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Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records
Conclusion: Electronic administrative records from this cohort produced reliable outcome prediction and identified clinically appropriate factors negatively impacting most outcome variables following hospital admission with stroke. This presents a means of future identification of modifiable factors associated with patient discharge destination. This may potentially aid in patient selection for certain interventions and aid in better patient and clinician education regarding expected discharge outcomes.
Source: Frontiers in Neurology - September 27, 2021 Category: Neurology Source Type: research

A Case of Transient Global Amnesia: A Review and How It May Shed Further Insight into the Neurobiology of Delusions
Conclusion In closing, our patient’s episode of TGA combined with her emotional and perceptual response lends credence to the proposal of a “fear/paranoia” circuit in the genesis of paranoid delusions—a circuit incorporating amygdala, frontal, and parietal cortices. Here, neutral or irrelevant stimuli, thoughts, and percepts come to engender fear and anxiety, while dysfunction in frontoparietal circuitry engenders inappropriate social predictions and maladaptive inferences about the intentions of others.[54] Hippocampus relays information about contextual information based on past experiences and the current situat...
Source: Innovations in Clinical Neuroscience - April 1, 2016 Category: Neuroscience Authors: ICN Online Editor Tags: Anxiety Disorders Behavioral and Cognitive Neurology Case Report Cognition Current Issue Dementia Medical Issues Neurologic Systems and Symptoms Psychiatry Schizophrenia delusions hippocampus neurobiology Transient global amnesia Source Type: research

Automatic Segmentation of the Left Atrium from Computed Tomography Angiography Images
Ann Biomed Eng. 2023 Mar 8. doi: 10.1007/s10439-023-03170-9. Online ahead of print.ABSTRACTThe left atrial appendage (LAA) causes 91% of thrombi in atrial fibrillation patients, a potential harbinger of stroke. Leveraging computed tomography angiography (CTA) images, radiologists interpret the left atrium (LA) and LAA geometries to stratify stroke risk. Nevertheless, accurate LA segmentation remains a time-consuming task with high inter-observer variability. Binary masks of the LA and their corresponding CTA images were used to train and test a 3D U-Net to automate LA segmentation. One model was trained using the entire un...
Source: Annals of Biomedical Engineering - March 8, 2023 Category: Biomedical Engineering Authors: Amaan Kazi Sage Betko Anish Salvi Prahlad G Menon Source Type: research

Deep-Learning for Epicardial Adipose Tissue Assessment With Computed  Tomography: Implications for Cardiovascular Risk Prediction
CONCLUSIONS: Automated assessment of EAT volume is possible in CCTA, including in patients who are technically challenging; it forms a powerful marker of metabolically unhealthy visceral obesity, which could be used for cardiovascular risk stratification.PMID:36881425 | DOI:10.1016/j.jcmg.2022.11.018
Source: Atherosclerosis - March 7, 2023 Category: Cardiology Authors: Henry W West Muhammad Siddique Michelle C Williams Lucrezia Volpe Ria Desai Maria Lyasheva Sheena Thomas Katerina Dangas Christos P Kotanidis Pete Tomlins Ciara Mahon Attila Kardos David Adlam John Graby Jonathan C L Rodrigues Cheerag Shirodaria John Dean Source Type: research