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Deep learning has shown significant value in automating radiological diagnostics but can be limited by a lack of generalizability to external datasets. Leveraging the geometric principles of non-Euclidean space, certain geometric deep learning approaches may offer an alternative means of improving model generalizability. This study investigates the potential advantages of hyperbolic convolutional neural networks (HCNNs) over traditional convolutional neural networks (CNNs) in neuroimaging tasks. We conducted a comparative analysis of HCNNs and CNNs across various medical imaging modalities and diseases, with a focus on a compiled multi-modality neuroimaging dataset. The models were assessed for their performance parity, robustness to adversarial attacks, semantic organization of embedding spaces, and generalizability. Zero-shot evaluations were also performed with ischemic stroke non-contrast CT images. HCNNs matched CNNs' performance in less complex settings and demonstrated superior semantic organization and robustness to adversarial attacks. While HCNNs equaled CNNs in out-of-sample datasets identifying Alzheimer's disease, in zero-shot evaluations, HCNNs outperformed CNNs and radiologists. HCNNs deliver enhanced robustness and organization in neuroimaging data. This likely underlies why, while HCNNs perform similarly to CNNs with respect to in-sample tasks, they confer improved generalizability. Nevertheless, HCNNs encounter efficiency and performance challenges with larger, complex datasets. These limitations underline the need for further optimization of HCNN architectures. HCNNs present promising improvements in generalizability and resilience for medical imaging applications, particularly in neuroimaging. Despite facing challenges with larger datasets, HCNNs enhance performance under adversarial conditions and offer better semantic organization, suggesting valuable potential in generalizable deep learning models in medical imaging and neuroimaging diagnostics. [ABSTRACT FROM AUTHOR]

This article discusses the feasibility and utility of monitoring intraoperative visual evoked potentials (VEPs) during endoscopic transsphenoidal surgery. The study reviewed 50 patients undergoing this surgery and found that a robust VEP signal was recorded in 73.3% of eyes. The authors recommend using intravenous anesthesia for reliable signal generation and suggest that VEP monitoring may be most helpful during tumor resection near the optic chiasm. The study concludes that intraoperative VEP monitoring is feasible and safe, with no complications or new visual deficits postoperatively. [Extracted from the article]

This article, titled "A Multicenter Study of Clinical Outcomes and Volumetric Trends in Microprolactinomas," explores the correlation between prolactin levels, tumor volumes, and clinical outcomes in patients with pituitary microadenomas. The study found that there was no significant correlation between prolactin levels and tumor volume at diagnosis. However, patients with higher tumor volumes tended to have higher prolactin levels. The article also discusses the different treatment options for microprolactinomas, including observation, medical therapy, and surgery. It concludes that tumors with higher prolactin levels may be more likely to undergo medical treatment, while tumors with lower levels may be suitable for surveillance as they tend to shrink over time. The article suggests that an elevated baseline PRL/volume ratio may indicate tumor growth that may require intervention. [Extracted from the article]