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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

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.

Originele taal-2Engels
Titel2019 IEEE International Ultrasonics Symposium, IUS 2019
Plaats van productiePiscataway
UitgeverijIEEE Computer Society
Pagina's1141-1144
Aantal pagina's4
ISBN van elektronische versie978-1-7281-4596-9
DOI's
StatusGepubliceerd - okt 2019
Evenement2019 IEEE International Ultrasonics Symposium, IUS 2019 - Glasgow, Verenigd Koninkrijk
Duur: 6 okt 20199 okt 2019

Congres

Congres2019 IEEE International Ultrasonics Symposium, IUS 2019
LandVerenigd Koninkrijk
StadGlasgow
Periode6/10/199/10/19

Vingerafdruk Duik in de onderzoeksthema's van 'Prediction of embryo implantation by machine learning based on ultrasound strain imaging'. Samen vormen ze een unieke vingerafdruk.

Citeer dit