Sazawal S, Ryckman KK, Das S, Khanam R, Nisar I, Jasper E, Dutta A, Rahman S, Mehmood U, Bedell B, Deb S, Chowdhury NH, Barkat A, Mittal H, Ahmed S, Khalid F, Raqib R, Manu A, Yoshida S, Ilyas M, Nizar A, Ali SM, Baqui AH, Jehan F, Dhingra U, Bahl R. Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in South Asia and sub-Saharan Africa. BMC pregnancy and childbirth. 2021 Sep 7;21(21). 609 p. PMID: 34493237 [PubMed] PMCID: PMC8424940
Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, advocacy, resources allocation and program evaluation and at an individual level for targeted care. Early prenatal ultrasound examination is not available in these settings, gestational age (GA) is estimated using new-born assessment, last menstrual period (LMP) recalls and birth weight, which are unreliable. Algorithms in developed settings, using metabolic screen data, provided GA estimates within 1-2 weeks of ultrasonography-based GA. We sought to leverage machine learning algorithms to improve accuracy and applicability of this approach to LMICs settings.