Developing a machine learning model for predicting varicocelectomy outcomes: a pilot study
A further debatable issue in the treatment of varicocele is which men would benefit from a varicocelectomy. Despite the increasing interest in Machine Learning (ML) in urology, there have been limited studies on the detection and prediction of varicocelectomy using artificial intelligence. We aim...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MRE Press
2024-12-01
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| Series: | Journal of Men's Health |
| Subjects: | |
| Online Access: | https://oss.jomh.org/files/article/20241230-447/pdf/JOMH2024101201.pdf |
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| Summary: | A further debatable issue in the treatment of varicocele is which men would
benefit from a varicocelectomy. Despite the increasing interest in Machine
Learning (ML) in urology, there have been limited studies on the detection and
prediction of varicocelectomy using artificial intelligence. We aimed to
develop a model to predict the improvement in semen parameters after
varicocelectomy using ML.The data for male patients who had clinical varicocele,
abnormal semen parameters (low sperm concentration, reduced total motile sperm
count, decreased progressive motility, and/or poor sperm morphology) and had
received a varicocelectomy were recorded retrospectively. Demographic,
anthropometric variables, physical examination findings, hematological,
radiological, and semen analysis parameters were evaluated. The patients were
separated into two groups according to the improvement in total motile sperm
count postoperatively as improvement (Group 1) and no improvement (Group 2). The
Extra Trees Classifier, Light Gradient Boosting Machine Classifier, eXtreme
Gradient Boosting Classifier, Logistic Regression, and Random Forest Classifier
techniques were used as ML algorithms.41 males were included in the study. 31
(75.6%) and 10 (24.4%) patients were classified as Group 1 and 2, respectively.
The Extra Trees Classifier algorithm was found to be the best ML technique for
predictions, according to the accuracy rates (92.3%) with an Area Under Curve of
0.92. We have shown for the first time in the literature that basic
laboratory and semen analysis findings can be used to select patients who will
benefit from varicocelectomy with the use of five ML methods. ML models could be
identified as a new prediction tool for selecting the patients who will benefit
from varicocelectomy. More detailed ML studies will be needed a larger number of
patients. |
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| ISSN: | 1875-6867 1875-6859 |