Prediction of embryo implantation by machine learning based on ultrasound strain imaging

F. Sammali; C. Blank; T. H.G.F. Bakkes; Y. Huang; C. Rabotti; B. C. Schoot; M. Mischi

doi:10.1109/ULTSYM.2019.8926228

Prediction of embryo implantation by machine learning based on ultrasound strain imaging

F. Sammali, C. Blank, T. H.G.F. Bakkes, Y. Huang, C. Rabotti, B. C. Schoot, M. Mischi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

Abstract

Because of the trend to postpone childbirth, the rate of couples dealing with infertility is rapidly increasing and approaching 20%. In-vitro fertilization (IVF) represents the only reproduction option in Europe for 2.5 million couples. However, its success rate remains below 30%. There is clear consensus on a major involvement of uterine contractions in IVF failure, especially during and after embryo transfer. Quantitative and non-invasive measurement of uterine (peristaltic) activity, combined with accurate interpretation and classification methods, can provide an important contribution towards improved IVF success rates. Therefore, this study investigates the use of machine learning for probabilistic classification of the uterine activity, as either favorable or adverse to embryo implantation. The results obtained in 16 patients undergoing an IVF cycle confirm the ability to predict successful embryo implantation by ultrasound uterine motion analysis combined with machine learning.

Original language	English
Title of host publication	2019 IEEE International Ultrasonics Symposium, IUS 2019
Place of Publication	Piscataway
Publisher	IEEE Computer Society
Pages	1141-1144
Number of pages	4
ISBN (Electronic)	978-1-7281-4596-9
DOIs	https://doi.org/10.1109/ULTSYM.2019.8926228
Publication status	Published - Oct 2019
Event	2019 IEEE International Ultrasonics Symposium, IUS 2019 - Glasgow, United Kingdom Duration: 6 Oct 2019 → 9 Oct 2019

Conference

Conference	2019 IEEE International Ultrasonics Symposium, IUS 2019
Country/Territory	United Kingdom
City	Glasgow
Period	6/10/19 → 9/10/19

Keywords

feature selection
in-vitro fertilization
machine learning
medical ultrasound
speckle tracking
uterine motion

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10.1109/ULTSYM.2019.8926228

Cite this

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title = "Prediction of embryo implantation by machine learning based on ultrasound strain imaging",

abstract = "Because of the trend to postpone childbirth, the rate of couples dealing with infertility is rapidly increasing and approaching 20%. In-vitro fertilization (IVF) represents the only reproduction option in Europe for 2.5 million couples. However, its success rate remains below 30%. There is clear consensus on a major involvement of uterine contractions in IVF failure, especially during and after embryo transfer. Quantitative and non-invasive measurement of uterine (peristaltic) activity, combined with accurate interpretation and classification methods, can provide an important contribution towards improved IVF success rates. Therefore, this study investigates the use of machine learning for probabilistic classification of the uterine activity, as either favorable or adverse to embryo implantation. The results obtained in 16 patients undergoing an IVF cycle confirm the ability to predict successful embryo implantation by ultrasound uterine motion analysis combined with machine learning.",

keywords = "feature selection, in-vitro fertilization, machine learning, medical ultrasound, speckle tracking, uterine motion",

author = "F. Sammali and C. Blank and Bakkes, {T. H.G.F.} and Y. Huang and C. Rabotti and Schoot, {B. C.} and M. Mischi",

year = "2019",

month = oct,

doi = "10.1109/ULTSYM.2019.8926228",

language = "English",

pages = "1141--1144",

booktitle = "2019 IEEE International Ultrasonics Symposium, IUS 2019",

publisher = "IEEE Computer Society",

address = "United States",

note = "2019 IEEE International Ultrasonics Symposium, IUS 2019 ; Conference date: 06-10-2019 Through 09-10-2019",

}

Sammali, F, Blank, C, Bakkes, THGF , Huang, Y, Rabotti, C, Schoot, BC & Mischi, M 2019, Prediction of embryo implantation by machine learning based on ultrasound strain imaging. in 2019 IEEE International Ultrasonics Symposium, IUS 2019., 8926228, IEEE Computer Society, Piscataway, pp. 1141-1144, 2019 IEEE International Ultrasonics Symposium, IUS 2019, Glasgow, United Kingdom, 6/10/19. https://doi.org/10.1109/ULTSYM.2019.8926228

Prediction of embryo implantation by machine learning based on ultrasound strain imaging. / Sammali, F.; Blank, C.; Bakkes, T. H.G.F. et al.

2019 IEEE International Ultrasonics Symposium, IUS 2019. Piscataway : IEEE Computer Society, 2019. p. 1141-1144 8926228.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Schoot, B. C.

AU - Mischi, M.

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AB - Because of the trend to postpone childbirth, the rate of couples dealing with infertility is rapidly increasing and approaching 20%. In-vitro fertilization (IVF) represents the only reproduction option in Europe for 2.5 million couples. However, its success rate remains below 30%. There is clear consensus on a major involvement of uterine contractions in IVF failure, especially during and after embryo transfer. Quantitative and non-invasive measurement of uterine (peristaltic) activity, combined with accurate interpretation and classification methods, can provide an important contribution towards improved IVF success rates. Therefore, this study investigates the use of machine learning for probabilistic classification of the uterine activity, as either favorable or adverse to embryo implantation. The results obtained in 16 patients undergoing an IVF cycle confirm the ability to predict successful embryo implantation by ultrasound uterine motion analysis combined with machine learning.

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Prediction of embryo implantation by machine learning based on ultrasound strain imaging

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