FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4309659864> ?p ?o ?g. }

• W4309659864 endingPage "133" @default.
• W4309659864 startingPage "124" @default.
• W4309659864 abstract "Cardiac magnetic resonance (CMR) with late gadolinium enhancement (LGE) is a key diagnostic tool in the differential diagnosis between non-ischemic cause of cardiac chest pain. Some patients are not eligible for a gadolinium contrast-enhanced CMR; in this scenario, the diagnosis remains challenging without invasive examination. Our purpose was to derive a machine learning model integrating some non-contrast CMR parameters and demographic factors to identify Takotsubo cardiomyopathy (TTC) in subjects with cardiac chest pain.Three groups of patients were retrospectively studied: TTC, acute myocarditis, and healthy controls. Global and regional left ventricular longitudinal, circumferential, and radial strain (RS) analysis included were assessed. Reservoir, conduit, and booster bi-atrial functions were evaluated by tissue-tracking. Parametric mapping values were also assessed in all the patients. Five different tree-based ensemble learning algorithms were tested concerning their ability in recognizing TTC in a fully cross-validated framework.The CMR-based machine learning (ML) ensemble model, by using the Extremely Randomized Trees algorithm with Elastic Net feature selection, showed a sensitivity of 92% (95% CI 78-100), specificity of 86% (95% CI 80-92) and area under the ROC of 0.94 (95% CI 0.90-0.99) in diagnosing TTC. Among non-contrast CMR parameters, the Shapley additive explanations analysis revealed that left atrial (LA) strain and strain rate were the top imaging markers in identifying TTC patients.Our study demonstrated that using a tree-based ensemble learning algorithm on non-contrast CMR parameters and demographic factors enables the identification of subjects with TTC with good diagnostic accuracy.Our results suggest that non-contrast CMR features can be implemented in a ML model to accurately identify TTC subjects. This model could be a valuable tool for aiding in the diagnosis of subjects with a contraindication to the contrast media. Furthermore, the left atrial conduit strain and strain rate were imaging markers that had a strong impact on TTC identification. Further prospective and longitudinal studies are needed to validate these findings and assess predictive performance in different cohorts, such as those with different ethnicities, and social backgrounds and undergoing different treatments." @default.
• W4309659864 created "2022-11-29" @default.
• W4309659864 creator A5001147278 @default.
• W4309659864 creator A5002084488 @default.
• W4309659864 creator A5003630060 @default.
• W4309659864 creator A5005964807 @default.
• W4309659864 creator A5007285074 @default.
• W4309659864 creator A5007797514 @default.
• W4309659864 creator A5022683161 @default.
• W4309659864 creator A5036247615 @default.
• W4309659864 creator A5060105805 @default.
• W4309659864 creator A5065064713 @default.
• W4309659864 creator A5071959897 @default.
• W4309659864 creator A5075403110 @default.
• W4309659864 date "2023-02-01" @default.
• W4309659864 modified "2023-09-30" @default.
• W4309659864 title "Machine learning approach in diagnosing Takotsubo cardiomyopathy: The role of the combined evaluation of atrial and ventricular strain, and parametric mapping" @default.
• W4309659864 cites W1784250842 @default.
• W4309659864 cites W2000050343 @default.
• W4309659864 cites W2027902935 @default.
• W4309659864 cites W2056132907 @default.
• W4309659864 cites W2092022758 @default.
• W4309659864 cites W2122825543 @default.
• W4309659864 cites W2126116248 @default.
• W4309659864 cites W2140921081 @default.
• W4309659864 cites W2328176404 @default.
• W4309659864 cites W2582556218 @default.
• W4309659864 cites W2585879775 @default.
• W4309659864 cites W2623800443 @default.
• W4309659864 cites W2625704543 @default.
• W4309659864 cites W2765719257 @default.
• W4309659864 cites W2773944606 @default.
• W4309659864 cites W2792832083 @default.
• W4309659864 cites W2801582053 @default.
• W4309659864 cites W2805683701 @default.
• W4309659864 cites W2806923528 @default.
• W4309659864 cites W2909191738 @default.
• W4309659864 cites W2911964244 @default.
• W4309659864 cites W2913742048 @default.
• W4309659864 cites W2972019069 @default.
• W4309659864 cites W3123983075 @default.
• W4309659864 cites W3138174714 @default.
• W4309659864 cites W3162195278 @default.
• W4309659864 cites W3163085312 @default.
• W4309659864 cites W4200421775 @default.
• W4309659864 cites W4210671637 @default.
• W4309659864 cites W4212883601 @default.
• W4309659864 cites W4213191760 @default.
• W4309659864 cites W4213425264 @default.
• W4309659864 cites W4225776358 @default.
• W4309659864 cites W4283262735 @default.
• W4309659864 cites W4288063454 @default.
• W4309659864 cites W4302028720 @default.
• W4309659864 doi "https://doi.org/10.1016/j.ijcard.2022.11.021" @default.
• W4309659864 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36410545" @default.
• W4309659864 hasPublicationYear "2023" @default.
• W4309659864 type Work @default.
• W4309659864 citedByCount "7" @default.
• W4309659864 countsByYear W43096598642023 @default.
• W4309659864 crossrefType "journal-article" @default.
• W4309659864 hasAuthorship W4309659864A5001147278 @default.
• W4309659864 hasAuthorship W4309659864A5002084488 @default.
• W4309659864 hasAuthorship W4309659864A5003630060 @default.
• W4309659864 hasAuthorship W4309659864A5005964807 @default.
• W4309659864 hasAuthorship W4309659864A5007285074 @default.
• W4309659864 hasAuthorship W4309659864A5007797514 @default.
• W4309659864 hasAuthorship W4309659864A5022683161 @default.
• W4309659864 hasAuthorship W4309659864A5036247615 @default.
• W4309659864 hasAuthorship W4309659864A5060105805 @default.
• W4309659864 hasAuthorship W4309659864A5065064713 @default.
• W4309659864 hasAuthorship W4309659864A5071959897 @default.
• W4309659864 hasAuthorship W4309659864A5075403110 @default.
• W4309659864 hasConcept C126322002 @default.
• W4309659864 hasConcept C126838900 @default.
• W4309659864 hasConcept C143409427 @default.
• W4309659864 hasConcept C164705383 @default.
• W4309659864 hasConcept C2776008845 @default.
• W4309659864 hasConcept C2778198053 @default.
• W4309659864 hasConcept C2778797674 @default.
• W4309659864 hasConcept C2780875844 @default.
• W4309659864 hasConcept C2987145844 @default.
• W4309659864 hasConcept C71924100 @default.
• W4309659864 hasConceptScore W4309659864C126322002 @default.
• W4309659864 hasConceptScore W4309659864C126838900 @default.
• W4309659864 hasConceptScore W4309659864C143409427 @default.
• W4309659864 hasConceptScore W4309659864C164705383 @default.
• W4309659864 hasConceptScore W4309659864C2776008845 @default.
• W4309659864 hasConceptScore W4309659864C2778198053 @default.
• W4309659864 hasConceptScore W4309659864C2778797674 @default.
• W4309659864 hasConceptScore W4309659864C2780875844 @default.
• W4309659864 hasConceptScore W4309659864C2987145844 @default.
• W4309659864 hasConceptScore W4309659864C71924100 @default.
• W4309659864 hasLocation W43096598641 @default.
• W4309659864 hasLocation W43096598642 @default.
• W4309659864 hasOpenAccess W4309659864 @default.
• W4309659864 hasPrimaryLocation W43096598641 @default.
• W4309659864 hasRelatedWork W1970337251 @default.
• W4309659864 hasRelatedWork W1978885557 @default.

• next