Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313374059> ?p ?o ?g. }
- W4313374059 endingPage "82" @default.
- W4313374059 startingPage "69" @default.
- W4313374059 abstract "Cardiac imaging is of paramount importance in the diagnosis and management of patients with heart disease. Multiple modalities are encompassed within cardiac imaging, including echocardiography, magnetic resonance imaging (MRI), computed tomography (CT), and nuclear medicine. All of the modalities are primed to utilize artificial intelligence to increase accuracy, efficiency, and discover novel relationships between disease and outcomes. Artificial intelligence in cardiac imaging can improve multiple sections in the imaging process: acquisition, optimization, measurements, interpretation, and decision support. Important strides forward have already been made in each of the modalities; some have shown the ability to automatically diagnose disease, others to improve efficiency of clinical workflow, and still others to predict morbidity. Reproducibility and challenges with deployment remain barriers to widespread use of artificial intelligence in cardiac imaging, but the road ahead shows promise." @default.
- W4313374059 created "2023-01-06" @default.
- W4313374059 creator A5089854102 @default.
- W4313374059 date "2022-01-01" @default.
- W4313374059 modified "2023-10-16" @default.
- W4313374059 title "Big Data and AI in Cardiac Imaging" @default.
- W4313374059 cites W1581517109 @default.
- W4313374059 cites W1708392634 @default.
- W4313374059 cites W1986823634 @default.
- W4313374059 cites W2031917779 @default.
- W4313374059 cites W2063167175 @default.
- W4313374059 cites W2067609993 @default.
- W4313374059 cites W2082704080 @default.
- W4313374059 cites W2087936817 @default.
- W4313374059 cites W2100821852 @default.
- W4313374059 cites W2116948892 @default.
- W4313374059 cites W2121827537 @default.
- W4313374059 cites W2124624517 @default.
- W4313374059 cites W2136544420 @default.
- W4313374059 cites W2141280582 @default.
- W4313374059 cites W2254050631 @default.
- W4313374059 cites W2271526803 @default.
- W4313374059 cites W2274227799 @default.
- W4313374059 cites W2303147257 @default.
- W4313374059 cites W2330578068 @default.
- W4313374059 cites W2345003174 @default.
- W4313374059 cites W2427373474 @default.
- W4313374059 cites W2432056964 @default.
- W4313374059 cites W2581465409 @default.
- W4313374059 cites W2591746411 @default.
- W4313374059 cites W2602944801 @default.
- W4313374059 cites W2606592330 @default.
- W4313374059 cites W2607413588 @default.
- W4313374059 cites W2617669016 @default.
- W4313374059 cites W2735582614 @default.
- W4313374059 cites W2751671543 @default.
- W4313374059 cites W2754679142 @default.
- W4313374059 cites W2756379568 @default.
- W4313374059 cites W2759653730 @default.
- W4313374059 cites W2765669952 @default.
- W4313374059 cites W2787540142 @default.
- W4313374059 cites W2789532252 @default.
- W4313374059 cites W2791504809 @default.
- W4313374059 cites W2799913286 @default.
- W4313374059 cites W2808935392 @default.
- W4313374059 cites W2883475966 @default.
- W4313374059 cites W2888171911 @default.
- W4313374059 cites W2888570268 @default.
- W4313374059 cites W2896287590 @default.
- W4313374059 cites W2897581582 @default.
- W4313374059 cites W2897944447 @default.
- W4313374059 cites W2901903036 @default.
- W4313374059 cites W2907599594 @default.
- W4313374059 cites W2909339748 @default.
- W4313374059 cites W2939219301 @default.
- W4313374059 cites W2956767972 @default.
- W4313374059 cites W2974289453 @default.
- W4313374059 cites W2977559140 @default.
- W4313374059 cites W2980762210 @default.
- W4313374059 cites W2981668949 @default.
- W4313374059 cites W2995383696 @default.
- W4313374059 cites W2999420594 @default.
- W4313374059 cites W3002705197 @default.
- W4313374059 cites W3011008525 @default.
- W4313374059 cites W3011098071 @default.
- W4313374059 cites W3012132823 @default.
- W4313374059 cites W3033480486 @default.
- W4313374059 cites W3035649997 @default.
- W4313374059 cites W3036319923 @default.
- W4313374059 cites W3047451937 @default.
- W4313374059 cites W3105445034 @default.
- W4313374059 cites W3108392885 @default.
- W4313374059 cites W3148807933 @default.
- W4313374059 cites W3161999894 @default.
- W4313374059 cites W3182600368 @default.
- W4313374059 cites W3185175075 @default.
- W4313374059 cites W3215686278 @default.
- W4313374059 cites W3217335348 @default.
- W4313374059 cites W333694449 @default.
- W4313374059 cites W4205716065 @default.
- W4313374059 cites W4292334794 @default.
- W4313374059 doi "https://doi.org/10.1007/978-3-031-11199-0_5" @default.
- W4313374059 hasPublicationYear "2022" @default.
- W4313374059 type Work @default.
- W4313374059 citedByCount "0" @default.
- W4313374059 crossrefType "book-chapter" @default.
- W4313374059 hasAuthorship W4313374059A5089854102 @default.
- W4313374059 hasConcept C126838900 @default.
- W4313374059 hasConcept C143409427 @default.
- W4313374059 hasConcept C144024400 @default.
- W4313374059 hasConcept C154945302 @default.
- W4313374059 hasConcept C177212765 @default.
- W4313374059 hasConcept C19527891 @default.
- W4313374059 hasConcept C2776008845 @default.
- W4313374059 hasConcept C2776127602 @default.
- W4313374059 hasConcept C2779903281 @default.
- W4313374059 hasConcept C31601959 @default.
- W4313374059 hasConcept C36289849 @default.