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Objective: To evaluate the current body of literature pertaining to the use of ocular point-of-care ultrasound (POCUS) in the emergency department. Methods: A comprehensive literature search was conducted on SCOPUS, Web of Science, MEDLINE, and Cochrane CENTRAL. Inclusion criteria included studies written in English only and primary clinical studies involving ocular POCUS scans in an emergency department setting. Exclusion criteria included non-primary studies (e.g. reviews or case reports), studies written in a non-English language, non-human studies, studies performed in a non-emergency setting, studies involving non-POCUS ocular ultrasound modalities, or studies published outside of the last decade. Data extraction was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations. Results: The initial search yielded 391 results with 153 duplicates. Of the remaining 238 studies selected for retrieval and screening, 24 met inclusion criteria. These 24 included studies encompassed 2448 patients across prospective, retrospective, cross sectional, and case series study designs. We found that a majority of included studies focus on the use of POCUS in the emergency department to measure ONSD as a proxy for papilledema and metabolic aberrations, while a minority use ocular POCUS to assist in the diagnosis of orbital fractures or posterior segment pathology. Conclusion: The vast majority of articles investigating the use of ocular POCUS in recent years emphasize its utility in measuring ONSD and fluctuations in intracranial pressure, though additional outcomes of interest include posterior segment, orbit, and globe pathology.
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OBJECTIVES: Accurate differentiation of temporary vs. permanent changes occurring following irreversible electroporation (IRE) holds immense importance for the early assessment of ablative treatment outcomes. Here, we investigated the benefits of advanced statistical learning models for an immediate evaluation of therapeutic outcomes by interpreting quantitative characteristics captured with conventional MRI. METHODS: The preclinical study integrated twenty-six rabbits with anatomical and perfusion MRI data acquired with a 3T clinical MRI scanner. T1w and T2w MRI data were quantitatively analyzed, and forty-six quantitative features were computed with four feature extraction methods. The candidate key features were determined by graph clustering following the filtering-based feature selection technique, RELIEFF algorithm. Kernel-based support vector machines (SVM) and random forest (RF) classifiers interpreting quantitative features of T1w, T2w, and combination (T1w+T2w) MRI were developed for replicating the underlying characteristics of the tissues to distinguish IRE ablation regions for immediate assessment of treatment response. Accuracy, sensitivity, specificity, and area under the receiver operating characteristics curve were used to evaluate classification performance. RESULTS: Following the analysis of quantitative variables, three features were integrated to develop a SVM classification model, while five features were utilized for generating RF classifiers. SVM classifiers demonstrated detection accuracy of 91.06%, 96.15%, and 98.04% for individual and combination MRI data, respectively. Besides, RF classifiers obtained slightly lower accuracy compared to SVM which were 95.06%, 89.40%, and 94.38% respectively. CONCLUSIONS: Quantitative models integrating structural characteristics of conventional T1w and T2w MRI data with statistical learning techniques identified IRE ablation regions allowing early assessment of treatment status.