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Machine Learning Prediction of Incomplete Hysteroscopic Myomectomy Using Preoperative Clinical and Imaging Variables.

Ido Givon1, David Nadav Sabag1, Bar Yacobi2

  • 1Helen Schneider Hospital for Women (Givon, Sabag, Chaim, Bor, Matot, Nassie, and Borovich), Rabin Medical Center, Petach Tikva, Israel; Faculty of Medical and Health Sciences (Givon, Sabag, Chaim, Bor, Matot, Nassie, and Borovich), Tel Aviv University, Tel Aviv, Israel.

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Summary
This summary is machine-generated.

A machine-learning model can predict incomplete hysteroscopic myomectomy using preoperative data. This tool aids in surgical planning and patient counseling for submucosal leiomyomas.

Keywords:
Hysteroscopic myomectomyLeiomyomaMachine learningPredictive modelingUltrasound

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Area of Science:

  • Gynecologic surgery
  • Machine learning in medicine
  • Uterine fibroids management

Background:

  • Submucosal leiomyomas require precise surgical removal.
  • Incomplete hysteroscopic myomectomy can lead to complications and repeat procedures.
  • Predictive tools are needed to optimize surgical outcomes.

Purpose of the Study:

  • Develop and validate a machine-learning (ML) model.
  • Predict incomplete hysteroscopic myomectomy using preoperative data.
  • Integrate clinical, ultrasound, and hysteroscopy findings.

Main Methods:

  • Retrospective cohort study of 328 women.
  • Utilized a CatBoost binary classifier.
  • Trained and validated using 5-fold cross-validation.

Main Results:

  • The ML model achieved an AUROC of 0.72 and average precision of 0.93.
  • Predictors included FIGO type, myoma diameter, and multiplicity.
  • The model outperformed logistic regression in predicting incomplete resection.

Conclusions:

  • A ML model integrating preoperative data accurately predicts incomplete hysteroscopic myomectomy.
  • This approach offers valuable risk estimates for surgical planning.
  • Enhances preoperative counseling for women with submucosal leiomyomas.