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Computer vision (CV) shows increasing promise as an efficient, low‐cost tool for video seizure detection and classification. Here, we provide an overview of the fundamental concepts needed to understand CV and summarize the structure and performance of various model architectures used in video seizure analysis. We conduct a systematic literature review of the PubMed, Embase, and Web of Science databases from January 1, 2000 to September 15, 2023, to identify the strengths and limitations of CV seizure analysis methods and discuss the utility of these models when applied to different clinical seizure phenotypes. Reviews, nonhuman studies, and those with insufficient or poor quality data are excluded from the review. Of the 1942 records identified, 45 meet inclusion criteria and are analyzed. We conclude that the field has shown tremendous growth over the past 2 decades, leading to several model architectures with impressive accuracy and efficiency. The rapid and scalable detection offered by CV models holds the potential to reduce sudden unexpected death in epilepsy and help alleviate resource limitations in epilepsy monitoring units. However, a lack of standardized, thorough validation measures and concerns about patient privacy remain important obstacles for widespread acceptance and adoption. Investigation into the performance of models across varied datasets from clinical and nonclinical environments is an essential area for further research. [ABSTRACT FROM AUTHOR]

Trigeminal neuralgia is characterized classically by recurrent, evocable, unilateral brief, electric, shocklike pains with an abrupt onset and cessation that affects one or more divisions of the trigeminal nerve. In recent years, the classification of trigeminal neuralgia has been updated based on further understanding. In this manuscript, the authors aim to explain the current understanding of the pathophysiology of trigeminal neuralgia, current diagnosis criteria, and the pharmacologic management and surgical treatments of options currently available., (Published by Elsevier Inc.)

There have been conflicting data regarding liver transplantation (LT) outcomes for hereditary hemochromatosis (HH), with no recent data on LT outcomes in patients with HH in the past decade. Using the United Network for Organ Sharing registry, we evaluated waitlist and post-LT survival in all adult patients listed for HH without concomitant liver disease from 2003 to 2019. Post-LT survival for HH was compared with a propensity-matched (recipient and donor factors) cohort of recipients with chronic liver disease (CLD). From 2003 to 2019, 862 patients with HH were listed for LT, of which 55.6% ( n = 479) patients underwent LT. The 1- and 5-year post-LT survival rates in patients with HH were 88.7% (95% confidence interval [CI], 85.4%-91.4%) and 77.5% (95% CI, 72.8%-81.4%), respectively, and were comparable with those in the propensity-matched CLD cohort ( p value = 0.96). Post-LT survival for HH was lower than for Wilson's disease, another hereditary metabolic liver disease with similar LT volume ( n = 365). Predictors for long-term (5-year) post-LT mortality included presence of portal vein thrombosis (hazard ratio [HR], 1.96; 95% CI, 1.07-3.58), obesity measurements greater than Class II (HR, 1.98; 95% CI, 1.16-3.39), and Karnofsky performance status (HR, 0.98; 95% CI, 0.97-0.99) at the time of LT. The leading cause of post-LT death ( n = 145) was malignancy (25.5%), whereas cardiac disease was the cause in less than 10% of recipients. In conclusion, short- and long-term survival rates for HH are excellent and comparable with those of other LT recipients. Improving extrahepatic metabolic factors and functional status in patients with HH prior to LT may improve outcomes., (Copyright © 2022 American Association for the Study of Liver Diseases.)