Pre- and post-surgery: advancements in artificial intelligence and machine learning models for enhancing patient management in infective endocarditis.

Infective endocarditis (IE) is a severe infection of the inner lining of the heart, known as the endocardium. It is characterized by a range of symptoms and has a complicated pattern of occurrence, leading to a significant number of deaths. IE poses significant diagnostic and treatment difficulties. This evaluation examines the utilization of artificial intelligence (AI) and machine learning (ML) models in addressing information extraction (IE) management. It focuses on the most recent advancements and possible applications. Through this paper, we observe that AI/ML can significantly enhance and outperform traditional diagnostic methods leading to more accurate risk stratification, personalized therapies as well and real-time monitoring facilities. For example, early postsurgical mortality prediction models like SYSUPMIE achieved 'very good' area under the curve (AUROC) values exceeding 0.81. Additionally, AI/ML has improved diagnostic accuracy for prosthetic valve endocarditis, with PET-ML models increasing sensitivity from 59% to 72% when integrated into ESC criteria and reaching a high specificity of 83%. Furthermore, inflammatory biomarkers such as IL-15 and CCL4 have been identified as predictive markers, showing 91% accuracy in forecasting mortality, and identifying high-risk patients with specific CRP, IL-15, and CCL4 levels. Even simpler ML models, like Naïve Bayes, demonstrated an excellent accuracy of 92.30% in death rate prediction following valvular surgery for IE patients. Furthermore, this review provides a vital assessment of the advantages and disadvantages of such AI/ML models, such as better-quality decision support approaches like adaptive response systems on one hand, and data privacy threats or ethical concerns on the other hand. In conclusion, Al and ML must continue, through multi-centric and validated research, to advance cardiovascular medicine, and overcome implementation challenges to boost patient outcomes and healthcare delivery.

Non-Dolichoectatic Vertebral Artery Compression of the Medulla: A Comprehensive Literature Review.

OBJECTIVE: Vertebral artery compression of the medulla is a rare vascular finding that causes a variety of clinical presentations, from asymptomatic to neurological disability. This article presents the largest literature review to date on medullary compression of the vertebral arteries. METHODS: An English literature search was performed using the PubMed database and the keywords vertebral artery tortuosity, vertebral artery compression, and medullary compression. RESULTS: A comprehensive literature search yielded 68 patients (57% male) with medullary compression by an intracranial vertebral artery (ICVA). The left side of the medulla was compressed in 44, the right side in 19, and bilateral in 7. The most common clinical symptom was weakness - 26 patients (36%) - 6 had quadriparesis and 6 had hemiparesis. 21 patients reported imbalance; 12 various sensory symptoms; 4 patients were asymptomatic. CONCLUSIONS: Understanding the anatomy of the vasculature can help mitigate future debilitating stroke symptoms. Concrete guidelines for revascularization surgery in symptomatic patients may also be effective. Future studies are needed to further clarify the prevalence, natural history, vascular etiology, and treatment of this condition, including asymptomatic patients and the likelihood that they will develop further neurological signs and disability.