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Prediction of Pelvic Organ Prolapse Postsurgical Outcome Using Biomaterial-Induced Blood Cytokine Levels: Machine Learning Approach.

Mihyun Lim WaughNicholas BoltinLauren WolfJane GoodwinPatti ParkerRonnie D HornerMatthew HermesThomas WheelerRichard L GoodwinMelissa A Moss
Published in: JMIR perioperative medicine (2023)
This preliminary study, incorporating a sample size of just 20 participants, identified correlations among cytokines and demonstrated the potential of this novel approach to predict mesh exposure through the vaginal wall following transvaginal POP repair surgery. Further study with a larger sample size will be pursued to confirm these results. If corroborated, this method could provide a personalized medicine approach to assist surgeons in their recommendation of POP repair surgeries with minimal potential for adverse outcomes.
Keyphrases
  • machine learning
  • minimally invasive
  • climate change
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  • high glucose
  • artificial intelligence
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