1. Tunçalp, Ӧ. et al. WHO recommendations on antenatal care for a positive pregnancy experience—going beyond survival. BJOG Int J Obstet Gynaecol 2017. 124, 860–862.
2. Lawn, J. E. et al. Every Newborn: progress, priorities, and potential beyond survival. Lancet Lond Engl 2014. 384, 189–205.
3. Walani, S. R. Global burden of preterm birth. Int J Gynecol Obstet 2020. 150, 31–33.
4. Oza, S., Cousens, S. N. & Lawn, J. E. Estimation of daily risk of neonatal death, including the day of birth, in 186 countries in 2013: a vital-registration and modelling-based study. Lancet Glob Health 2014. 2, e635-644.
5. Kim, E. T., Singh, K., Moran, A., Armbruster, D. & Kozuki, N. Obstetric ultrasound use in low and middle-income countries: a narrative review. Reprod Health 2018. 15, 129.
6. ACOG. Committee Opinion No 700: Methods for Estimating the Due Date. Obstet Gynecol 2017. 129, e150–e154.
7. Miller, L. et al. Working with what you have: How the East Africa Preterm Birth Initiative used gestational age data from facility maternity registers. PloS One 2020. 15, e0237656.
8. Karl, S. et al. Preterm or not–an evaluation of estimates of gestational age in a cohort of women from rural Papua New Guinea. PloS One 2015. 10, e0124286.
9. Kullinger, M., Granfors, M., Kieler, H. & Skalkidou, A. Discrepancy between pregnancy dating methods affects obstetric and neonatal outcomes: a population-based register cohort study. Sci Rep 2018. 8, 6936.
10. WHO. Global strategy on digital health 2020-2025. Geneva: World Health Organization 2021. https://apps.who.int/iris/handle/10665/344249, (accessed March 25, 2022).
11. Nelson, G. A. & Holschuh, C. Evaluation of Telehealth Use in Prenatal Care for Patient and Provider Satisfaction: A Step Toward Reducing Barriers to Care. J Nurse Pract 2021. 17, 481–484.
12. Meskó, B., Drobni, Z., Bényei, É., Gergely, B. & Győrffy, Z. Digital health is a cultural transformation of traditional healthcare. mHealth 2017. 3, 38.
13. Miller, D. D. & Brown, E. W. Artificial Intelligence in Medical Practice: The Question to the Answer? Am J Med 2018. 131, 129–133.
14. Reis, Z. S. N., Vitral, G. L. N., de Souza, I. M. F., Rego, M. A. S. & Guimaraes, R. N. Newborn skin reflection: Proof of concept for a new approach for predicting gestational age at birth. A cross-sectional study. PloS One 2017. 12, e0184734.
15. Collins, G. S., Reitsma, J. B., Altman, D. G. & Moons, K. G. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med 2015. 13, 1.
16. Venema, E. et al. Large-scale validation of the prediction model risk of bias assessment Tool (PROBAST) using a short form: high risk of bias models show poorer discrimination. J Clin Epidemiol 2021. 138, 32–39.
17. Reis, Z. S. N. et al. Prematurity detection evaluating interaction between the skin of the newborn and light: protocol for the preemie-test multicentre clinical trial in Brazilian hospitals to validate a new medical device. BMJ Open 2019. 9, e027442.
18. Silva, P. C., Guimarães, R. N., Souza, R. G. & Reis, Z. S. N. A quantitative cross-sectional analysis of the melanin index in the skin of preterm newborns and its association with gestational age at birth. Skin Res Technol Off J Int Soc Bioeng Skin ISBS Int Soc Digit Imaging Skin ISDIS Int Soc Skin Imaging ISSI 2020. 26, 356–36.
19. Wyckoff, M. H. et al. Neonatal life support: 2020 international consensus on cardiopulmonary resuscitation and emergency cardiovascular care science with treatment recommendations. Circulation 2020. 142, n. 16_suppl_1, p. S185-S221.
20. Scott, K. et al. “I can guess the month … but beyond that, I can’t tell” an exploratory qualitative study of health care provider perspectives on gestational age estimation in Rajasthan, India. BMC Pregnancy Childbirth 2020. 20, 529.
21. Weiss, S. M. & Indurkhya, N. Rule-based machine learning methods for functional prediction. J Artif Intell Res 1995. 3, 383–403.
22. Reis, Z. et al. Premature or Small for Gestational Age Discrimination: International Multicenter Trial Protocol for Classification of the Low-Birth-Weight Newborn Through the Optical Properties of the Skin. JMIR Res Protoc 2020. 9, e16477.
23. Ananth, C. V. & Brandt, J. S. Fetal growth and gestational age prediction by machine learning. Lancet Digit Health 2020. 2, e336–e337.
24. Fung, R. et al. Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study. Lancet Digit Health 2020. 2, e368–e375.
25. Torres, M. T., Valstar, M. F., Henry, C., Ward, C. & Sharkey, D. Small sample deep learning for newborn gestational age estimation. 12th IEEE International Conference on Automatic Face & Gesture Recognition 2017. pp. 79–86. doi:10.1109/FG.2017.19.
26. Rittenhouse, K. J. et al. Improving preterm newborn identification in low-resource settings with machine learning. PloS One 2019. 14, e0198919.
27. Soubeiga, D., Gauvin, L., Hatem, M. A. & Johri, M. Birth Preparedness and Complication Readiness (BPCR) interventions to reduce maternal and neonatal mortality in developing countries: systematic review and meta-analysis. BMC Pregnancy Childbirth 2014. 14, 129.
28. Emeruwa, U. N., Krenitsky, N. M., & Sheen, J. J. Advances in Management for Preterm Fetuses at Risk of Delivery. Clinics in Perinatology 2020. 47, 685-703.
29. Condon, J. et al. Expression of type 2 11β-hydroxysteroid dehydrogenase and corticosteroid hormone receptors in early human fetal life. J Clin Endocrinol Metab 1998. 83, 4490–4497.
30. August, D. & Kandasamy, Y. The effects of antenatal glucocorticoid exposure on fetal and neonatal skin maturation. J Perinat Med 2017. 45.
31. Lee, A. C. et al. Diagnostic Accuracy of Neonatal Assessment for Gestational Age Determination: A Systematic Review. Pediatrics 2017. 140, e20171423.
32. Leal, M. do C. et al. Saúde reprodutiva, materna, neonatal e infantil nos 30 anos do Sistema Único de Saúde (SUS). Ciênc Saúde Coletiva 2018. 23, 1915–1928.
33. Ezbakhe, F., Pérez-Foguet, A. Child mortality levels and trends. Demographic Research 2020. 43, p. 1263–1296.