Dataset Information

Comprehensive miscarriage dataset for an early miscarriage prediction.

ABSTRACT: We present risk factors for predicting miscarriage. Our data is created through an android mobile application that collects automatically real-time data about the pregnant woman. This process is done every 60 s while the mobile application is on active mode. We distinguish two types of data: data from mobile phone and data from healthcare sensors. Data generated is real and concerns real pregnant women to test and validate the proposed system and assess its performance and effectiveness.

SUBMITTER: Asri H

PROVIDER: S-EPMC5992995 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comprehensive miscarriage dataset for an early miscarriage prediction.

Asri Hiba H Mousannif Hajar H Al Moatassime Hassan H

Data in brief 20180517

We present risk factors for predicting miscarriage. Our data is created through an android mobile application that collects automatically real-time data about the pregnant woman. This process is done every 60 s while the mobile application is on active mode. We distinguish two types of data: data from mobile phone and data from healthcare sensors. Data generated is real and concerns real pregnant women to test and validate the proposed system and assess its performance and effectiveness. ...[more]

PMID: 29892640

Similar Datasets

Project description:BackgroundIt is challenging to predict the outcome of the pregnancy when fetal heart activity is detected in early pregnancy. However, an accurate prediction is of importance for obstetricians as it helps to provide appropriate consultancy and determine the frequency of ultrasound examinations. The purpose of this study was to investigate the role of the convolutional neural network (CNN) in the prediction of spontaneous miscarriage risk through the analysis of early ultrasound gestational sac images.MethodsA total of 2196 ultrasound images from 1098 women with early singleton pregnancies of gestational age between 6 and 8 weeks were used for training a CNN for the prediction of the miscarriage in the retrospective study. The patients who had positive fetal cardiac activity on their first ultrasound but then experienced a miscarriage were enrolled. The control group was randomly selected in the same database from the fetuses confirmed to be normal during follow-up. Diagnostic performance of the algorithm was validated and tested in two separate test sets of 136 patients with 272 images, respectively. Performance in prediction of the miscarriage was compared between the CNN and the manual measurement of ultrasound characteristics in the prospective study.ResultsThe accuracy of the predictive model was 80.32% and 78.1% in the retrospective and prospective study, respectively. The area under the receiver operating characteristic curve (AUC) for classification was 0.857 (95% confidence interval [CI], 0.793-0.922) in the retrospective study and 0.885 (95%CI, 0.846-0.925) in the prospective study, respectively. Correspondingly, the predictive power of the CNN was higher compared with manual ultrasound characteristics, for which the AUCs of the crown-rump length combined with fetal heart rate was 0.687 (95%CI, 0.587-0.775).ConclusionsThe CNN model showed high accuracy for predicting miscarriage through the analysis of early pregnancy ultrasound images and achieved better performance than that of manual measurement.

Dataset Information

Comprehensive miscarriage dataset for an early miscarriage prediction.

Publications

Comprehensive miscarriage dataset for an early miscarriage prediction.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets