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Total 224 results found since Jan 2013.

Validation of a machine learning software tool for automated large vessel occlusion detection in patients with suspected acute stroke
ConclusionThe StrokeSENS LVO machine learning algorithm detects anterior LVO with high accuracy from a range of scans in a large dataset.
Source: Neuroradiology - November 9, 2022 Category: Radiology Source Type: research

EVT, neuroimaging lead to higher stroke care costs
Stroke treatment costs increased 4.9% between 2012 and 2019, driven by endovascula...Read more on AuntMinnie.comRelated Reading: Can machine learning plus CBCT predict blood leaks in stroke patients? How does work compare between academic, private interventionalists? CT shows efficacy of recanalization in stroke patients CT stroke assessment helps determine best treatment AI spots large vessel occlusions, predicts patient outcome
Source: AuntMinnie.com Headlines - November 9, 2022 Category: Radiology Source Type: news

Machine learning based outcome prediction of large vessel occlusion of the anterior circulation prior to thrombectomy in patients with wake-up stroke
CONCLUSION: Machine learning algorithms have the potential to aid in the decision making for thrombectomy in patients with wake-up stroke especially in hospitals, where emergency CTP or MRI imaging is not available.PMID:36344011 | DOI:10.1177/15910199221135695
Source: Interventional Neuroradiology - November 7, 2022 Category: Radiology Authors: Ludger Feyen Christian Blockhaus Marcus Katoh Patrick Haage Christina Schaub Stefan Rohde Source Type: research

Can machine learning plus CBCT predict blood leaks in stroke patients?
Can machine learning used with conebeam CT (CBCT) help clinicians predict whethe...Read more on AuntMinnie.comRelated Reading: CT shows efficacy of recanalization in stroke patients CT stroke assessment helps determine best treatment AI spots large vessel occlusions, predicts patient outcome Is MRI a necessary addition to CT for stroke patients? Body composition analysis shows utility on CT
Source: AuntMinnie.com Headlines - October 31, 2022 Category: Radiology Source Type: news

Brain PET and Cerebrovascular Disease
Cerebrovascular disease encompasses a broad spectrum of diseases such as stroke, hemorrhage, and cognitive decline associated with vascular narrowing, obstruction, rupture, and inflammation, among other issues. Recent advances in hardware and software have led to improvements in brain PET. Although still in its infancy, machine learning using convolutional neural networks is gaining traction in this area, often with a focus on providing high-quality images with reduced noise using a shorter acquisition time or less radiation exposure for the patient.
Source: PET Clinics - October 27, 2022 Category: Radiology Authors: Katarina Chiam, Louis Lee, Phillip H. Kuo, Vincent C. Gaudet, Sandra E. Black, Katherine A. Zukotynski Source Type: research

Can machine learning of post-procedural cone-beam CT images in acute ischemic stroke improve the detection of 24-h hemorrhagic transformation? A preliminary study
ConclusionML demonstrates high-sensitivity but low-accuracy 24-h HT prediction in AIS. The automated CB-CT imaging evaluation resizes sensitivity, specificity, and accuracy rates of visual interpretation reported in the literature so far. A standardized quantitative interpretation of CB-CT may be warranted to overcome the inter-operator variability.
Source: Neuroradiology - October 25, 2022 Category: Radiology Source Type: research

AI spots large vessel occlusions, predicts patient outcome
Artificial intelligence (AI) was able to detect large vessel occlusions (LVO...Read more on AuntMinnie.comRelated Reading: FDA says AI doesn't exclude LVO cases from radiologist review Report: Reimbursement drives adoption of AI software for stroke How can radiologists benefit from AI in 2021? Viz.ai touts research at stroke conference Machine learning helps find vessel occlusions on CTA
Source: AuntMinnie.com Headlines - September 23, 2022 Category: Radiology Source Type: news

Clot-based radiomics model for cardioembolic stroke prediction with CT imaging before recanalization: a multicenter study
ConclusionThe proposed CT-based radiomics model could reliably predict CE stroke in AIS, performing better than the routine radiological method.Key Points• Admission CT imaging could offer valuable information to identify the acute ischemic stroke source by radiomics analysis.• The proposed CT imaging–based radiomics model yielded a higher area under the curve (0.838) than the routine radiological method (0.713; p = 0.007).• Several radiomic features showed significantly stronger correlations with two main thrombus constituents (red blood cells, |rmax|, 0.74; fibrin and platelet, |rmax|, 0.68) than routine radiolog...
Source: European Radiology - September 6, 2022 Category: Radiology Source Type: research

Use of Machine Learning Algorithms to Predict the Outcomes of Mechanical Thrombectomy in Acute Ischemic Stroke Patients With an Extended Therapeutic Time Window
Conclusions Machine learning algorithms may facilitate prediction of 90-day functional outcomes in AIS patients with an extended therapeutic time window.
Source: Journal of Computer Assisted Tomography - September 1, 2022 Category: Radiology Tags: Neuroimaging: Brain Source Type: research

Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks FUNCTIONAL
CONCLUSIONS: An unsupervised generative adversarial network can be used to obtain automated infarct lesion segmentations with a moderate Dice similarity coefficient and good volumetric correspondence.
Source: American Journal of Neuroradiology - August 8, 2022 Category: Radiology Authors: van Voorst, H., Konduri, P. R., van Poppel, L. M., van der Steen, W., van der Sluijs, P. M., Slot, E. M. H., Emmer, B. J., van Zwam, W. H., Roos, Y. B. W. E. M., Majoie, C. B. L. M., Zaharchuk, G., Caan, M. W. A., Marquering, H. A., on behalf of the CONTR Tags: FUNCTIONAL Source Type: research

Improving interobserver agreement and performance of deep learning models for segmenting acute ischemic stroke by combining DWI with optimized ADC thresholds
ConclusionsCombining an ADC threshold of 0.6 × 10−3 mm2/s with DWI reduces interobserver and inter-DLM difference and achieves best segmentation performance of AIS lesions using DLMs.Key Points•Higher Dice similarity coefficient (DSC) in predicting acute ischemic stroke lesions was achieved by ADC thresholds combined with DWI than by DWI alone (all p< .05).•DSC had a negative association with the ADC threshold in most sizes, both hospitals, and both observers (most p< .05) and a positive association with the stroke size in all ADC thresholds, both hospitals, and both observers (all p< .001).•An ADC thresh...
Source: European Radiology - July 14, 2022 Category: Radiology Source Type: research

FReSCO: Flow Reconstruction and Segmentation for low-latency Cardiac Output monitoring using deep artifact suppression and segmentation
CONCLUSIONS: The FReSCO framework was successfully demonstrated for real-time monitoring of CO during exercise and could provide a convenient tool for assessment of the hemodynamic response to a range of stressors.PMID:35781891 | DOI:10.1002/mrm.29374
Source: Magnetic Resonance in Medicine - July 5, 2022 Category: Radiology Authors: Olivier Jaubert Javier Montalt-Tordera James Brown Daniel Knight Simon Arridge Jennifer Steeden Vivek Muthurangu Source Type: research