Predicting pregnancy-related pelvic girdle pain using machine learning

This study highlighted the potential of ML models to enhance the ability to predict PPGP and offers a more accurate and comprehensive approach to identifying women at risk of PPGP. The integration of ML techniques into clinical practice could improve early identification and inform preventative and intervention strategies, potentially reducing the impact of PPGP on pregnant women.

Authors

Atefe Ashrafi
Hadi Akbarzadeh Khorshidi (ORCID: https://orcid.org/0000-0002-2653-4102)
Dragana Ceprnja (ORCID: https://orcid.org/0000-0002-1955-9096)
Amitabh Gupta (ORCID: https://orcid.org/0000-0002-0999-4078)
Darren Beales (ORCID: https://orcid.org/0000-0002-7176-4644)
Daniel Thomson
Amir Marashi

Institutions

The University of Sydney (AU)
The University of Melbourne (AU)
Curtin University (AU)
Government of Western Australia Department of Health (AU)
Westmead Hospital (AU)
Western Sydney University (AU)

Publication Details

Journal: Musculoskeletal Science and Practice
Published: 2025-03-26
DOI: https://doi.org/10.1016/j.msksp.2025.103321
Citations: 2
Primary Topic: Pregnancy-related medical research
Type: article
Field-Weighted Citation Impact: 3.76

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article

Predicting pregnancy-related pelvic girdle pain using machine learning

Atefe Ashrafi, Hadi Akbarzadeh Khorshidi, Dragana Ceprnja, Amitabh Gupta et al.

Mar 26, 2025

2 citations

Musculoskeletal Science and Practice

Pregnancy-related medical research

3.76

article

Predicting pregnancy-related pelvic girdle pain using machine learning

Atefe Ashrafi, Hadi Akbarzadeh Khorshidi, Dragana Ceprnja, Amitabh Gupta, Darren Beales, Daniel Thomson, Amir Marashi

March 26, 2025article en

2 citations

Abstract

This study highlighted the potential of ML models to enhance the ability to predict PPGP and offers a more accurate and comprehensive approach to identifying women at risk of PPGP. The integration of ML techniques into clinical practice could improve early identification and inform preventative and intervention strategies, potentially reducing the impact of PPGP on pregnant women.

Musculoskeletal Science and PracticeVol. 77

The University of Sydney (AU), The University of Melbourne (AU), Curtin University (AU), Government of Western Australia Department of Health (AU), Westmead Hospital (AU), Western Sydney University (AU)

Openalex Percentile: Top 9%

Pregnancy-related medical research

3.76

DOI Open Access PDF OpenAlex

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Predicting pregnancy-related pelvic girdle pain using machine learning — Atefe Ashrafi, Hadi Akbarzadeh Khorshidi, et al. · Musculoskeletal Science and Practice (2025) | TGRS Research Map | TGRS