Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Regression Toward the Mean

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...

You might also read

Related Articles

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

Sort by

Same author

Assessment of pregnancy intentions in emergency department patients with pregnancy of unknown location.

BMJ sexual & reproductive health·2026

Same author

Generalisability of Maternal Genetic Risk Score for Birth Weight Across Racial Identity and Ancestry: A Secondary Analysis of a Prospective Cohort Study.

BJOG : an international journal of obstetrics and gynaecology·2025

Same author

Inherited genetic risk in stillbirth: A shared genomic segments analysis of high-risk pedigrees.

HGG advances·2025

Same author

Genetic associations with placental and pregnancy proteins in maternal serum identify biomarkers for hypertension in pregnancy.

American journal of obstetrics and gynecology·2025

Same author

Predicting Perinatal Morbidity in Fetal Growth Restriction: Evidence, Challenges, and Opportunities.

Clinical obstetrics and gynecology·2025

Same author

Use of a Maternal Genetic Risk Score to Derive and Test a Genetically Customized Fetal Growth Curve.

O&G open·2025

Search research articles

Related Experiment Video

Updated: Feb 20, 2026

The Perinatal Asphyxiated Lamb Model: A Model for Newborn Resuscitation

The Perinatal Asphyxiated Lamb Model: A Model for Newborn Resuscitation

Published on: August 15, 2018

Stillbirth Prediction: Current Approaches, Challenges, and Future Directions.

Alexis A Doyle¹, Nathan R Blue²

¹Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, University of Utah Health, Salt Lake City, UT, USA; Intermountain Health, Maternal Fetal Medicine. Salt Lake City, UT, USA.

Clinics in Perinatology

|February 18, 2026

Summary

Predicting stillbirth is challenging due to limited data and traditional models. Novel omics and AI approaches show promise for better prediction, but require further clinical validation.

Keywords:

Artificial intelligence Biomarkers Genomics Machine learning Predictive modeling Risk stratification Stillbirth

More Related Videos

Model Surgical Training: Skills Acquisition in Fetoscopic Laser Photocoagulation of Monochorionic Diamniotic Twin Placenta Using Realistic Simulators

Model Surgical Training: Skills Acquisition in Fetoscopic Laser Photocoagulation of Monochorionic Diamniotic Twin Placenta Using Realistic Simulators

Published on: March 21, 2018

Instrumentation of Near-term Fetal Sheep for Multivariate Chronic Non-anesthetized Recordings

Instrumentation of Near-term Fetal Sheep for Multivariate Chronic Non-anesthetized Recordings

Published on: October 25, 2015

Related Experiment Videos

Last Updated: Feb 20, 2026

The Perinatal Asphyxiated Lamb Model: A Model for Newborn Resuscitation

The Perinatal Asphyxiated Lamb Model: A Model for Newborn Resuscitation

Published on: August 15, 2018

Model Surgical Training: Skills Acquisition in Fetoscopic Laser Photocoagulation of Monochorionic Diamniotic Twin Placenta Using Realistic Simulators

Model Surgical Training: Skills Acquisition in Fetoscopic Laser Photocoagulation of Monochorionic Diamniotic Twin Placenta Using Realistic Simulators

Published on: March 21, 2018

Instrumentation of Near-term Fetal Sheep for Multivariate Chronic Non-anesthetized Recordings

Instrumentation of Near-term Fetal Sheep for Multivariate Chronic Non-anesthetized Recordings

Published on: October 25, 2015

Area of Science:

Perinatal medicine
Computational biology
Reproductive health

Background:

Current stillbirth prediction models lack clinical detail and cannot assess both stillbirth and postnatal mortality risks.
External validation of risk stratification models across diverse populations is difficult.
Existing methods are limited by traditional regression-based modeling.

Purpose of the Study:

To explore novel technologies for improved stillbirth prediction.
To address limitations of current stillbirth prediction methods.
To enhance mechanistic understanding and clinical prognostication for stillbirth.

Main Methods:

Review of current stillbirth prediction limitations.
Exploration of omics-based technologies.
Investigation of artificial intelligence (AI)-based approaches.

Main Results:

Omics and AI technologies offer potential improvements over traditional methods.
Novel approaches may enhance mechanistic understanding of stillbirth.
Novel approaches may improve clinical prognostication for stillbirth.

Conclusions:

Omics-based and AI-based approaches show promise for advancing stillbirth prediction.
Further external validation and clinical studies are essential before implementing novel approaches.
Addressing data granularity and simultaneous risk assessment are key challenges for future models.