Prediction of newborn's body mass index using nationwide multicenter ultrasound data: a machine-learning study

doi:10.1186/s12884-021-03660-5

. 2021 Mar 2;21(1):172.

doi: 10.1186/s12884-021-03660-5.

Prediction of newborn's body mass index using nationwide multicenter ultrasound data: a machine-learning study

Kwang-Sig Lee^#¹, Ho Yeon Kim^#², Se Jin Lee³, Sung Ok Kwon⁴, Sunghun Na⁵, Han Sung Hwang⁶, Mi Hye Park⁷, Ki Hoon Ahn⁸; Korean Society of Ultrasound in Obstetrics and Gynecology Research Group

Affiliations

¹ AI Center, Korea University College of Medicine, Seoul, South Korea.
² Department of Obstetrics and Gynecology, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul, 02841, South Korea.
³ Department of Obstetrics and Gynecology, Kangwon National University Hospital, Kangwon National University School of Medicine, Kangwon, Chuncheon, South Korea.
⁴ Department of Preventive Medicine, Kangwon National University School of Medicine, Kangwon, Chuncheon, South Korea.
⁵ Department of Obstetrics and Gynecology, Kangwon National University Hospital, Kangwon National University School of Medicine, Kangwon, Chuncheon, South Korea. lahun@kangwon.ac.kr.
⁶ Department of Obstetrics and Gynecology, Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, South Korea.
⁷ Department of Obstetrics and Gynecology, Ewha Medical Institute, Ewha Medical Center, Ewha Womans University College of Medicine, Seoul, South Korea.
⁸ Department of Obstetrics and Gynecology, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul, 02841, South Korea. akh1220@hanmail.net.

^# Contributed equally.

PMID: 33653299
PMCID: PMC7927215
DOI: 10.1186/s12884-021-03660-5

Prediction of newborn's body mass index using nationwide multicenter ultrasound data: a machine-learning study

Kwang-Sig Lee et al. BMC Pregnancy Childbirth. 2021.

. 2021 Mar 2;21(1):172.

doi: 10.1186/s12884-021-03660-5.

Authors

Affiliations

¹ AI Center, Korea University College of Medicine, Seoul, South Korea.
² Department of Obstetrics and Gynecology, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul, 02841, South Korea.
³ Department of Obstetrics and Gynecology, Kangwon National University Hospital, Kangwon National University School of Medicine, Kangwon, Chuncheon, South Korea.
⁴ Department of Preventive Medicine, Kangwon National University School of Medicine, Kangwon, Chuncheon, South Korea.
⁵ Department of Obstetrics and Gynecology, Kangwon National University Hospital, Kangwon National University School of Medicine, Kangwon, Chuncheon, South Korea. lahun@kangwon.ac.kr.
⁶ Department of Obstetrics and Gynecology, Research Institute of Medical Science, Konkuk University School of Medicine, Seoul, South Korea.
⁷ Department of Obstetrics and Gynecology, Ewha Medical Institute, Ewha Medical Center, Ewha Womans University College of Medicine, Seoul, South Korea.
⁸ Department of Obstetrics and Gynecology, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul, 02841, South Korea. akh1220@hanmail.net.

^# Contributed equally.

PMID: 33653299
PMCID: PMC7927215
DOI: 10.1186/s12884-021-03660-5

Abstract

Background: This study introduced machine learning approaches to predict newborn's body mass index (BMI) based on ultrasound measures and maternal/delivery information.

Methods: Data came from 3159 obstetric patients and their newborns enrolled in a multi-center retrospective study. Variable importance, the effect of a variable on model performance, was used for identifying major predictors of newborn's BMI among ultrasound measures and maternal/delivery information. The ultrasound measures included biparietal diameter (BPD), abdominal circumference (AC) and estimated fetal weight (EFW) taken three times during the week 21 - week 35 of gestational age and once in the week 36 or later.

Results: Based on variable importance from the random forest, major predictors of newborn's BMI were the first AC and EFW in the week 36 or later, gestational age at delivery, the first AC during the week 21 - the week 35, maternal BMI at delivery, maternal weight at delivery and the first BPD in the week 36 or later. For predicting newborn's BMI, linear regression (2.0744) and the random forest (2.1610) were better than artificial neural networks with one, two and three hidden layers (150.7100, 154.7198 and 152.5843, respectively) in the mean squared error.

Conclusions: This is the first machine-learning study with 64 clinical and sonographic markers for the prediction of newborns' BMI. The week 36 or later is the most effective period for taking the ultrasound measures and AC and EFW are the best predictors of newborn's BMI alongside gestational age at delivery and maternal BMI at delivery.

Keywords: Abdominal circumference; Body mass index; Estimated fetal weight; Newborn.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

**Fig. 1**
Variable Importance Values of Top 20 Predictors for Newborn’s Body Mass Index from the Random Forest. **Notes***. AC:* Abdominal Circumference. *BMI:* Body Mass Index. *BPD:* Biparietal Diameter. *CRL:* Crown-Rump Length. *EFW:* Estimated Fetal Weight. *FL:* Femur Length. *HC:* Head Circumference. *NT:* Nuchal Translucency. *GA11:* Gestational Age, Week 11 - Week 13. *GA14:* Gestational Age, Week 14 - Week 19. *GA20:* Gestational Age, Week 20. *GA21:* Gestational Age, Week 21 - Week 35. *GA36:* Gestational Age, Week 36 or Later. *W/D:* Gestational Age - Weeks/Days

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References

1. Blencowe H, Krasevec J, de Onis M, Black RE, An X, Stevens GA, et al. National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: a systematic analysis. Lancet Glob Health. 2019;7(7):e849–e860. doi: 10.1016/S2214-109X(18)30565-5. - DOI - PMC - PubMed
1. World Health Organization. Global database on child health and malnutrition. Geneva: WHO; 2019. http://www.who.int/nutgrowthdb/estimates/en/. Accessed 24 Apr 2020.
1. Lang JE, Bunnell HT, Hossain MJ, Wysocki T, Lima JJ, Finkel TH, et al. Being overweight or obese and the development of asthma. Pediatrics. 2018;142(6). pii: e20182119. - PubMed
1. Quek YH, Tam WWS, Zhang MWB, Ho RCM. Exploring the association between childhood and adolescent obesity and depression: a meta-analysis. Obes Rev. 2017;18(7):742–754. doi: 10.1111/obr.12535. - DOI - PubMed
1. Pulgaron ER, Delamater AM. Obesity and type 2 diabetes in children: epidemiology and treatment. Curr Diab Rep. 2014;14(8):508. doi: 10.1007/s11892-014-0508-y. - DOI - PMC - PubMed

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[1] Blencowe H, Krasevec J, de Onis M, Black RE, An X, Stevens GA, et al. National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: a systematic analysis. Lancet Glob Health. 2019;7(7):e849–e860. doi: 10.1016/S2214-109X(18)30565-5. - DOI - PMC - PubMed

[2] Blencowe H, Krasevec J, de Onis M, Black RE, An X, Stevens GA, et al. National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: a systematic analysis. Lancet Glob Health. 2019;7(7):e849–e860. doi: 10.1016/S2214-109X(18)30565-5. - DOI - PMC - PubMed

[3] World Health Organization. Global database on child health and malnutrition. Geneva: WHO; 2019. http://www.who.int/nutgrowthdb/estimates/en/. Accessed 24 Apr 2020.

[4] World Health Organization. Global database on child health and malnutrition. Geneva: WHO; 2019. http://www.who.int/nutgrowthdb/estimates/en/. Accessed 24 Apr 2020.

[5] Lang JE, Bunnell HT, Hossain MJ, Wysocki T, Lima JJ, Finkel TH, et al. Being overweight or obese and the development of asthma. Pediatrics. 2018;142(6). pii: e20182119. - PubMed

[6] Lang JE, Bunnell HT, Hossain MJ, Wysocki T, Lima JJ, Finkel TH, et al. Being overweight or obese and the development of asthma. Pediatrics. 2018;142(6). pii: e20182119. - PubMed

[7] Quek YH, Tam WWS, Zhang MWB, Ho RCM. Exploring the association between childhood and adolescent obesity and depression: a meta-analysis. Obes Rev. 2017;18(7):742–754. doi: 10.1111/obr.12535. - DOI - PubMed

[8] Quek YH, Tam WWS, Zhang MWB, Ho RCM. Exploring the association between childhood and adolescent obesity and depression: a meta-analysis. Obes Rev. 2017;18(7):742–754. doi: 10.1111/obr.12535. - DOI - PubMed

[9] Pulgaron ER, Delamater AM. Obesity and type 2 diabetes in children: epidemiology and treatment. Curr Diab Rep. 2014;14(8):508. doi: 10.1007/s11892-014-0508-y. - DOI - PMC - PubMed

[10] Pulgaron ER, Delamater AM. Obesity and type 2 diabetes in children: epidemiology and treatment. Curr Diab Rep. 2014;14(8):508. doi: 10.1007/s11892-014-0508-y. - DOI - PMC - PubMed

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Prediction of newborn's body mass index using nationwide multicenter ultrasound data: a machine-learning study

Affiliations

Prediction of newborn's body mass index using nationwide multicenter ultrasound data: a machine-learning study

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

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