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Automated prediction of final infarct volume in patients with large-vessel occlusion acute ischemic stroke
CONCLUSIONS: In a cohort of patients with LVO AIS in whom reperfusion was achieved, determinations of infarct core at presentation by NCHCT-ASPECTS and a machine learning model analyzing CTA source images were equivalent to CTP in predicting FIV. These findings have suggested that the information to accurately predict infarct core in patients with LVO AIS was present in conventional imaging modalities (NCHCT and CTA) and accessible by machine learning methods.PMID:34198252 | DOI:10.3171/2021.4.FOCUS21134
Source: Neurosurgical Focus - July 1, 2021 Category: Neurosurgery Authors: Rania Abdelkhaleq Youngran Kim Swapnil Khose Peter Kan Sergio Salazar-Marioni Luca Giancardo Sunil A Sheth Source Type: research

Novel Approaches to Detection of Cerebral Microbleeds: Single Deep Learning Model to Achieve a Balanced Performance
Cerebral microbleeds (CMBs) are considered essential indicators for the diagnosis of cerebrovascular disease and cognitive disorders. Traditionally, CMBs are manually interpreted based on criteria including the shape, diameter, and signal characteristics after an MR examination, such as susceptibility-weighted imaging or gradient echo imaging (GRE). In this paper, an efficient method for CMB detection in GRE scans is presented.
Source: Journal of Stroke and Cerebrovascular Diseases - June 24, 2021 Category: Neurology Authors: Min Jae Myung, Kyung Mi Lee, Hyug-Gi Kim, Janghoon Oh, Ji Young Lee, Ilah Shin, Eui Jong Kim, Jin San Lee Source Type: research

AI analysis of SPECT MPI scans can predict future cardiac events
The combination of artificial intelligence (AI) and SPECT myocardial perfusio...Read more on AuntMinnie.comRelated Reading: Machine learning plus CT boosts prediction of major coronary events Report charts 80% drop in cardiac SPECT during COVID-19 Machine-learning model predicts adverse cardiac outcomes AI predicts heart attack, stroke on cardiac MRI Deep learning advances SPECT MPI
Source: AuntMinnie.com Headlines - June 12, 2021 Category: Radiology Source Type: news

A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework
This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from “ground truth” images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for c...
Source: Journal of Digital Imaging - June 2, 2021 Category: Radiology Source Type: research

Predictive and diagnosis models of stroke from hemodynamic signal monitoring
AbstractThis work presents a novel and promising approach to the clinical management of acute stroke. Using machine learning techniques, our research has succeeded in developing accurate diagnosis and prediction real-time models from hemodynamic data. These models are able to diagnose stroke subtype with 30 min of monitoring, to predict the exitus during the first 3 h of monitoring, and to predict the stroke recurrence in just 15 min of monitoring. Patients with difficult access to a CT scan and all patients that arrive at the stroke unit of a specialized hospital will benefit from these positive results. The results obtai...
Source: Medical and Biological Engineering and Computing - May 14, 2021 Category: Biomedical Engineering Source Type: research

Rapid Assessment of Acute Ischemic Stroke by Computed Tomography Using Deep Convolutional Neural Networks
This study proposes an automatic identification scheme for acute ischemic stroke using deep convolutional neural networks (DCNNs) based on non-contrast computed tomographic (NCCT) images. Our image database for the classification model was composed of 1254 grayscale NCCT images from 96 patients (573 images) with acute ischemic stroke and 121 normal controls (681 images). According to the consensus of critical stroke findings by two neuroradiologists, a gold standard was established and used to train the proposed DCNN using machine-generated image features. Including the earliest DCNN, AlexNet, the popular Inception-v3, and...
Source: Journal of Digital Imaging - May 7, 2021 Category: Radiology 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

Learning non-local perfusion textures for high-quality computed tomography perfusion imaging
CONCLUSIONS: The presented NPTN method can obtain high-quality CTP images and estimate high-accuracy CBF map by characterizing more structure details and contrast variants in the CTP image, and outperform the competing methods at low-dose cases.PMID:33910178 | DOI:10.1088/1361-6560/abfc90
Source: Physics in Medicine and Biology - April 28, 2021 Category: Physics Authors: Sui Li Dong Zeng Zhaoying Bian Danyang Li Manman Zhu Jing Huang Jianhua Ma Source Type: research

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage
AbstractWe hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This retrospective multicenter cohort study analyzed 520 non-enhanced CT scans and clinical data of patients with acute spontaneous ICH. Clinical outcome at hospital discharge was dichotomized into good outcome and poor outcome using different modified Rankin Scale (mRS) cut-off values. Predictive performance of a random forest machine learning approach based on filter- and texture-derived high-end image features was evaluated for differentiation of functional ...
Source: Translational Stroke Research - February 6, 2021 Category: Neurology Source Type: research

Will MRI replace the EEG for the diagnosis of nonconvulsive status epilepticus, especially focal?
Authors: Gelisse P, Genton P, Crespel A, Lefevre PH Abstract Magnetic resonance imaging (MRI) can now be used to diagnose or to provide confirmation of focal nonconvulsive status epilepticus (NCSE). Approximately half of patients with status epilepticus (SE) have signal changes. MRI can also aid in the differential diagnosis with generalized NCSE when there is a clinical or EEG doubt, e.g. with metabolic/toxic encephalopathies or Creutzfeldt-Jakob disease. With the development of stroke centers, MRI is available 24h/24 in most hospitals. MRI has a higher spatial resolution than electroencephalography (EEG). MRI wit...
Source: Revue Neurologique - January 26, 2021 Category: Neurology Tags: Rev Neurol (Paris) Source Type: research

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