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Toward point-of-care ultrasound estimation of fetal gestational age from the trans-cerebellar diameter using CNN-based ultrasound image analysis

Research output: Contribution to journalArticle

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Toward point-of-care ultrasound estimation of fetal gestational age from the trans-cerebellar diameter using CNN-based ultrasound image analysis. / Maraci, Mohammad A.; Yaqub, Mohammad; Craik, Rachel; Beriwal, Sridevi; Self, Alice; Von Dadelszen, Peter; Papageorghiou, Aris; Noble, J. Alison.

In: Journal of Medical Imaging, Vol. 7, No. 1, 014501, 01.01.2020.

Research output: Contribution to journalArticle

Harvard

Maraci, MA, Yaqub, M, Craik, R, Beriwal, S, Self, A, Von Dadelszen, P, Papageorghiou, A & Noble, JA 2020, 'Toward point-of-care ultrasound estimation of fetal gestational age from the trans-cerebellar diameter using CNN-based ultrasound image analysis', Journal of Medical Imaging, vol. 7, no. 1, 014501. https://doi.org/10.1117/1.JMI.7.1.014501

APA

Maraci, M. A., Yaqub, M., Craik, R., Beriwal, S., Self, A., Von Dadelszen, P., Papageorghiou, A., & Noble, J. A. (2020). Toward point-of-care ultrasound estimation of fetal gestational age from the trans-cerebellar diameter using CNN-based ultrasound image analysis. Journal of Medical Imaging, 7(1), [014501]. https://doi.org/10.1117/1.JMI.7.1.014501

Vancouver

Maraci MA, Yaqub M, Craik R, Beriwal S, Self A, Von Dadelszen P et al. Toward point-of-care ultrasound estimation of fetal gestational age from the trans-cerebellar diameter using CNN-based ultrasound image analysis. Journal of Medical Imaging. 2020 Jan 1;7(1). 014501. https://doi.org/10.1117/1.JMI.7.1.014501

Author

Maraci, Mohammad A. ; Yaqub, Mohammad ; Craik, Rachel ; Beriwal, Sridevi ; Self, Alice ; Von Dadelszen, Peter ; Papageorghiou, Aris ; Noble, J. Alison. / Toward point-of-care ultrasound estimation of fetal gestational age from the trans-cerebellar diameter using CNN-based ultrasound image analysis. In: Journal of Medical Imaging. 2020 ; Vol. 7, No. 1.

Bibtex Download

@article{5609424d92ba4e93881b8d07731e98bc,
title = "Toward point-of-care ultrasound estimation of fetal gestational age from the trans-cerebellar diameter using CNN-based ultrasound image analysis",
abstract = "Obstetric ultrasound is a fundamental ingredient of modern prenatal care with many applications including accurate dating of a pregnancy, identifying pregnancy-related complications, and diagnosis of fetal abnormalities. However, despite its many benefits, two factors currently prevent wide-scale uptake of this technology for point-of-care clinical decision-making in low- and middle-income country (LMIC) settings. First, there is a steep learning curve for scan proficiency, and second, there has been a lack of easy-to-use, affordable, and portable ultrasound devices. We introduce a framework toward addressing these barriers, enabled by recent advances in machine learning applied to medical imaging. The framework is designed to be realizable as a point-of-care ultrasound (POCUS) solution with an affordable wireless ultrasound probe, a smartphone or tablet, and automated machine-learning-based image processing. Specifically, we propose a machine-learning-based algorithm pipeline designed to automatically estimate the gestational age of a fetus from a short fetal ultrasound scan. We present proof-of-concept evaluation of accuracy of the key image analysis algorithms for automatic head transcerebellar plane detection, automatic transcerebellar diameter measurement, and estimation of gestational age on conventional ultrasound data simulating the POCUS task and discuss next steps toward translation via a first application on clinical ultrasound video from a low-cost ultrasound probe.",
keywords = "gestational age, global health, machine learning, point-of-care ultrasound, prenatal health",
author = "Maraci, {Mohammad A.} and Mohammad Yaqub and Rachel Craik and Sridevi Beriwal and Alice Self and {Von Dadelszen}, Peter and Aris Papageorghiou and Noble, {J. Alison}",
year = "2020",
month = jan,
day = "1",
doi = "10.1117/1.JMI.7.1.014501",
language = "English",
volume = "7",
journal = "Journal of medical imaging (Bellingham, Wash.)",
issn = "2329-4302",
publisher = "SPIE",
number = "1",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Toward point-of-care ultrasound estimation of fetal gestational age from the trans-cerebellar diameter using CNN-based ultrasound image analysis

AU - Maraci, Mohammad A.

AU - Yaqub, Mohammad

AU - Craik, Rachel

AU - Beriwal, Sridevi

AU - Self, Alice

AU - Von Dadelszen, Peter

AU - Papageorghiou, Aris

AU - Noble, J. Alison

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Obstetric ultrasound is a fundamental ingredient of modern prenatal care with many applications including accurate dating of a pregnancy, identifying pregnancy-related complications, and diagnosis of fetal abnormalities. However, despite its many benefits, two factors currently prevent wide-scale uptake of this technology for point-of-care clinical decision-making in low- and middle-income country (LMIC) settings. First, there is a steep learning curve for scan proficiency, and second, there has been a lack of easy-to-use, affordable, and portable ultrasound devices. We introduce a framework toward addressing these barriers, enabled by recent advances in machine learning applied to medical imaging. The framework is designed to be realizable as a point-of-care ultrasound (POCUS) solution with an affordable wireless ultrasound probe, a smartphone or tablet, and automated machine-learning-based image processing. Specifically, we propose a machine-learning-based algorithm pipeline designed to automatically estimate the gestational age of a fetus from a short fetal ultrasound scan. We present proof-of-concept evaluation of accuracy of the key image analysis algorithms for automatic head transcerebellar plane detection, automatic transcerebellar diameter measurement, and estimation of gestational age on conventional ultrasound data simulating the POCUS task and discuss next steps toward translation via a first application on clinical ultrasound video from a low-cost ultrasound probe.

AB - Obstetric ultrasound is a fundamental ingredient of modern prenatal care with many applications including accurate dating of a pregnancy, identifying pregnancy-related complications, and diagnosis of fetal abnormalities. However, despite its many benefits, two factors currently prevent wide-scale uptake of this technology for point-of-care clinical decision-making in low- and middle-income country (LMIC) settings. First, there is a steep learning curve for scan proficiency, and second, there has been a lack of easy-to-use, affordable, and portable ultrasound devices. We introduce a framework toward addressing these barriers, enabled by recent advances in machine learning applied to medical imaging. The framework is designed to be realizable as a point-of-care ultrasound (POCUS) solution with an affordable wireless ultrasound probe, a smartphone or tablet, and automated machine-learning-based image processing. Specifically, we propose a machine-learning-based algorithm pipeline designed to automatically estimate the gestational age of a fetus from a short fetal ultrasound scan. We present proof-of-concept evaluation of accuracy of the key image analysis algorithms for automatic head transcerebellar plane detection, automatic transcerebellar diameter measurement, and estimation of gestational age on conventional ultrasound data simulating the POCUS task and discuss next steps toward translation via a first application on clinical ultrasound video from a low-cost ultrasound probe.

KW - gestational age

KW - global health

KW - machine learning

KW - point-of-care ultrasound

KW - prenatal health

UR - http://www.scopus.com/inward/record.url?scp=85081211211&partnerID=8YFLogxK

U2 - 10.1117/1.JMI.7.1.014501

DO - 10.1117/1.JMI.7.1.014501

M3 - Article

AN - SCOPUS:85081211211

VL - 7

JO - Journal of medical imaging (Bellingham, Wash.)

JF - Journal of medical imaging (Bellingham, Wash.)

SN - 2329-4302

IS - 1

M1 - 014501

ER -

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