Seminars in Neurology最新文献

Cerebrovascular imaging has undergone significant advances, enhancing the diagnosis and management of cerebrovascular diseases such as stroke, aneurysms, and arteriovenous malformations. This chapter explores key imaging modalities, including non-contrast computed tomography, computed tomography angiography, magnetic resonance imaging (MRI), and digital subtraction angiography. Innovations such as high-resolution vessel wall imaging, artificial intelligence (AI)-driven stroke detection, and advanced perfusion imaging have improved diagnostic accuracy and treatment selection. Additionally, novel techniques like 7-T MRI, molecular imaging, and functional ultrasound provide deeper insights into vascular pathology. AI and machine learning applications are revolutionizing automated detection and prognostication, expediting treatment decisions. Challenges remain in standardization, radiation exposure, and accessibility. However, continued technological advances, multimodal imaging integration, and AI-driven automation promise a future of precise, non-invasive cerebrovascular diagnostics, ultimately improving patient outcomes.

Magnetic resonance imaging (MRI) remains an integral diagnostic tool in multiple sclerosis (MS), for both making the initial diagnosis and monitoring for disease relapse and progression. Despite the applied use of MRI according to the revised McDonald's criteria in 2017, there has been persistent low diagnostic specificity, especially as it pertains to differentiating radiologically isolated syndrome from alternative diagnoses that mimic demyelination. This report will provide an overview of recent paraclinical innovations, with a focus on MRI biomarkers and parameters such as the central vein and paramagnetic rim signs. Utilizing these tools in clinical practice will hopefully improve precision in the diagnosis and monitoring of MS and reduce the risk of false-positive diagnoses.

The skull base and cranial nerves are of high neurological interest. Although the anatomy is complex, a clinicoradiologic approach using modern neuroimaging informed by history taking and physical examination can be employed to elucidate many problems in skull base neurology. This review illustrates diagnostic principles and pearls in skull base medicine with illustrative case vignettes.

Neuroimaging plays a key role in the diagnosis of central nervous system (CNS) infections, as well as common infectious mimics. Standard imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), as well as advanced techniques such as vessel wall imaging and MR spectroscopy, are becoming widely used in many areas of the world and are valuable tools to aid neurologists in clinical practice. This review seeks to elucidate patterns of infectious pathogen tropism in the brain and spine, detail key imaging features of specific neuroinfectious pathogens such as patterns of enhancement and formation of mass lesions, and improve understanding of the sequential development of CNS infections and their complications including stroke and hydrocephalus. Here, we focus on a clinically relevant approach, categorizing pathogens in detail based on clinical syndrome and neuroanatomical imaging findings.

Carotid artery stenosis is a major cause of acute ischemic stroke, accounting for approximately 15% of cases. Although optimal medical therapy remains the cornerstone of management, current guidelines recommend consideration of surgical intervention for symptomatic patients with ≥50% stenosis and asymptomatic patients with ≥70% stenosis. Extensive evidence supports carotid endarterectomy (CEA) as the gold standard procedure, whereas transfemoral carotid angioplasty and stenting (TF-CAS) and transcarotid artery revascularization (TCAR) offer safe alternatives for patients with high surgical risk. Emerging data suggest that TCAR provides safety and efficacy profiles comparable to CEA and superior to TF-CAS in select patients. Considering these findings, selecting an appropriate revascularization strategy should rely on a multidisciplinary, individualized risk-benefit assessment. This article aims to provide a comparative review of the latest evidence on clinical indications, surgical techniques, and outcomes for current carotid revascularization strategies.

Stroke is a major global health burden, requiring time-sensitive diagnosis and treatment to improve patient outcomes. This urgency has created a compelling role for artificial intelligence in the stroke imaging workflow to accelerate diagnosis and treatment. Artificial intelligence has demonstrated a significant impact across multiple aspects of stroke care, including automated detection of acute findings, expedited triage and notification of findings, quantitative assessment of infarcts, predictive prognostication of outcomes, as well as acceleration of image acquisition. However, these advances are accompanied by important limitations including introduction of biases and challenges in the real-world clinical integration of such tools. In this review, we examine the current applications of artificial intelligence in stroke imaging and evaluate the limitations and real-world implementation challenges.

Documentation, coding, and billing (claims submission) are foundational to neurologic practice in the United States, enabling accurate reimbursement, effective communication, and data-driven advancements in patient care, research, and education. Neurologists navigate complex regulatory frameworks and evolving payer guidelines, requiring meticulous attention to diagnostic coding, evaluation and management (E/M) services, and procedure-specific requirements. This chapter examines critical aspects of neurologic billing and coding, including ICD-10-CM (International Classification of Diseases, Tenth Revision, Clinical Modification) for diagnostic accuracy, updated E/M guidelines emphasizing medical decision-making and time, and new telemedicine codes. It highlights the best practices for procedure coding and the use of digital health technologies. The challenges posed by prior authorization are explored, alongside potential solutions like artificial intelligence-driven tools and policy reform. By prioritizing precision, compliance, and technological adaptation, neurologists can enhance patient outcomes, support practice sustainability, and contribute to the broader goals of equitable, efficient, and innovative neurologic care.

The practice of neurology is undergoing significant transformation due to evolving economic pressures, workforce shortages, and increasing demand for neurologic care. Lower reimbursements, increasing operating expenses, and complexity of care challenge the sustainability of existing practice models. This study outlines critical considerations for practice and staffing models in neurology, focusing on strategies to optimize access to care, contain costs for patients and the practice, and enhance operational efficiency. Key topics discussed include integrating advanced practice providers, expanding teleneurology and intravisit care, exploring value-based care models, and enhancing workflows via technology to improve patient experience and clinic efficiency. As the field continues to evolve, neurology practices must adopt agile strategies that balance clinical excellence with economic sustainability in order to meet the demands of a challenging healthcare landscape.