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Testicular salvage: using machine learning algorithm to develop a predictive model in testicular torsion.

Mithat Ekşi1, Abdullah Hizir Yavuzsan2, İsmail Evren3

  • 1Department of Urology, University of Health Sciences, Istanbul Bakirkoy Dr. Sadi Konuk Training and Research Hospital, Zuhuratbaba Mh. Tevfik Saglam Cd. No:11 Bakirkoy, Istanbul, Turkey. mithat_eksi@hotmail.com.

Pediatric Surgery International
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Summary

A machine learning model significantly outperformed classical statistics in predicting orchiectomy necessity for testicular torsion patients. This offers a more accurate, cost-effective tool for clinical decision-making in urology.

Keywords:
Machine learningOrchidopexyOrchiectomyTesticular salvageTesticular torsion

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Area of Science:

  • Urology
  • Medical Informatics
  • Surgical Prediction Modeling

Background:

  • Testicular torsion is a surgical emergency requiring prompt diagnosis.
  • Predicting the need for orchiectomy (testicular removal) is crucial for patient management.
  • Classical statistical methods have limitations in complex predictive tasks.

Purpose of the Study:

  • To compare the predictive accuracy of a machine learning (ML) model versus a classical statistical model (Cox Regression) for orchiectomy in testicular torsion.
  • To identify key preoperative parameters influencing orchiectomy decisions.

Main Methods:

  • Retrospective review of patients with testicular torsion (2000-2020).
  • Data collection included demographics, clinical findings, and admission details.
  • Development of prediction models using Cox Regression and Random Forest (ML).

Main Results:

  • Orchiectomy was performed in 28.3% of cases.
  • Monocyte count, symptom duration, and prior Doppler ultrasound were significant predictors.
  • Random Forest model achieved higher accuracy (AUC 0.95) than Cox Regression (AUC 0.937).

Conclusions:

  • Machine learning models demonstrate superior performance in predicting orchiectomy for testicular torsion.
  • ML offers a cost-effective and increasingly powerful tool for clinical application in urological emergencies.