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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

Interpretable Machine Learning Modeling for Ischemic Stroke Outcome Prediction
ConclusionMachine learning that is applied to quantifiable image features from CT and CTA alongside basic clinical characteristics constitutes a promising automated method in the pre-interventional prediction of stroke prognosis. Interpretable models allow for exploring which initial features contribute the most to post-thrombectomy outcome prediction overall and for each individual patient outcome.
Source: Frontiers in Neurology - May 19, 2022 Category: Neurology Source Type: research

Machine Learning-Based Model for Prediction of Hemorrhage Transformation in Acute Ischemic Stroke After Alteplase
In this study, an RF machine learning method was successfully established to predict HT in AIS patients after intravenous alteplase, which the sensitivity was 66.7%, and the specificity was 80.7%.
Source: Frontiers in Neurology - June 10, 2022 Category: Neurology Source Type: research

Prediction of Clinical Outcomes in Acute Ischaemic Stroke Patients: A Comparative Study
Conclusion: Aggregating clinical and imaging information leads to considerably better outcome prediction models. While lesion-symptom mapping is a useful tool to investigate structure-function relationships of the brain, it does not lead to better outcome predictions compared to a simple data-driven feature selection approach, which is less computationally expensive and easier to implement.
Source: Frontiers in Neurology - May 6, 2021 Category: Neurology Source Type: research

A brain CT-based approach for predicting and analyzing stroke-associated pneumonia from intracerebral hemorrhage
DiscussionOur findings suggest that our method is effective in classifying the development of pneumonia based on brain CT scans. Furthermore, we identified distinct characteristics, such as volume and distribution, of ICH in four different types of SAP.
Source: Frontiers in Neurology - June 2, 2023 Category: Neurology Source Type: research

Beta Amyloid Deposition Is Not Associated With Cognitive Impairment in Parkinson's Disease
In this study, we used a well-validated visual assessment to clinically rate scans as being amyloid positive or negative (38). As there is not an accepted threshold based on standardized centiloid reference regions, we defined an amyloid positivity centiloid cut-off threshold in our sample. Our cut-off (CL = 31.3, SUVR = 1.21) corresponds well to the estimated value proposed by Rowe and colleagues (34) in the context of AD (CL = 25–30), however our estimated threshold may be biased by the low number of Aβ positive patients. Our results suggest a lower prevalence of amyloid-positive PDD individuals than in ...
Source: Frontiers in Neurology - April 23, 2019 Category: Neurology Source Type: research

CT-based thrombus radiomics nomogram for predicting secondary embolization during mechanical thrombectomy for large vessel occlusion
ConclusionThis nomogram could be used to optimize the surgical MT procedure for LVO based on the risk of developing SE.
Source: Frontiers in Neurology - May 12, 2023 Category: Neurology Source Type: research

Predicting DWI-FLAIR mismatch on NCCT: the role of artificial intelligence in hyperacute decision making
ConclusionThe DWI-FLAIR mismatch may be reckoned using NCCT images through advanced artificial intelligence techniques.
Source: Frontiers in Neurology - June 12, 2023 Category: Neurology Source Type: research

Computed tomography angiography-based radiomics model for predicting carotid atherosclerotic plaque vulnerability
This study aimed to identify radiomic features associated with the neovascularization of CAP and construct a prediction model for CAP vulnerability based on radiomic features. CTA data and clinical data of patients with CAPs who underwent CTA and CEUS between January 2018 and December 2021 in Beijing Hospital were retrospectively collected. The data were divided into a training cohort and a testing cohort using a 7:3 split. According to the examination of CEUS, CAPs were dichotomized into vulnerable and stable groups. 3D Slicer software was used to delineate the region of interest in CTA images, and the Pyradiomics package...
Source: Frontiers in Neurology - June 16, 2023 Category: Neurology Source Type: research