FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2075596125> ?p ?o ?g. }

• W2075596125 endingPage "434" @default.
• W2075596125 startingPage "427" @default.
• W2075596125 abstract "In Brief Aim To establish proof of the principle that a computer-based neural network method can be employed that will enhance diagnostic accuracy vis-à-vis image analysis alone in the interpretation of treadmill exercise tests performed in conjunction with myocardial perfusion imaging. Materials and methods One-hundred-and-two patients underwent myocardial perfusion imaging in association with the standard Bruce protocol. Twenty objective parameters describing each patient's exercise physiology, general clinical status and image appearance were used to train an artificial neural network. Classification accuracy of the neural network and clinical interpretation was determined by coronary angiography. We evaluated the ability of the neural network to integrate clinical, exercise and imaging data to determine the likelihood of coronary artery disease and compared these results with an optimized method of clinical image interpretation, which made use of all available clinical, angiographic and stress test data. Results The artificial neural network had a sensitivity of 88% and a specificity of 65% for detection of ischemic heart disease and was comparable to that of the optimized clinical method (sensitivity 80%, specificity 69%). Incorporation of clinical and exercise data significantly improved the predictive accuracy of the network compared to a network based on image data alone (P<0.05). Conclusion The results show a computer-based neural network can perform as well as expert readers working under optimal conditions including full knowledge of the patient's clinical, prior angiographic and stress test data. Thus, the method is promising as a diagnostic aid to the recognition of ischemic heart disease in the clinical setting of treadmill exercise testing in conjunction with myocardial perfusion imaging. A computer-based artificial intelligence neural network was developed to integrate clinical, exercise and imaging data from radionuclide stress testing and thereby obtain an objective, reliable, reproducible method for interpretation of these tests which could be used by anyone with a personal computer. The artificial intelligence method showed a sensitivity of 88% and a specificity of 65% for the diagnosis of ischemic heart disease and was comparable to that of our standard clinical method (sensitivity 80%, specificity 69%), which employs dual expert readers with full access to all relevant clinical information. Incorporation of clinical/exercise data significantly improved predictive accuracy of the network over that of a network constructed using image data alone (Z=2.26, p<05). Finally, the neural network performed as well as the expert readers in the diagnosis of 1, 2 and 3 vessel disease and in identification of the specific coronary vessel(s) involved." @default.
• W2075596125 created "2016-06-24" @default.
• W2075596125 creator A5018160566 @default.
• W2075596125 creator A5060455412 @default.
• W2075596125 creator A5067779324 @default.
• W2075596125 creator A5086683300 @default.
• W2075596125 date "2004-11-01" @default.
• W2075596125 modified "2023-10-02" @default.
• W2075596125 title "Integration of clinical and imaging data to predict the presence of coronary artery disease with the use of neural networks" @default.
• W2075596125 cites W1760932166 @default.
• W2075596125 cites W1965956449 @default.
• W2075596125 cites W2019390202 @default.
• W2075596125 cites W2056023776 @default.
• W2075596125 cites W2056362996 @default.
• W2075596125 cites W2057006281 @default.
• W2075596125 cites W2059117609 @default.
• W2075596125 cites W2115011142 @default.
• W2075596125 cites W2125443492 @default.
• W2075596125 cites W2126124195 @default.
• W2075596125 cites W2133468553 @default.
• W2075596125 cites W2169714732 @default.
• W2075596125 cites W2342311534 @default.
• W2075596125 doi "https://doi.org/10.1097/00019501-200411000-00010" @default.
• W2075596125 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/15492592" @default.
• W2075596125 hasPublicationYear "2004" @default.
• W2075596125 type Work @default.
• W2075596125 sameAs 2075596125 @default.
• W2075596125 citedByCount "16" @default.
• W2075596125 countsByYear W20755961252012 @default.
• W2075596125 countsByYear W20755961252013 @default.
• W2075596125 countsByYear W20755961252019 @default.
• W2075596125 countsByYear W20755961252020 @default.
• W2075596125 countsByYear W20755961252021 @default.
• W2075596125 countsByYear W20755961252022 @default.
• W2075596125 countsByYear W20755961252023 @default.
• W2075596125 crossrefType "journal-article" @default.
• W2075596125 hasAuthorship W2075596125A5018160566 @default.
• W2075596125 hasAuthorship W2075596125A5060455412 @default.
• W2075596125 hasAuthorship W2075596125A5067779324 @default.
• W2075596125 hasAuthorship W2075596125A5086683300 @default.
• W2075596125 hasConcept C126322002 @default.
• W2075596125 hasConcept C126838900 @default.
• W2075596125 hasConcept C154945302 @default.
• W2075596125 hasConcept C164705383 @default.
• W2075596125 hasConcept C2778213512 @default.
• W2075596125 hasConcept C2778405248 @default.
• W2075596125 hasConcept C41008148 @default.
• W2075596125 hasConcept C50644808 @default.
• W2075596125 hasConcept C71924100 @default.
• W2075596125 hasConceptScore W2075596125C126322002 @default.
• W2075596125 hasConceptScore W2075596125C126838900 @default.
• W2075596125 hasConceptScore W2075596125C154945302 @default.
• W2075596125 hasConceptScore W2075596125C164705383 @default.
• W2075596125 hasConceptScore W2075596125C2778213512 @default.
• W2075596125 hasConceptScore W2075596125C2778405248 @default.
• W2075596125 hasConceptScore W2075596125C41008148 @default.
• W2075596125 hasConceptScore W2075596125C50644808 @default.
• W2075596125 hasConceptScore W2075596125C71924100 @default.
• W2075596125 hasIssue "7" @default.
• W2075596125 hasLocation W20755961251 @default.
• W2075596125 hasLocation W20755961252 @default.
• W2075596125 hasOpenAccess W2075596125 @default.
• W2075596125 hasPrimaryLocation W20755961251 @default.
• W2075596125 hasRelatedWork W2042629756 @default.
• W2075596125 hasRelatedWork W2049849716 @default.
• W2075596125 hasRelatedWork W2083924738 @default.
• W2075596125 hasRelatedWork W2248047273 @default.
• W2075596125 hasRelatedWork W2589653403 @default.
• W2075596125 hasRelatedWork W2965039513 @default.
• W2075596125 hasRelatedWork W3043525524 @default.
• W2075596125 hasRelatedWork W3173255959 @default.
• W2075596125 hasRelatedWork W4220660297 @default.
• W2075596125 hasRelatedWork W4244277884 @default.
• W2075596125 hasVolume "15" @default.
• W2075596125 isParatext "false" @default.
• W2075596125 isRetracted "false" @default.
• W2075596125 magId "2075596125" @default.
• W2075596125 workType "article" @default.