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

Abstract

To 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. Patients 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. Among 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%. The 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.