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Updated: Aug 10, 2025

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A Scoring System Developed by a Machine Learning Algorithm to Better Predict Adnexal Torsion.

Ohad Atia1, Ella Hazan2, Reut Rotem3

  • 1Department of Pediatrics, Shaare Zedek Medical Center, affiliated with the Hebrew University School of Medicine (Dr. Atia), Jerusalem, Israel.

Journal of Minimally Invasive Gynecology
|February 12, 2023
PubMed
Summary

A new prediction score can help diagnose adnexal torsion (AT) in women. Key factors include vomiting, left-sided pain, and pregnancy, improving emergency room assessments for suspected AT.

Keywords:
Adnexal torsionLaparoscopyPredictionScoringUltrasound

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Area of Science:

  • Gynecology
  • Surgical Diagnosis
  • Medical Informatics

Background:

  • Adnexal torsion (AT) is a gynecological emergency requiring prompt diagnosis.
  • Accurate preoperative diagnosis of AT remains challenging, impacting surgical outcomes.
  • Developing a reliable prediction tool is crucial for timely intervention.

Purpose of the Study:

  • To develop and validate a clinically relevant prediction score for diagnosing adnexal torsion (AT).
  • To identify key clinical and sonographic predictors of AT in women undergoing surgery for suspected AT.

Main Methods:

  • Retrospective cohort study of 503 women undergoing urgent laparoscopy for suspected AT.
  • Utilized univariate, multivariate, and Random Forest machine learning models.
  • Developed a predictive score based on significant variables and evaluated its accuracy using ROC curves.

Main Results:

  • Vomiting, left-sided complaints, and concurrent pregnancy were significant predictors of AT.
  • Ovarian edema and decreased vascular flow on ultrasound increased AT risk.
  • The developed predictive score demonstrated an area under the curve of 0.72, with a cutoff >5 yielding 64% sensitivity and 73% specificity.

Conclusions:

  • Clinical and ultrasound findings can enhance emergency room evaluations for suspected AT.
  • The developed predictive score offers a potential tool for improving AT diagnosis.
  • Further prospective studies are recommended to validate the model's accuracy.