro EM, Tumtu MI, Gebre DS. Predictors, causes, and trends of neonatal mortality at Nekemte Referral Hospital, east Wollega Zone, western Ethiopia (2010-2014). PLoS One. 2019;14(10):e0221513. doi:10.1371/journal.pone.0221513
25. Verma J, Anand S, Kapoor N, Gedam S, Patel U. Neonatal outcome in new-borns admitted in Nicu of Tertiary Care Hospital in Central India: A 5-year study. Int J Contemp Pediatrics. 2018;5(4):1364. doi:10.18203/2349-3291.ijcp20182512
26. Ezenwa BN, Umoren R, Fajolu IB, Hippe DS, Bucher S, Purkayastha S, et al. Using Mobile Virtual Reality Simulation to Prepare for In-Person Helping Babies Breathe Training. JMIR. 2022;8(3):e37297. https://pubmed.ncbi.nlm.nih.gov/36094807/
27. Adhikari R, Kydonaki C, Lawrie J, et al. A mixed-methods feasibility study to assess the acceptability and applicability of immersive virtual reality sepsis game as an adjunct to nursing education. Nurse Educ Today. 2021;103:104944. doi:10.1016/j.nedt.2021.104944
28. Sawyer T, Umoren R, Gray MM. Neonatal resuscitation: Advances in training and Practice. Adv Med Educ Pract. 2016;Volume 8:11–9. doi:10.2147/amep.s109099
29. Umoren R, Bucher S, Hippe DS, Ezenwa BN, Fajolu IB, Okwako FM, et al. eHBB: a randomised controlled trial of virtual reality or video for neonatal resuscitation refresher training. BMJ Open. 2021;11(8):e048506. https://bmjopen.bmj.com/content/11/8/e048506
30. Sawyer T, Strandjord TP, Johnson K, Low D. Neonatal airway simulators, how good are they? A comparative study. J Pediatr Perinatol Child Health. 2015;36(2):151–6. doi:10.1038/jp.2015.161
31. Pathirana J, Muñoz FM, Abbing-Karahagopian V, et al. Neonatal death: Case definition & guidelines for immunization safety data. Vaccine. 2016;34(49):6027–37. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5139812
32. Onu CC, Udeogu I, Ndiomu E, Kengni U, Precup D, Sant’anna GM, et al. Ubenwa: Cry-based Diagnosis of Birth Asphyxia. arXiv. 2017. Doi:10.48550/arXiv.171106045
33. Lahmiri S, Tadj C, Gargour C, Bekiros S. Deep learning systems for automatic diagnosis of infant cry signals. Chaos Soliton Fract. 2022;154.
34. Cortes C, Vapnik V. Support-Vector Networks. Machine Learning. 1995;20(3):273–97. doi:10.1007/bf00994018
35. Kiberu VM, Mars M, Scott RE. Barriers and opportunities to implementation of sustainable e-Health programmes in Uganda: A literature review. Afr J Prim Health Care Fam Med. 2017;9(1):10. https://phcfm.org/index.php/phcfm/article/view/1277/2058
36. Harding K, Biks G, Adefris M, Loehr J, Gashaye K, Tilahun B, et al. A mobile health model supporting Ethiopia’s eHealth strategy. Digit Med. 2018;4(2):54.
37. Onu CC. Harnessing infant cry for swift, cost-effective diagnosis of perinatal asphyxia in low-resource settings. IHTC. 2014. doi:10.1109/ihtc.2014.7147559
38. Alghoul A, Al Ajrami S, Al Jarousha G, et al. Email Classification Using Artificial Neural Network. IJAER. 2018;2(11):8–14.
39. Lee K-S, Ahn KH. Artificial neural network analysis of spontaneous preterm labor and birth and its major determinants. JKMS. 2019;34(16). doi:10.3346/jkms.2019.34.e128
40. Al-Shawwa M, Abu-Naser SS. Predicting Birth Weight Using Artificial Neural Network. IJAHMR. 2019;3(1):9–14.
41. Larrazabal AJ, Nieto N, Peterson V, Milone DH, Ferrante E. Gender imbalance in medical imaging datasets produces biased classifiers. Proc Natl Acad Sci USA. 2020;117(23):12592–12594. doi:10.1073/pnas.1919012117
42. Chioma R, Sbordone A, Patti ML, Perri A, Vento G, Nobile S. Applications of artificial intelligence in Neonatology. Apl Sci. 2023;13(5):3211. doi:10.3390/app13053211
43. Kwok TC, Henry C, Saffaran S, Meeus M, Bates D, Van Laere D, et al. Application and potential of artificial intelligence in neonatal medicine. Semin Fetal Neonatal Med. 2022;27(5):101346. doi:10.1016/j.siny.2022.101346
44. McCradden MD, Stephenson EA, Anderson JA. Clinical research underlies ethical integration of healthcare artificial intelligence. Nat Med. 2020;26(9):1325–1326. doi:10.1038/s41591-020-1035-9
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2025 Deborah Yong Yujie

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
Provided they are the owners of the copyright to their work, authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository, in a journal or publish it in a book), with an acknowledgement of its initial publication in this journal.