Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

155
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
155
Classification of Illness01:17

Classification of Illness

7.6K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
7.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Parent and professional experiences of a clinical trial of prenatal and postnatal stem cell therapy for severe osteogenesis imperfecta.

European journal of human genetics : EJHG·2026
Same author

Comparative multimodal calibration of patient-specific atrial fibrillation models: Impact of imaging and electrophysiology data on arrhythmogenic substrate identification.

The Journal of physiology·2026
Same author

Metabolic expenditure, neurodevelopment, and weight gain into early childhood after fetal growth restriction.

Scientific reports·2026
Same author

Correction: A systematic review of multimodal machine learning models for heart failure classification and prognosis prediction.

Frontiers in cardiovascular medicine·2026
Same author

Communicating unexpected news to pregnant people living with mental health conditions in fetal medicine (the UNDERSTAND study): Healthcare professionals' perspectives.

PloS one·2026
Same author

Effects of physical activity and diet in pregnancy to prevent gestational diabetes: an individual participant data (IPD) meta-analysis on the differential effects of interventions with economic evaluation.

Health technology assessment (Winchester, England)·2026

Related Experiment Video

Updated: Jul 26, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

Machine learning and disease prediction in obstetrics.

Zara Arain1, Stamatina Iliodromiti2, Gregory Slabaugh3

  • 1Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK.

Current Research in Physiology
|June 16, 2023
PubMed
Summary

Machine learning and artificial intelligence are transforming obstetric care by improving fetal well-being assessments and predicting diseases like pre-eclampsia and preterm birth. These advanced tools enhance patient safety and clinical practice in maternity care.

Keywords:
CardiotocographyEchocardiographyGestational diabetesMachine learningMagnetic resonance imagingObstetricsPre-eclampsiaPreterm birthUltrasound

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

Related Experiment Videos

Last Updated: Jul 26, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

Area of Science:

  • Obstetrics and Gynecology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Machine learning (ML) and artificial intelligence (AI) are increasingly integrated into healthcare.
  • Predictive tools in obstetrics leverage electronic health records, imaging, and digital devices.

Purpose of the Study:

  • To review the latest ML tools and algorithms for obstetric care.
  • To explore AI applications in assessing fetal well-being and diagnosing obstetric conditions.
  • To discuss the role of ML in improving prenatal, intrapartum, and overall maternity care safety.

Main Methods:

  • Review of current literature on ML and AI in obstetrics.
  • Exploration of algorithms for prediction models using diverse data sources.
  • Analysis of intelligent tools for diagnostic imaging and functional assessments (ultrasound, MRI).

Main Results:

  • ML tools show promise in predicting obstetric diseases (gestational diabetes, pre-eclampsia, preterm birth, fetal growth restriction).
  • AI enhances automated diagnostic imaging for fetal anomalies and functional assessments.
  • Intelligent MRI sequencing aids prenatal diagnosis and preterm birth risk reduction.

Conclusions:

  • ML and AI are revolutionizing obstetric and maternity care, enhancing diagnostic accuracy and patient experience.
  • These technologies improve safety standards in intrapartum care and early complication detection.
  • Continued development of AI tools is crucial for advancing patient safety and clinical practice in obstetrics.