Machine learning insights on optimal timing for re‐suturing postpartum perineal wound dehiscence
Machine learning models can reliably predict re-suturing decisions in perineal wound dehiscence. Wound severity and clinician seniority were the strongest predictors supporting data-driven standardization of perineal wound management. Future work should focus on prospective validation, improving interpretability, and clinical integration.
Authors
- R. McDonald (ORCID: https://orcid.org/0009-0007-4038-1521)
- J. Oliver Daly (ORCID: https://orcid.org/0000-0003-2462-7492)
- Paul Khoo
- Debjyoti Karmakar (ORCID: https://orcid.org/0000-0003-1754-3602)
Institutions
- The University of Melbourne (AU)
- Women's and Children's Hospital (AU)
- Western Health (AU)
- Peninsula Health (AU)
- Monash Health (AU)
Publication Details
- Journal
- International Journal of Gynecology & Obstetrics
- Published
- 2025-06-28
- DOI
- https://doi.org/10.1002/ijgo.70339
- Primary Topic
- Pelvic floor disorders treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00