ArticlePediatric surgery international2022
Testicular salvage: using machine learning algorithm to develop a predictive model in testicular torsion.
Article in Pediatric surgery international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Is It Too Late? Machine Learning for Predicting Orchiectomy Versus Testicular Preservation in Acute Torsion.Diagnostics (Basel, Switzerland) · 2026Article
- Predicting orchiectomy in testicular torsion using hybrid machine learning and explainable AI: a web-based clinical decision support system.World journal of urology · 2026Article
- Machine learning models in predicting viability after testicular torsion: a proof of concept study.Pediatric surgery international · 2026Article
- Long time follow-up for patients with testicular torsion: new findings.American journal of clinical and experimental urology · 2024Article
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Authors and funding
10 authors.
Funding
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Abstract
purposeTo compare the models developed with a classical statistics method and a machine learning model to predict the possibility of orchiectomy using preoperative parameters in patients who were admitted with testicular torsion. MATERIALS AND
methodsPatients who underwent scrotal exploration due to testicular torsion between the years 2000 and 2020 were retrospectively reviewed. Demographic data, features of admission time, and other preoperative clinical findings were recorded. Cox Regression Analysis as a classical statistics method and Random Forest as a Machine Learning algorithm was used to create a prediction model.
resultsAmong patients, 215 (71.6%) were performed orchidopexy and 85 (28.3%) were performed orchiectomy. The multivariate analysis revealed that monocyte count, symptom duration, and the number of previous Doppler ultrasonography were predictive of orchiectomy. Classical Cox regression analysis had an area under the curve (AUC) 0.937 with a sensitivity and specificity of 88 and 87%. The AUC for the Random Forest model was 0.95 with a sensitivity and specificity of 92 and 89%.
conclusionThe ML model outperformed the conventional statistical regression model in the prediction of orchiectomy. The ML methods are cheap, and their powers increase with increasing data input; we believe that their clinical use will increase over time.
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