Navigating the Frontier: AI-Powered Advances in Neonatal Medicine
Authors/Creators
- 1. International Journal of Medical Science and Innovative Research (IJMSIR)
Description
Abstract
With its innovative methods for diagnosis, clinical judgment, and ongoing monitoring, artificial intelligence (AI) is becoming a game-changer in neonatal care. With an emphasis on its potential to improve early disease detection, diagnostic accuracy, and individualized treatment plans, this narrative review examines the developing use of AI in neonatal care. Predictive modeling, neurological evaluation, imaging analysis, and therapy recommendations are some of the primary areas where AI is having a big influence. Neonatal outcomes are improved by these technologies, which let clinicians provide more prompt and focused therapies. Notwithstanding its potential, there are a number of obstacles to integrating AI into neonatal treatment. Algorithmic bias, restricted availability to high-quality datasets, and concerns about patient data confidentiality and informed permission are some of the main obstacles. Furthermore, concerns about ethical issues and regulatory supervision are still present. However, to improve and test these tools, physicians and AI developers must continue their interdisciplinary partnership. Future initiatives should focus on strengthening governance structures, increasing algorithm openness, and boosting data quality. AI has the potential to be a useful addition to newborn care with careful application, enhancing clinical judgment, optimizing processes, and eventually improving infant survival and long-term developmental outcomes.
Files
NEHA.pdf
Files
(986.4 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:e6a93f579fcb3b2ff49c526422aa6828
|
986.4 kB | Preview Download |
Additional details
Software
References
- 1. Mangold C, Zoretic S, Thallapureddy K, Moreira A, Chorath K. Machine learning models for predicting neonatal mortality: a systematic review. Neonatology 2021; 118:394–405 2. GreenburySF,OughamK,WuJ,BattersbyC,GaleC,ModiN,etal.Identificationof variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning. Sci Rep 2021;11:7178. 3. Temple, M.W., Lehmann, C.U. & Fabbri, D. Predicting Discharge Dates From the NICU Using Progress Note Data. Pediatrics 136, e395-405 (2015). 4. Kumar N, Akangire G, Sullivan B, Fairchild K, Sampath V. Continuous vital sign analysis for predicting and preventing neonatal diseases in the twenty-first century: big data to the forefront. Pediatr Res 2020;87:210–20. 5. Reed, N.E., Gini, M., Johnson, P.E. & Moller, J.H. Diagnosing congenital heart defects using the Fallot computational model. Artificial Intelligence in Medicine 10, 25-40 (1997). 6. Li, L., et al. The use of fuzzy backpropagation neural networks for the early diagnosis of hypoxic ischemic encephalopathy in newborns. J Biomed Biotechnol 2011, 349490 (2011). 7. Zernikow, B., et al. Artificial neural network for predicting intracranial haemorrhage in preterm neonates. Acta Paediatrica 87, 969-975 (1998). 8. Ferreira, D., Oliveira, A. & Freitas, A. Applying data mining techniques to improve diagnosis in neonatal jaundice. BMC medical informatics and decision making 12, 1-6 (2012). 9. Ji, J., et al. A data-driven algorithm integrating clinical and laboratory features for the diagnosis and prognosis of necrotizing enterocolitis. PLoS One 9, e89860 (2014). 10. Ambalavanan, N., et al. Prediction of neurologic morbidity in extremely low birth weight infants. Journal of Perinatology 20, 496-503 (2000). 11. Soleimani, F., Teymouri, R. & Biglarian, A. Predicting developmental disorder in infants using an artificial neural network. Acta Medica Iranica, 347-352 (2013). 12. Bartz-Kurycki, M.A., et al. Enhanced neonatal surgical site infection prediction model utilizing statistically and clinically significant variables in combination with a machine learning algorithm. Am J Surg 216, 764-777 (2018). 13. Hsu, K.P., et al. A newborn screening system based on service-oriented architecture embedded support vector machine. J Med Syst 34, 899-907 (2010). 14. Baumgartner, C., et al. Supervised machine learning techniques for the classification of metabolic disorders in newborns. Bioinformatics 20, 2985-2996 (2004). 15. Chen, W.H., et al. Web-based newborn screening system for metabolic diseases: machine learning versus clinicians. J Med Internet Res 15, e98 (2013). 16. Hauptmann, A., Arridge, S., Lucka, F., Muthurangu, V. & Steeden, J.A. Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease. Magn Reson Med 81, 1143-1156 (2019). 17. Dimitriou G, Fouzas S, Vervenioti A, Tzifas S, Mantagos S. Prediction of extubation outcome in preterm infants by composite extubation indices. Pediatr Crit Care Med 2011;12:e242–9. 18. Precup D, Robles-Rubio CA, Brown KA, Kanbar L, Kaczmarek J, Chawla S, et al. Prediction of extubation readiness in extreme preterm infants based on measures of cardiorespiratory variability. Annu Int Conf IEEE Eng Med Biol Soc 2012;2012: 5630–3. 19. Mueller M, Almeida JS, Stanislaus R, Wagner CL. Can machine learning methods predict extubation outcome in premature infants as well as clinicians? J Neonatal Biol 2013;2. 20. Raimondi, F.; Migliaro, F.; Verdoliva, L.; Gragnaniello, D.; Poggi, G.; Kosova, R.; Sansone, C.; Vallone, G.; Capasso, L. Visual assessment versus computer-assisted gray scale analysis in the ultrasound evaluation of neonatal respiratory status. PLoS ONE 2018, 13, e0202397. [Google Scholar] [CrossRef] [PubMed] 21. Varisco, G.; Peng, Z.; Kommers, D.; Zhan, Z.; Cottaar, W.; Andriessen, P.; Long, X.; van Pul, C. Central apnea detection in premature infants using machine learning. Comput. Methods Programs Biomed. 2022, 226, 107155. [Google Scholar] [CrossRef] [PubMed] 22. Son, J.; Kim, D.; Na, J.Y.; Jung, D.; Ahn, J.-H.; Kim, T.H.; Park, H.-K. Development of artificial neural networks for early prediction of intestinal perforation in preterm infants. Sci. Rep. 2022, 12, 12112. [Google Scholar] [CrossRef] 23. Han, J.H.; Yoon, S.J.; Lee, H.S.; Park, G.; Lim, J.; Shin, J.E.; Eun, H.S.; Park, M.S.; Lee, S.M. Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants. Yonsei Med. J. 2022, 63, 640–647. [Google Scholar] [CrossRef] 24. Guedalia, J.; Farkash, R.; Wasserteil, N.; Kasirer, Y.; Rottenstreich, M.; Unger, R.; Grisaru Granovsky, S. Primary risk stratification for neonatal jaundice among term neonates using machine learning algorithm. Early Hum. Dev. 2022, 165, 105538. [Google Scholar] [CrossRef] 25. Shellhaas, R.A.; Chang, T.; Tsuchida, T.; Scher, M.S.; Riviello, J.J.; Abend, N.S.; Nguyen, S.; Wusthoff, C.J.; Clancy, R.R. The American Clinical Neurophysiology Society's Guideline on Continuous Electroencephalography Monitoring in Neonates. J. Clin. Neurophysiol. 2011, 28, 611–617. [Google Scholar] [CrossRef] [Green Version] 26. Tagin, M.A.; Woolcott, C.G.; Vincer, M.J.; Whyte, R.K.; Stinson, D.A. Hypothermia for neonatal hypoxic ischemic encephalopathy: An updated systematic review and meta-analysis. Arch. Pediatr. Adolesc. Med. 2012, 166, 558–566. [Google Scholar] [CrossRef] [Green Version] 27. De Vries, L.S.; Groenendaal, F.; van Haastert, I.C.; Eken, P.; Rademaker, K.J.; Meiners, L.C. Asymmetrical myelination of the posterior limb of the internal capsule in infants with periventricular haemorrhagic infarction: An early predictor of hemiplegia. Neuropediatrics 1999, 30, 314–319. [Google Scholar] [CrossRef] 28. Valavani, E.; Blesa, M.; Galdi, P.; Sullivan, G.; Dean, B.; Cruickshank, H.; Sitko-Rudnicka, M.; Bastin, M.E.; Chin, R.F.M.; MacIntyre, D.J.; et al. Language function following preterm birth: Prediction using machine learning. Pediatr. Res.2022, 92, 480–489. [Google Scholar] [CrossRef] 29. Balta, D.; Kuo, H.; Wang, J.; Porco, I.G.; Morozova, O.; Schladen, M.M.; Cereatti, A.; Lum, P.S.; Della Croce, U. Characterization of Infants' General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study. Sensors 2022, 22, 7426. [Google Scholar] 30. Lynch CD, Zhang J. The research implications of the selection of a gestational age estimation method. Paediatr Perinat Epidemiol 2007;21(Suppl 2):86–96. 31. Porras AR, Rosenbaum K, Tor-Diez C, Summar M, Linguraru MG. 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; 3:e635–43. 32. Nobile, S.; Gnocchini, F.; Pantanetti, M.; Battistini, P.; Carnielli, V.P. The importance of oxygen control reaffirmed: Experience of ROP reduction at a single tertiary care center. J. Pediatr. Ophthalmol. Strabismus. 2014, 51, 112–115. [Google Scholar] [CrossRef] [PubMed] 33. Wu, Q.; Hu, Y.; Mo, Z.; Wu, R.; Zhang, X.; Yang, Y.; Liu, B.; Xiao, Y.; Zeng, X.; Lin, Z.; et al. Development and Validation of a Deep Learning Model to Predict the Occurrence and Severity of Retinopathy of Prematurity. JAMA Netw. Open2022, 5, e2217447. [Google Scholar] [CrossRef] [PubMed] 34. Barrero-Castillero, A., Corwin, B.K., VanderVeen, D.K. & Wang, J.C. Workforce Shortage for Retinopathy of Prematurity Care and Emerging Role of Telehealth and Artificial Intelligence. Pediatr Clin North Am 67, 725-733 (2020). 35. Alvarez-Fuente, M.; Arruza, L.; Muro, M.; Zozaya, C.; Avila, A.; López-Ortego, P.; González-Armengod, G.; Torrent, A.; Luis Gavilán, J.; del Cerro, M.J. The economic impact of prematurity and bronchopulmonary dysplasia. Eur. J. Pediatr. 2017, 176, 1587–1593. [Google Scholar] [CrossRef] [PubMed] 36. Pinto, F.; Fernandes, E.; Virella, D.; Abrantes, A.; Neto, M.T. Born Preterm: A Public Health Issue. Port. J. Public Health 2019, 37, 38–49. [Google Scholar] [CrossRef] 37. Zejin Ou, Z.; Yu, D.; Liang, Y.; He, H.; He, W.; Li, Y.; Zhang, M.; Gao, Y.; Wu, F.; Chen, Q. Yongzhi LiGlobal trends in incidence and death of neonatal disorders and its specific causes in 204 countries/territories during 1990–2019. BMC Public Health 2022, 22, 360. [Google Scholar]