Uzun H, et al. Int Neurourol J. 2012 Dec;16(4):181-186. http://dx.doi.org/10.5213/inj.2012.16.4.181

Background and Objective: Diabetes Mellitus is a long-term, multifaceted metabolic condition that necessitates ongoing medical management. Hypogonadism is a syndrome that is a clinical and/or biochemical indicator of testosterone deficiency. Cross-sectional studies have reported that 20–80.4% of all men with Type 2 diabetes have hypogonadism, and Type 2 diabetes is related to low testosterone. This study presents an analysis of the use of ML and EL classifiers in predicting testosterone deficiency. In our study, we compared optimized traditional ML classifiers and three EL classifiers using grid search and stratified k-fold cross-validation. We used the SMOTE method for the class imbalance problem. Methods: This database contains 3397 patients for the assessment of testosterone deficiency. Among these patients, 1886 patients with Type 2 diabetes were included in the study. In the data preprocessing stage, firstly, outlier/excessive observation analyses were performed with LOF and missing value analyses were performed with random forest. The SMOTE is a method for generating synthetic samples of the minority class. Four basic classifiers, namely MLP, RF, ELM and LR, were used as first-level classifiers. Tree ensemble classifiers, namely ADA, XGBoost and SGB, were used as second-level classifiers. Results: After the SMOTE, while the diagnostic accuracy decreased in all base classifiers except ELM, sensitivity values increased in all classifiers. Similarly, while the specificity values decreased in all classifiers, F1 score increased. The RF classifier gave more successful results on the base-training dataset. The most successful ensemble classifier in the training dataset was the ADA classifier in the original data and in the SMOTE data. In terms of the testing data, XGBoost is the most suitable model for your intended use in evaluating model performance. XGBoost, which exhibits a balanced performance especially when the SMOTE is used, can be preferred to correct class imbalance. Conclusions: The SMOTE is used to correct the class imbalance in the original data. However, as seen in this study, when the SMOTE was applied, the diagnostic accuracy decreased in some models but the sensitivity increased significantly. This shows the positive effects of the SMOTE in terms of better predicting the minority class.

Sperm morphology is crucial in semen analysis for diagnosing male infertility. To reduce limitations in visual assessment, such as variability in biological conditions and the biologist’s experience, developing computer-based sperm analysis techniques is imperative. In this study, a total of 49345 RGB sperm morphology patches were obtained using the proposed image acquisition technique and three different Diff-Quick staining methods: BesLab, Histoplus, and GBL. The images were labeled by experts under 18 classes, including sperm head, neck, and tail abnormality types, along with a normal class. The head category includes amorphous, tapered, double, pyriform, pin, vacuolated, narrow acrosome, and round. The neck category encompasses thin, thick, twisted, and asymmetrical. The tail category includes double, curly, long, short, and twisted. The Efficient-V2-Medium achieved accuracy rates of 65.05% and 67.42% on the BesLab and Histoplus datasets, respectively, while the GBL dataset yielded an accuracy of 63.58% using the Efficient-V2-Small. This study experimentally demonstrates that the Histoplus staining method is more suitable for deep learning-based automated analysis systems. As a reference for future studies, 35 different deep learning architectures were trained on the proposed dataset, establishing a classification baseline. The results show that the dataset can be successfully applied to complex deep learning models. Additionally, it addresses the absence of a large-scale sperm morpholgy analysis public datasets and can serve as a standard benchmark for future studies.

Introduction: Fertility is fundamental to human well-being, significantly impacting both individual lives and societal development. In particular, sperm morphology—referring to the shape, size, and structural integrity of sperm cells—is a key indicator in diagnosing male infertility and selecting viable sperm in assisted reproductive technologies such as in vitro fertilisation (IVF) and intracytoplasmic sperm injection (ICSI). However, traditional manual evaluation methods are highly subjective and inconsistent, creating a need for standardized, automated systems. Objectives: This study aims to develop a robust and fully automated sperm morphology classification framework capable of accurately identifying a wide range of morphological abnormalities, thereby minimizing observer variability and improving diagnostic support in reproductive healthcare. Methods: We propose a novel ensemble-based classification approach that combines convolutional neural network (CNN)-derived features using both feature-level and decision-level fusion techniques. Features extracted from multiple EfficientNetV2 variants are fused and classified using Support Vector Machines (SVM), Random Forest (RF), and Multi-Layer Perceptron with Attention (MLP-Attention). Decision-level fusion is achieved via soft voting to enhance robustness and accuracy. Results: The proposed ensemble framework was evaluated using the Hi-LabSpermMorpho dataset, which contains 18 distinct sperm morphology classes. The fusion-based model achieved an accuracy of 67.70%, significantly outperforming individual classifiers. The integration of multiple CNN architectures and ensemble techniques effectively mitigated class imbalance and enhanced the generalizability of the model. Conclusions: The presented methodology demonstrates a substantial improvement over traditional and single-model approaches in automated sperm morphology classification. By leveraging ensemble learning and multi-level fusion, the model provides a reliable and scalable solution for clinical decision-making in male fertility assessment.

Objective.Vascular risk factors frequently coexist with lower urinary tract symptoms (LUTS) in both men and women, suggesting that atherosclerosis and endothelial dysfunction may play a major role in the development of LUTS. Arterial stiffness owing to decreased arterial compliance is one of the major signs of vascular ageing, and carotid intima–media thickness (CIMT) can be used in establishing the extent and severity of vascular impairment. This study investigated the association of arterial stiffness and vascular atherosclerotic involvement with LUTS in men and overactive bladder (OAB) in women. Material and methods.This study was prospective and cross-sectional, consisting of 66 patients with LUTS (38 men with LUTS, 28 women with OAB) and 62 controls without LUTS or OAB. Carotid–femoral pulse-wave velocity (CF-PWV) was measured as an index of arterial stiffness. CIMT was measured with B-mode ultrasonography to assess vascular impairment. Results.Patients with LUTS had significantly higher CF-PWV (9.8 ± 2.6 vs 7.0 ± 1.4 m/s, p < 0.001) and CIMT (0.89 ± 0.16 and 0.56 ± 0.31 mm, p < 0.001) compared with controls. Age and glucose were also different between LUTS and control groups (p < 0.001 and p = 0.003, respectively). In the multivariate analyses, both CIMT ( = 0.429, p < 0.001) and PWV ( = 0.451, p < 0.001) were related to LUTS. Conclusions.Vascular impairment was found to be associated with LUTS in both men and women. Atherosclerosis-related vascular risk factors are also known to be related to LUTS and both disorders may have pathogenic interactions.