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Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence
Lees, C. C. et al. ISUOG Practice Guidelines: diagnosis and management of small-for-gestational-age fetus and fetal growth restriction. Ultrasound Obstet. Gynecol. 56, 298–312 (2020).
Martin. Health—United Nations Sustainable Development. United Nations Sustainable Development https://www.un.org/sustainabledevelopment/health/#tab-3f22056b0e91266e8b2 (2023).
Krasevec, J. et al. Study protocol for UNICEF and WHO estimates of global, regional, and national low birthweight prevalence for 2000 to 2020. Gates Open Res. 6, 80 (2022).
Blencowe, H. et al. National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: a systematic analysis. Lancet Glob. Health 7, e849–e860 (2019).
Jia, C. H. et al. Short term outcomes of extremely low birth weight infants from a multicenter cohort study in Guangdong of China. Sci. Rep. 12, 11119 (2022).
Low birth weight. https://www.who.int/data/nutrition/nlis/info/low-birth-weight.
Okwaraji, Y. B. et al. National, regional, and global estimates of low birthweight in 2020, with trends from 2000: a systematic analysis. Lancet 403, 1071–1080 (2024).
International Institute for Population Sciences (IIPS) and Macro International. 2007. National Family Health Survey (NFHS-3), 2005–06: India: Volume I. Mumbai: IIPS.
Singh, D. et al. Prevalence and correlates of low birth weight in India: findings from National Family Health Survey 5. BMC Pregnancy Childbirth 23, 1–10 (2023).
Sabbaghchi, M., Jalali, R. & Mohammadi, M. A systematic review and meta-analysis on the prevalence of low birth weight infants in Iran. J. Pregnancy 2020, 1–7 (2020).
Institute of Medicine (US) Committee on Improving Birth Outcomes. In Improving Birth Outcomes: Meeting the Challenge in the Developing World (eds. Bale, J. R., Stoll, B. J., Lucas, A. O.) (National Academies Press, 2003). https://www.ncbi.nlm.nih.gov/books/NBK222095/.
Derakhshi, B., Esmailnasab, N., Ghaderi, E. & Hemmatpour, S. Risk factor of preterm labor in the west of Iran: a case-control study. DOAJ 43, 499–506 (2014).
Moradi, G., Zokaeii, M., Goodarzi, E. & Khazaei, Z. Maternal risk factors for low birth weight infants: a nested case-control study of rural areas in Kurdistan (western of Iran). J. Prev. Med. Hyg. (2021).
Katz, J. et al. Mortality risk in preterm and small-for-gestational-age infants in low-income and middle-income countries: a pooled country analysis. Lancet 382, 417–425 (2013).
Knop, M. R. et al. Birth weight and risk of Type 2 diabetes mellitus, cardiovascular disease, and hypertension in adults: a meta-analysis of 7,646,267 participants from 135 studies. J. Am. Heart Assoc. 7, e008870 (2018).
Wu, M. et al. Estimation of fetal weight by ultrasonic examination. Int. J. Clin. Exp. Med. 8, 540–545 (2015).
Dwivedi, Y. K. et al. Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manag. 57, 101994 (2021).
Alanazi, A. Using machine learning for healthcare challenges and opportunities. Inform. Med. Unlocked 30, 100924 (2022).
Reddy, S. Explainability and artificial intelligence in medicine. Lancet Digit. Health 4, e214–e215 (2022).
Reza, T. B. & Salma, N. Prediction and feature selection of low birth weight using machine learning algorithms. J. Health Popul. Nutr. 43, 1–10 (2024).
Ranjbar, A. et al. Machine learning-based approach for predicting low birth weight. BMC Pregnancy Childbirth 23, 1–9 (2023).
de Morais, F. L. et al. Utilization of tree-based machine learning models for predicting low birth weight cases. BMC Pregnancy Childbirth 25, 207 (2025).
Naimi, A. I., Platt, R. W. & Larkin, J. C. Machine learning for fetal growth prediction. Epidemiology 29, 290 (2018).
Tao, J., Yuan, Z., Sun, L., Yu, K. & Zhang, Z. Fetal birthweight prediction with measured data by a temporal machine learning method. BMC Med. Inform. Decis. Mak. 21, 1–10 (2021).
Rubaiya, N., Mansur, M., Alam, M. M. & Rayhan, M. I. Unraveling birth weight determinants: integrating machine learning, spatial analysis, and district-level mapping. Heliyon 10, e27341 (2024).
Sadeghi, Z. et al. A review of explainable artificial intelligence in healthcare. Comput. Electr. Eng. 118, 109370 (2024).
Gill, S. V., May-Benson, T. A., Teasdale, A. & Munsell, E. G. Birth and developmental correlates of birth weight in a sample of children with potential sensory processing disorder. BMC Pediatr. 13, 1–9 (2013).
Tarín, J. J., García-Pérez, M. A. & Cano, A. Potential risks to offspring of intrauterine exposure to maternal age-related obstetric complications. Reprod. Fertil. Dev. 29, 1468–1475 (2016).
Wang, S. et al. Changing trends of birth weight with maternal age: a cross-sectional study in Xi’an city of Northwestern China. BMC Pregnancy Childbirth 20, 1–7 (2020).
Softa, S. M. et al. The association of maternal height with mode of delivery and fetal birth weight at King Abdulaziz University Hosc, Jeddah,. Saudi Arabia. Cureus 14, e27493 (2022).
Rochow, N. et al. Maternal body height is a stronger predictor of birth weight than ethnicity: analysis of birth weight percentile charts. J. Perinat. Med. 47, 22–29 (2018).
Spada, E., Chiossi, G., Coscia, A., Monari, F. & Facchinetti, F. Effect of maternal age, height, BMI and ethnicity on birth weight: an Italian multicenter study. J. Perinat. Med. 46, 1016–1021 (2017).
Ludwig, D. S. & Currie, J. The association between pregnancy weight gain and birthweight: a within-family comparison. Lancet 376, 984–990 (2010).
Hussey, M. R. et al. Associations of placental lncRNA expression with maternal pre-pregnancy BMI and infant birthweight in two birth cohorts. J. Dev. Orig. Health Dis. 16, 1–10 (2025).
Garces, A. et al. Association of parity with birthweight and neonatal death in five sites: The Global Network’s Maternal Newborn Health Registry study. Reprod. Health 17, 1–8 (2020).
Shah, P. S. Parity and low birth weight and preterm birth: a systematic review and meta-analyses. Acta Obstet. Gynecol. Scand. 89, 862–875 (2010).
Wang, Y. A. et al. Preterm birth and low birth weight after assisted reproductive technology-related pregnancy in Australia between 1996 and 2000. Fertil. Steril. 83, 1650–1658 (2005).
Zhong, X. et al. Effect of parental physiological conditions and assisted reproductive technologies on the pregnancy and birth outcomes in infertile patients. Oncotarget 8, 18409–18416 (2016).
Rahmati, S., Azami, M., Badfar, G., Parizad, N. & Sayehmiri, K. The relationship between maternal anemia during pregnancy with preterm birth: a systematic review and meta-analysis. J. Matern. Fetal Neonatal Med. 33, 2679–2689 (2018).
Sherwani, S. I., Khan, H. A., Ekhzaimy, A., Masood, A. & Sakharkar, M. K. Significance of HbA1C test in diagnosis and prognosis of diabetic patients. Biomark. Insights 11, BMI.S38440 (2016).
Gabbe, S. Management of diabetes mellitus complicating pregnancy. Obstet. Gynecol. 102, 857–868 (2003).
Li, J. et al. Maternal TSH levels at first trimester and subsequent spontaneous miscarriage: a nested case–control study. Endocr. Connect. 8, 1288–1293 (2019).
Nishioka, E. et al. Relationship between maternal thyroid-stimulating hormone (TSH) elevation during pregnancy and low birth weight: A longitudinal study of apparently healthy urban Japanese women at very low risk. Early Hum. Dev. 91, 181–185 (2015).
Bilardo, C. M. et al. ISUOG Practice Guidelines (updated): performance of 11–14-week ultrasound scan. Ultrasound Obstet. Gynecol. 61, 127–143 (2023).
Kalousová, M., Muravská, A. & Zima, T. Pregnancy-associated plasma protein A (PAPP-A) and preeclampsia. Adv. Clin. Chem. 63, 169–209 (2014).
Shiefa, S., Amargandhi, M., Bhupendra, J., Moulali, S. & Kristine, T. First trimester maternal serum screening using biochemical markers PAPP-A and free Β-HCG for Down syndrome, Patau syndrome and Edward syndrome. Indian J. Clin. Biochem. 28, 3–12 (2012).
Salomon, L. J. et al. ISUOG Practice Guidelines (updated): performance of the routine mid-trimester fetal ultrasound scan. Ultrasound Obstet. Gynecol. 59, 840–856 (2022).
Ogonowski, J. & Miazgowski, T. Intergenerational transmission of macrosomia in women with gestational diabetes and normal glucose tolerance. Eur. J. Obstet. Gynecol. Reprod. Biol. 195, 113–116 (2015).
Yang, Y. et al. The association of gestational diabetes mellitus with fetal birth weight. J. Diabetes Complications 32, 635–642 (2018).
Ardissino, M. et al. Maternal hypertension increases risk of preeclampsia and low fetal birthweight: Genetic evidence from a Mendelian randomization study. Hypertension 79, 588–598 (2022).
Cutland, C. L. et al. Low birth weight: case definition & guidelines for data collection, analysis, and presentation of maternal immunization safety data. Vaccine 35, 6492–6500 (2017).
Aziz, F., Khan, M. F. & Moiz, A. Gestational diabetes mellitus, hypertension, and dyslipidemia as the risk factors of preeclampsia. Sci. Rep. 14, 1–11 (2024).
Celik, N. Welch’s ANOVA: Heteroskedastic skew-t error terms. Commun. Stat. Theory Methods 51, 3065–3076 (2022).
Mujahid, M. et al. Data oversampling and imbalanced datasets: an investigation of performance for machine learning and feature engineering. J Big Data 11, 87 (2024).
Cerda, P., Varoquaux, G. & Kégl, B. Similarity encoding for learning with dirty categorical variables. Mach. Learn. 107, 1477–1494 (2018).
Alkhawaldeh, I. M., Albalkhi, I. & Naswhan, A. J. Challenges and limitations of synthetic minority oversampling techniques in machine learning. World J. Methodol. 13, 373–378 (2023).
Abedin, M. Z., Guotai, C., Hajek, P. & Zhang, T. Combining weighted SMOTE with ensemble learning for the class-imbalanced prediction of small business credit risk. Complex Intell. Syst. 9, 3559–3579 (2022).
Kraskov, A., Stögbauer, H. & Grassberger, P. Estimating mutual information. Phys. Rev. E 69, 066138 (2004).
Shipe, M. E., Deppen, S. A., Farjah, F. & Grogan, E. L. Developing prediction models for clinical use using logistic regression: an overview. J. Thorac. Dis. 11, S574–S584 (2019).
Montomoli, J. et al. Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients. J. Intensive Med. 1, 110–116 (2021).
Jabbar, M. A., Deekshatulu, B. L. & Chandra, P. Classification of heart disease using K-nearest neighbor and genetic algorithm. arXiv preprint arXiv:1508.02061 (2015).
Zheng, J. et al. Clinical data based XGBoost algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012–2021) multicenter retrospective case-control study. BMC Gastroenterol. 23, 1–13 (2023).
Loef, B. et al. Using random forest to identify longitudinal predictors of health in a 30-year cohort study. Sci. Rep. 12, 1–12 (2022).
Wallace, M. L. et al. Use and misuse of random forest variable importance metrics in medicine: demonstrations through incident stroke prediction. BMC Med. Res. Methodol. 23, 1–12 (2023).
Singh, D. & Singh, B. Investigating the impact of data normalization on classification performance. Appl. Soft Comput. 97, 105524 (2019).
Kaliappan, J. et al. Impact of cross-validation on machine learning models for early detection of intrauterine fetal demise. Diagnostics (Basel) 13, 1692 (2023).
Elgeldawi, E., Sayed, A., Galal, A. R. & Zaki, A. M. Hyperparameter tuning for machine learning algorithms used for Arabic sentiment analysis. Informatics 8, 79 (2021).
Shekar, B. H. & Dagnew, G. Grid search-based hyperparameter tuning and classification of microarray cancer data. In Proc. Int. Conf. Adv. Comput. Commun. Paradigms (ICACCP) (2019). https://doi.org/10.1109/ICACCP.2019.8882943
Chadaga, K. et al. Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers. Sci. Rep. 14, 1–14 (2024).
Tougui, I., Jilbab, A. & Mhamdi, J. E. Impact of the choice of cross-validation techniques on the results of machine learning-based diagnostic applications. Healthc. Inform. Res. 27, 189–199 (2021).
Rodríguez-Pérez, R. & Bajorath, J. Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. J. Comput. Aided Mol. Des. 34, 1013–1026 (2020).
Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56–67 (2020).
Abdullakutty, F. et al. Histopathology in focus: a review on explainable multi-modal approaches for breast cancer diagnosis. Front. Med. 11, 1450103 (2024).
Javaid, M., Haleem, A., Pratap Singh, R., Suman, R. & Rab, S. Significance of machine learning in healthcare: Features, pillars and applications. Int. J. Intell. Netw. 3, 58–73 (2022).
Arain, Z., Iliodromiti, S., Slabaugh, G., David, A. L. & Chowdhury, T. T. Machine learning and disease prediction in obstetrics. Curr. Res. Physiol. 6, 100099 (2023).
Alzubaidi, M. et al. Ensemble transfer learning for fetal head analysis: From segmentation to gestational age and weight prediction. Diagnostics 12, 2229 (2022).
Hughes, M. M., Black, R. E. & Katz, J. 2500-g low birth weight cutoff: history and implications for future research and policy. Matern. Child Health J. 21, 283 (2016).
Zeegers, B. et al. Birthweight charts customised for maternal height optimises the classification of small and large-for-gestational age newborns. Acta Paediatr. https://doi.org/10.1111/APA.17332 (2024).
Teoh, Z.H., Mariapun, J., Ko, V.S.Y., Dominic, N.A., Jeganathan, R., Karalasingam, S.D. & Thirunavuk Arasoo, V.J. Maternal height, and ethnicity and birth weight: A retrospective cohort study of uncomplicated term vaginal deliveries in Malaysia. Birth. 51, 620–628 (2024).
Sutan, R., Mohtar, M., Mahat, A. N. & Tamil, A. M. Determinant of low birth weight infants: a matched case control study. Open J. Prev. Med. 4, 91–99 (2014).
To, W. W. K., Cheung, W. & Kwok, J. S. Y. Paternal height and weight as determinants of birth weight in a Chinese population. Am. J. Perinatol. 15, 545–548 (1998).
Pölzlberger, E., Hartmann, B., Hafner, E., Stümpflein, I. & Kirchengast, S. Maternal height and pre-pregnancy weight status are associated with fetal growth patterns and newborn size. J. Biosoc. Sci. 49, 392–407 (2017).
Ay, L. et al. Maternal anthropometrics are associated with fetal size in different periods of pregnancy and at birth: the Generation R Study. BJOG 116, 953–963 (2009).
Mathai, M. et al. Ethnicity and fetal growth in Fiji. Aust. N. Z. J. Obstet. Gynaecol. 44, 318–321 (2004).
Figueras, F. et al. Customized birthweight standards for a Spanish population. Eur. J. Obstet. Gynecol. Reprod. Biol. 136, 20–24 (2008).
Britto, R. P. D. A. et al. Influence of maternal height and weight on low birth weight: a cross-sectional study in poor communities of northeastern Brazil. PLoS ONE 8, e000000 (2013).
Mongelli, M., Figueras, F., Francis, A. & Gardosi, J. A customised birthweight centile calculator developed for an Australian population. Aust. N. Z. J. Obstet. Gynaecol. 47, 128–131 (2007).
McCowan, L., Stewart, A. W., Francis, A. & Gardosi, J. A customised birthweight centile calculator developed for a New Zealand population. Aust. N. Z. J. Obstet. Gynaecol. 44, 428–431 (2004).
Zhang, G. et al. Assessing the causal relationship of maternal height on birth size and gestational age at birth: a Mendelian randomization analysis. PLoS Med. 12, e100000 (2015).
Kaur, S. et al. Risk factors for low birth weight among rural and urban Malaysian women. BMC Public Health 19, 539 (2019).
Inoue, S. et al. Association between short maternal height and low birth weight: a hospital-based study in Japan. J. Korean Med. Sci. 31, 353–359 (2016).
Retnakaran, R. et al. Association of timing of weight gain in pregnancy with infant birth weight. JAMA Pediatr. 172, 136–142 (2018).
Mohapatra, I., Harshini, N., Samantaray, S. R. & Naik, G. Association between early pregnancy body mass index and gestational weight gain in relation to neonatal birth weight. Cureus 14, e23000 (2022).
Arora, P. & Tamber Aeri, B. Gestational weight gain among healthy pregnant women from Asia in comparison with Institute of Medicine (IOM) Guidelines-2009: a systematic review. J. Pregnancy 2019, 3849596 (2019).
Hackmon, R. et al. Do early fetal measurements and nuchal translucency correlate with term birth weight?. J. Obstet. Gynaecol. Can. 39, 750–756 (2017).
Jackson, M. & Rose, N. C. Diagnosis and management of fetal nuchal translucency. Semin. Roentgenol. 33, 333–338 (1998).
Kalem, Z., Kaya, A. E., Bakırarar, B. & Kalem, M. N. Fetal nuchal translucency: is there an association with birthweight and neonatal wellbeing?. Turk. J. Obstet. Gynecol. 16, 35–40 (2019).
Napolitano, R. et al. Pregnancy dating by fetal crown–rump length: a systematic review of charts. BJOG 121, 556–565 (2014).
Fries, N. et al. The impact of optimal dating on the assessment of fetal growth. BMC Pregnancy Childbirth 21, 1–8 (2021).
Salomon, L. J. Early fetal growth: concepts and pitfalls. Ultrasound Obstet. Gynecol. 35, 385–389 (2010).
Papageorghiou, A. T. et al. International standards for early fetal size and pregnancy dating based on ultrasound measurement of crown–rump length in the first trimester of pregnancy. Ultrasound Obstet. Gynecol. 44, 641 (2014).
Kozuki, N. et al. The associations of parity and maternal age with small-for-gestational-age, preterm, and neonatal and infant mortality: A meta-analysis. BMC Public Health 13(Suppl 3), S2 (2013).
Hinkle, S. N. et al. The association between parity and birthweight in a longitudinal consecutive pregnancy cohort. Paediatr. Perinat. Epidemiol. 28, 106 (2013).
Krulewitch, C. J., Herman, A. A., Yu, K. F. & Johnson, Y. R. Does changing paternity contribute to the risk of intrauterine growth retardation?. Paediatr. Perinat. Epidemiol. 11, 41–47 (1997).
Luo, Z. C. et al. The effects and mechanisms of primiparity on the risk of pre-eclampsia: a systematic review. Paediatr. Perinat. Epidemiol. 21, 36–45 (2007).
Sørnes, T. & Bakke, T. Uterine size, parity and umbilical cord length. Acta Obstet. Gynecol. Scand. 68, 439–441 (1989).
Clapp, J. F. & Capeless, E. Cardiovascular function before, during, and after the first and subsequent pregnancies. Am. J. Cardiol. 80, 1469–1473 (1997).
Dasa, T. T., Okunlola, M. A. & Dessie, Y. Effect of grand multiparity on the adverse birth outcome: a hospital-based prospective cohort study in Sidama Region, Ethiopia. Int. J. Womens Health 14, 363 (2022).
Lin, L., Lu, C., Chen, W., Li, C. & Guo, V. Y. Parity and the risks of adverse birth outcomes: a retrospective study among Chinese. BMC Pregnancy Childbirth 21, 1–11 (2021).
Baer, R. J., Lyell, D. J., Norton, M. E., Currier, R. J. & Jelliffe-Pawlowski, L. L. First trimester pregnancy-associated plasma protein-A and birth weight. Eur. J. Obstet. Gynecol. Reprod. Biol. 198, 1–6 (2016).
Turrado Sánchez, E. M., De Miguel Sánchez, V. & Macía Cortiñas, M. Correlation between PAPP-A levels determined during the first trimester and birth weight at full-term. Reprod. Sci. 30, 3235 (2023).
Zhao, D. et al. Association between maternal blood glucose levels during pregnancy and birth outcomes: a birth cohort study. Int. J. Environ. Res. Public Health 20, 2102 (2023).
Everett, T. R. & Lees, C. C. Beyond the placental bed: placental and systemic determinants of the uterine artery Doppler waveform. Placenta 33, 893–901 (2012).
Liu, Y. et al. Impact of gestational hypertension and preeclampsia on low birthweight and small-for-gestational-age infants in China: a large prospective cohort study. J. Clin. Hypertens. 23, 835 (2021).
Gibbs, C. M., Wendt, A., Peters, S. & Hogue, C. J. The impact of early age at first childbirth on maternal and infant health. Paediatr. Perinat. Epidemiol. 26, 259–284 (2012).
Markovitz, B. P., Cook, R., Flick, L. H. & Leet, T. L. Socioeconomic factors and adolescent pregnancy outcomes: distinctions between neonatal and post-neonatal deaths?. BMC Public Health 5, 279 (2005).
Sharma, V. et al. Young maternal age and the risk of neonatal mortality in rural Nepal. Arch. Pediatr. Adolesc. Med. 162, 828–835 (2008).
Alam, N. Teenage motherhood and infant mortality in Bangladesh: maternal age-dependent effect of parity one. J. Biosoc. Sci. 32, 229–236 (2000).
Restrepo-Méndez, M. C. et al. Childbearing during adolescence and offspring mortality: findings from three population-based cohorts in southern Brazil. BMC Public Health 11, 781 (2011).
Paranjothy, S., Broughton, H., Adappa, R. & Fone, D. Teenage pregnancy: who suffers?. Arch. Dis. Child. 94, 239–245 (2009).
Lawlor, D. A., Mortensen, L. & Andersen, A. M. N. Mechanisms underlying the associations of maternal age with adverse perinatal outcomes: a sibling study of 264,695 Danish women and their firstborn offspring. Int. J. Epidemiol. 40, 1205–1214 (2011).
Fall, C. H. D. et al. Association between maternal age at childbirth and child and adult outcomes in the offspring: a prospective study in five low-income and middle-income countries (COHORTS collaboration). Lancet Glob. Health 3, e366–e377 (2015).
Alves, C., Jenkins, S. M. & Rapp, A. Early pregnancy loss (spontaneous abortion) (StatPearls, 2023).
Jackson, T. & Watkins, E. Early pregnancy loss. J. Am. Acad. Physician Assist. 34, 22–27 (2021).
Nicolaides, K. H. A model for a new pyramid of prenatal care based on the 11 to 13 weeks’ assessment. Prenat. Diagn. 31, 3–6 (2011).
Bekele, W. T. Machine learning algorithms for predicting low birth weight in Ethiopia. BMC Med. Inform. Decis. Mak. 22, 1–16 (2022).
Arayeshgari, M., Najafi-Ghobadi, S., Tarhsaz, H., Parami, S. & Tapak, L. Machine learning-based classifiers for the prediction of low birth weight. Healthc. Inform. Res. 29, 54 (2023).
Pollob, S. M. A. I., Abedin, M. M., Islam, M. T., Islam, M. M. & Maniruzzaman, M. Predicting risks of low birth weight in Bangladesh with machine learning. PLoS ONE 17, e000000 (2022).
Khan, W. et al. Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms. Sci. Rep. 12, 1–12 (2022).
Hussain, Z. & Borah, M. D. Birth weight prediction of newborn baby with application of machine learning techniques on features of mother. J. Stat. Manag. Syst. 23, 1079–1091 (2020).
Alabbad, D. A. et al. Birthweight range prediction and classification: a machine learning-based sustainable approach. Mach. Learn. Knowl. Extr. 6, 770–788 (2024).
Dola, S. S. & Valderrama, C. E. Exploring parental factors influencing low birth weight on the 2022 CDC natality dataset. BMC Med. Inform. Decis. Mak. 24, 367 (2024).
Liu, Q. et al. Machine learning approaches for predicting fetal macrosomia at different stages of pregnancy: a retrospective study in China. BMC Pregnancy Childbirth 25, 1–10 (2025).
Brand Stories
Studying a galaxy far, far away could become easier with help from AI, says researcher
A recent Memorial University of Newfoundland graduate says his research may help study galaxies more efficiently — with help from Artificial Intelligence.
As part of Youssef Zaazou’s master’s of science, he developed an AI-based image-processing technique that generates predictions of what certain galaxies may look like in a given wavelength of light.
“Think of it as translating galaxy images across different wavelengths of light,” Zaazou told CBC News over email.
He did this by researching past methods for similar tasks, adapting current AI tools for his specific purposes, finding and curating the right dataset to train the models, along with plenty of trial and error.
“Instead of … having to look at an entire region of sky, we can get predictions for certain regions and figure out, ‘Oh this might be interesting to look at,'” said Zaazou. “So we can then prioritize how we use our telescope resources.”
Zaazou recently teamed up with his supervisors Terrence Tricco and Alex Bihlo to co-author a paper on his research in The Astrophysical Journal, which is published by The American Astronomical Society.
Tricco says this research could also help justify allocation of high-demand telescopes like the Hubble Space Telescope, which has a competitive process to assign its use.
A future for AI in astronomy
Both Tricco and Zaazou emphasised the research does not use AI to replace current methods but to augment them.
Tricco says that Zaazou’s findings have the potential to help guide future telescope development, and predict what astronomers might expect to see, making for more efficient exploration.
Calling The Astrophysical Journal the “gold standard” for astronomy journals in the world, Tricco hopes the wider astronomical community will take notice of Zaazou’s findings.
“We want to have them be aware of this because as I was mentioning, AI, machine learning, and physics, astronomy, it’s still very new for physicists and for astronomers, and they’re a little bit hesitant about these tools,” said Tricco.
Tricco praised the growing presence of space research in general at Memorial University.
“We are here, we’re doing great research,” he said.
He added growing AI expertise is also transferable to other disciplines.
“I think that builds into our just tech ecosystem here as well.”
‘Only the beginning’
Though Zaazou’s time as a Memorial University student is over, he hopes to see research in this area continue to grow.
“I’m hoping this is the beginning of further research to be done,” he said.
Though Zaazou described his contribution to the field as merely a “pebble,” he’s happy to have been able to do his part.
“I’m an astronomer. And it just feels great to be able to say that and to be able to have that little contribution because I just love the field and I’m fascinated by everything out there,” said Zaazou.
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‘You can make really good stuff – fast’: new AI tools a gamechanger for film-makers | Artificial intelligence (AI)
A US stealth bomber flies across a darkening sky towards Iran. Meanwhile, in Tehran a solitary woman feeds stray cats amid rubble from recent Israeli airstrikes.
To the uninitiated viewer, this could be a cinematic retelling of a geopolitical crisis that unfolded barely weeks ago – hastily shot on location, somewhere in the Middle East.
However, despite its polished production look, it wasn’t shot anywhere, there is no location, and the woman feeding stray cats is no actor – she doesn’t exist.
The engrossing footage is the “rough cut” of a 12-minute short film about last month’s US attack on Iranian nuclear sites, made by the directors Samir Mallal and Bouha Kazmi. It is also made entirely by artificial intelligence.
The clip is based on a detail the film-makers read in news coverage of the US bombings – a woman who walked the empty streets of Tehran feeding stray cats. Armed with the information, they have been able to make a sequence that looks as if it could have been created by a Hollywood director.
The impressive speed and, for some, worrying ease with which films of this kind can be made has not been lost on broadcasting experts.
Last week Richard Osman, the TV producer and bestselling author, said that an era of entertainment industry history had ended and a new one had begun – all because Google has released a new AI video making tool used by Mallal and others.
“So I saw this thing and I thought, ‘well, OK that’s the end of one part of entertainment history and the beginning of another’,” he said on The Rest is Entertainment podcast.
Osman added: “TikTok, ads, trailers – anything like that – I will say will be majority AI-assisted by 2027.”
For Mallal, a award-winning London-based documentary maker who has made adverts for Samsung and Coca-Cola, AI has provided him with a new format – “cinematic news”.
The Tehran film, called Midnight Drop, is a follow-up to Spiders in the Sky, a recreation of a Ukrainian drone attack on Russian bombers in June.
Within two weeks, Mallal, who directed Spiders in the Sky on his own, was able to make a film about the Ukraine attack that would have cost millions – and would have taken at least two years including development – to make pre-AI.
“Using AI, it should be possible to make things that we’ve never seen before,” he said. “We’ve never seen a cinematic news piece before turned around in two weeks. We’ve never seen a thriller based on the news made in two weeks.”
Spiders in the Sky was largely made with Veo3, an AI video generation model developed by Google, and other AI tools. The voiceover, script and music were not created by AI, although ChatGPT helped Mallal edit a lengthy interview with a drone operator that formed the film’s narrative spine.
Google’s film-making tool, Flow, is powered by Veo3. It also creates speech, sound effects and background noise. Since its release in May, the impact of the tool on YouTube – also owned by Google – and social media in general has been marked. As Marina Hyde, Osman’s podcast partner, said last week: “The proliferation is extraordinary.”
Quite a lot of it is “slop” – the term for AI-generated nonsense – although the Olympic diving dogs have a compelling quality.
Mallal and Kazmi aim to complete the film, which will intercut the Iranian’s story with the stealth bomber mission and will be six times the length of Spider’s two minutes, in August. It is being made by a mix of models including Veo3, OpenAI’s Sora and Midjourney.
“I’m trying to prove a point,” says Mallal. “Which is that you can make really good stuff at a high level – but fast, at the speed of culture. Hollywood, especially, moves incredibly slowly.”
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He adds: “The creative process is all about making bad stuff to get to the good stuff. We have the best bad ideas faster. But the process is accelerated with AI.”
Mallal and Kazmi also recently made Atlas, Interrupted, a short film about the 3I/Atlas comet, another recent news event, that has appeared on the BBC.
David Jones, the chief executive of Brandtech Group, an advertising startup using generative AI – the term for tools such as chatbots and video generators – to create marketing campaigns, says the advertising world is about to undergo a revolution due to models such as Veo3.
“Today, less than 1% of all brand content is created using gen AI. It will be 100% that is fully or partly created using gen AI,” he says.
Netflix also revealed last week that it used AI in one of its TV shows for the first time.
However, in the background of this latest surge in AI-spurred creativity lies the issue of copyright. In the UK, the creative industries are furious about government proposals to let models be trained on copyright-protected work without seeking the owner’s permission – unless the owner opts out of the process.
Mallal says he wants to see a “broadly accessible and easy-to-use programme where artists are compensated for their work”.
Beeban Kidron, a cross-bench peer and leading campaigner against the government proposals, says AI film-making tools are “fantastic” but “at what point are they going to realise that these tools are literally built on the work of creators?” She adds: “Creators need equity in the new system or we lose something precious.”
YouTube says its terms and conditions allow Google to use creators’ work for making AI models – and denies that all of YouTube’s inventory has been used to train its models.
Mallal calls his use of AI to make films “prompt craft”, a phrase that uses the term for giving instructions to AI systems. When making the Ukraine film, he says he was amazed at how quickly a camera angle or lighting tone could be adjusted with a few taps on a keyboard.
“I’m deep into AI. I’ve learned how to prompt engineer. I’ve learned how to translate my skills as a director into prompting. But I’ve never produced anything creative from that. Then Veo3 comes out, and I said, ‘OK, finally, we’re here.’”
We stand at a technological crossroads remarkably similar to the early 2000s, when the internet’s explosive growth outpaced existing infrastructure capabilities. Just as dial-up connections couldn’t support the emerging digital economy, today’s classical computing systems are hitting fundamental limits that will constrain AI’s continued evolution. The solution lies in quantum computing – and the next five to six years will determine whether we successfully navigate this crucial transition.
The computational ceiling blocking AI advancement
Current AI systems face insurmountable mathematical barriers that mirror the bandwidth bottlenecks of early internet infrastructure. Training large language models like GPT-3 consumes 1,300 megawatt-hours of electricity, while classical optimization problems require exponentially increasing computational resources. Google’s recent demonstration starkly illustrates this divide: their Willow quantum processor completed calculations in five minutes that would take classical supercomputers 10 septillion years – while consuming 30,000 times less energy.
The parallels to early 2000s telecommunications are striking. Then, streaming video, cloud computing, and e-commerce demanded faster data speeds that existing infrastructure couldn’t provide. Today, AI applications like real-time molecular simulation, financial risk optimization, and large-scale pattern recognition are pushing against the physical limits of classical computing architectures. Just as the internet required fiber optic cables and broadband infrastructure, AI’s next phase demands quantum computational capabilities.
Breakthrough momentum accelerating toward mainstream adoption
The quantum computing landscape has undergone transformative changes in 2024-2025 that signal mainstream viability. Google’s Willow chip achieved below-threshold error correction – a critical milestone where quantum systems become more accurate as they scale up. IBM’s roadmap targets 200 logical qubits by 2029, while Microsoft’s topological qubit breakthrough promises inherent error resistance. These aren’t incremental improvements; they represent fundamental advances that make practical quantum-AI systems feasible.
Industry investments reflect this transition from research to commercial reality. Quantum startups raised $2 billion in 2024, representing a 138 per cent increase from the previous year. Major corporations are backing this confidence with substantial commitments: IBM’s $30 billion quantum R&D investment, Microsoft’s quantum-ready initiative for 2025, and Google’s $5 million quantum applications prize. The market consensus projects quantum computing revenue will exceed $1 billion in 2025 and reach $28-72 billion by 2035.
Expert consensus on the five-year transformation window
Leading quantum computing experts across multiple organizations align on a remarkably consistent timeline. IBM’s CEO predicts quantum advantage demonstrations by 2026, while Google targets useful quantum computers by 2029. Quantinuum’s roadmap promises universal fault-tolerant quantum computing by 2030. IonQ projects commercial quantum advantages in machine learning by 2027. This convergence suggests the 2025-2030 period will be as pivotal for quantum computing as 1995-2000 was for internet adoption.
The technical indicators support these projections. Current quantum systems achieve 99.9 per cent gate fidelity – crossing the threshold for practical applications. Multiple companies have demonstrated quantum advantages in specific domains: JPMorgan and Amazon reduced portfolio optimization problems by 80 per cent, while quantum-enhanced traffic optimization decreased Beijing congestion by 20 per cent. These proof-of-concept successes mirror the early internet’s transformative applications before widespread adoption.
Real-world quantum-AI applications emerging across industries
The most compelling evidence comes from actual deployments showing measurable improvements. Cleveland Clinic and IBM launched a dedicated healthcare quantum computer for protein interaction modeling in cancer research. Pfizer partnered with IBM for quantum molecular modeling in drug discovery. DHL optimized international shipping routes using quantum algorithms, reducing delivery times by 20 per cent.
These applications demonstrate quantum computing’s unique ability to solve problems that scale exponentially with classical approaches. Quantum systems process multiple possibilities simultaneously through superposition, enabling breakthrough capabilities in optimization, simulation, and machine learning that classical computers cannot replicate efficiently. The energy efficiency advantages are equally dramatic – quantum systems achieve 3-4 orders of magnitude better energy consumption for specific computational tasks.
The security imperative driving quantum adoption
Beyond performance advantages, quantum computing addresses critical security challenges that will force rapid adoption. Current encryption methods protecting AI systems will become vulnerable to quantum attacks within this decade. The US government has mandated federal agencies transition to quantum-safe cryptography, while NIST released new post-quantum encryption standards in 2024. Organizations face a “harvest now, decrypt later” threat where adversaries collect encrypted data today for future quantum decryption.
This security imperative creates unavoidable pressure for quantum adoption. Satellite-based quantum communication networks are already operational, with China’s quantum network spanning 12,000 kilometers and similar projects launching globally. The intersection of quantum security and AI protection will drive widespread infrastructure upgrades in the coming years.
Preparing for the quantum era transformation
The evidence overwhelmingly suggests we’re approaching a technological inflection point where quantum computing transitions from experimental curiosity to essential infrastructure. Just as businesses that failed to adapt to internet connectivity fell behind in the early 2000s, organizations that ignore quantum computing risk losing competitive advantage in the AI-driven economy.
The quantum revolution isn’t coming- it’s here. The next five to six years will determine which organizations successfully navigate this transition and which turn into casualties of technological change. AI systems must re-engineer themselves to leverage quantum capabilities, requiring new algorithms, architectures, and approaches that blend quantum and classical computing.
This represents more than incremental improvement; it’s a fundamental paradigm shift that will reshape how we approach computation, security, and artificial intelligence. The question isn’t whether quantum computing will transform AI – it’s whether we’ll be ready for the transformation.
(Krishna Kumar is a technology explorer & strategist based in Austin, Texas in the US. Rakshitha Reddy is AI developer based in Atlanta, US)
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