公开(公告)号：US20240170147A1

公开(公告)日：2024-05-23

申请号：US18280254

申请日：2022-03-03

Applicant: Tel HaShomer Medical Research Infrastructure and Services Ltd.

Inventor： Abraham TSUR ,  Raanan MEYER ,  Roni EILENBERG

IPC: G16H50/20 ,  A61B5/00 ,  A61B8/08 ,  G16H50/30

CPC classification number: G16H50/20 ,  A61B5/435 ,  A61B8/0866 ,  G16H50/30

Abstract: There is provided a computer implemented method of training a machine learning model for prediction of unplanned Cesarean Delivery (uCD), comprising: creating or receiving a multi-record training dataset, wherein a record comprises: at least one fetal biometric parameter of a sample fetus obtained by an ultrasonography device, at least one personal parameter of a sample mother of the sample fetus, and a ground truth indicating whether a birth of the sample fetus by the sample mother was a uCD, and training the machine learning model on the multi-record training dataset for generating an outcome indicating likelihood of uCD for a target mother in response to an input of at least one fetal biometric parameter of a target fetus of the target mother and at least one personal parameter of the target mother.