查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news originating from Konya, Turkey, by News Rx correspondents, research stated, “Varicocoele is a correctable cause of male infertility. Although physical examination is still being used in diagnosis and grading, it gives conflicting results when compared to ultrasonography-based var icocoele grading.” Our news journalists obtained a quote from the research, “We aimed to develop a multi-class machine learning model for the grading of varicocoeles based on ultr asonographic measurements. Between January and May 2024, we enrolled unilateral varicocoele patients at an infertility clinic, assessing their varicocoele stage s using the Dubin and Amelar system. We measured vascular diameter and reflux ti me at the testicular apex and the subinguinal region ultrasonography in both the supine and standing positions. Using these measurements, we developed four mult i-class machine learning models, evaluating their performance metrics and determ ining which patient position and projection were most influential in varicocoele grading. We included 248 patients with unilateral varicocoele in the study, the ir average age was 26.61 ± 4.95 years old. Of these, 212 had left-sided and 36 h ad right-sided varicocoeles. According to the Dubin and Amelar system, there wer e 66 grade I, 96 grade II, and 86 grade III varicocoeles. Among the models we cr eated, the random forest (RF) model performed best, with an overall accuracy of 0.81 ± 0.06, an F1 score of 0.79 ± 0.02, a sensitivity of 0.69 ± 0.02, and a spe cificity of 0.8 ± 0.03. Vascular diameter measurement at the testicular apex in the supine position had the most impact on grading across all models. In support vector machine and multi-layer perceptron models, reflux time measurements from the subinguinal projection in the standing position contributed the most, while in RF and k-nearest neighbors models, measurements from the subinguinal project ion in the supine position were the most influential. Machine learning methods h ave demonstrated superior accuracy in predicting disease compared to traditional statistical regressions and nomograms. These advancements hold promise for clin ically automated prediction of varicocoele grades in patients.”
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