Male infertility remains a significant global health concern, with abnormal sperm head morphology recognized as a key factor impacting fertility. Traditional analysis of sperm morphology through manual microscopy is labor-intensive and susceptible to variability among observers. In this study, we introduce a deep learning framework designed to automate sperm head classification, integrating EdgeSAM for precise segmentation with a Sperm Head Pose Correction Network to standardize orientation and position. The classification network employs flip feature fusion and deformable convolutions to capture symmetrical characteristics, which enhances classification accuracy across morphological variations. Our model achieves a test accuracy of 97.5% on the HuSHem and Chenwy datasets, outperforming existing methods and demonstrating greater robustness to rotational and translational transformations. This approach offers a streamlined, automated solution for sperm morphology analysis, providing a reliable tool to support clinical fertility diagnostics and research applications.

At the core of AI-driven electrocardiogram diagnosis lies the precise localization of the QRS complex. While QRS detection methods for multiple leads have been researched adequately in the last few decades, their multi-lead strategies still need to be designed manually. Therefore, a QRS detector that can fuse multiple leads automatically is still worth investigating. Methods: The proposed QRS detector comprises a leads-distillation module (LDM) and a QRS detection module. The LDM can distill multi-lead signals into single-lead ones. This procedure minimizes the weight proportions assigned to noisy leads, enabling the network to generate a novel signal that facilitates the recognition of QRS waves. The QRS detection module, utilizing U-Net, is capable of discerning QRS complexes from the novel signal. Results: Our method demonstrates outstanding performance with a parameter count of only 5216. It achieves an excellent F1 score of 99.83 on the MITBIHA database and 99.77 on the INCART database, specifically in the inter-patient pattern. In the cross-database pattern, our approach maintains a strong performance with an F1 score of 99.22 on the INCART database and an F1 score of 99.09 on the MITBIHA database. Conclusion: Our method provides a novel idea for universal multi-lead QRS detection. It possesses advantages, such as reduced computational parameters, enhanced precision, and heightened compatibility. Significance: Our method canceled the repeated deployment of the QRS detection function to different lead configurations in the electrocardiogram (ECG) diagnostic system. Moreover, the scaling operation may become a simple tool to decrease the computational load of the network.

The sperm chromatin dispersion (SCD) test is widely used to assess sperm DNA fragmentation by observing the halo structure around the sperm head. However, manual halo interpretation is labor-intensive and subjective, which limits its consistency and throughput in clinical practice. To address these limitations, we propose SHIELD-Net, an image-level single-sperm halo morphology classification framework for four halo categories: large halo, medium halo, small halo, and without halo. SHIELD-Net includes two modules, i.e., a hybrid ROI extraction (HyREx) module and dual-view learning with ROI-guided gate fusion (RGF). The HyREx module converts the traditional direct classification pipeline into a segmentation-classification pipeline. It uses the cell segmentation foundation model, i.e., Cellpose, to segment halo-centered regions of interest (ROIs) and proposes a fallback mechanism to ensure segmentation effectiveness. In this way, HyREx can construct a head–halo-centered ROI and reduce interference from tail and background regions. Based on the original image and the extracted ROI, SHIELD-Net performs dual-view ConvNeXt encoding. RGF further uses the ROI representation to generate a spatial gate that recalibrates the original-view feature map toward halo-morphology-related responses. Experiments were conducted on a public expert-annotated sperm microscopy dataset containing 403 individual sperm halo images using stratified five-fold cross-validation. SHIELD-Net achieved a Macro F1 of 0.9290 ± 0.0161. Compared with the original-image ConvNeXt-Small baseline, SHIELD-Net improved Macro F1 by 0.1755. These results indicate that combining head–halo-centered ROI construction with ROI-guided feature recalibration improves image-level sperm halo morphology classification.

Large language models (LLMs) show clinical potential. We evaluated the performance of LLMs, including ChatGPT, DeepSeek, Gemini, and Grok, in solving early