Artificial intelligence fully automated myocardial strain quantification for risk stratification following acute myocardial infarction View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-07-18

AUTHORS

Sören J. Backhaus, Haneen Aldehayat, Johannes T. Kowallick, Ruben Evertz, Torben Lange, Shelby Kutty, Boris Bigalke, Matthias Gutberlet, Gerd Hasenfuß, Holger Thiele, Thomas Stiermaier, Ingo Eitel, Andreas Schuster

ABSTRACT

Feasibility of automated volume-derived cardiac functional evaluation has successfully been demonstrated using cardiovascular magnetic resonance (CMR) imaging. Notwithstanding, strain assessment has proven incremental value for cardiovascular risk stratification. Since introduction of deformation imaging to clinical practice has been complicated by time-consuming post-processing, we sought to investigate automation respectively. CMR data (n = 1095 patients) from two prospectively recruited acute myocardial infarction (AMI) populations with ST-elevation (STEMI) (AIDA STEMI n = 759) and non-STEMI (TATORT-NSTEMI n = 336) were analysed fully automated and manually on conventional cine sequences. LV function assessment included global longitudinal, circumferential, and radial strains (GLS/GCS/GRS). Agreements were assessed between automated and manual strain assessments. The former were assessed for major adverse cardiac event (MACE) prediction within 12 months following AMI. Manually and automated derived GLS showed the best and excellent agreement with an intraclass correlation coefficient (ICC) of 0.81. Agreement was good for GCS and poor for GRS. Amongst automated analyses, GLS (HR 1.12, 95% CI 1.08–1.16, p < 0.001) and GCS (HR 1.07, 95% CI 1.05–1.10, p < 0.001) best predicted MACE with similar diagnostic accuracy compared to manual analyses; area under the curve (AUC) for GLS (auto 0.691 vs. manual 0.693, p = 0.801) and GCS (auto 0.668 vs. manual 0.686, p = 0.425). Amongst automated functional analyses, GLS was the only independent predictor of MACE in multivariate analyses (HR 1.10, 95% CI 1.04–1.15, p < 0.001). Considering high agreement of automated GLS and equally high accuracy for risk prediction compared to the reference standard of manual analyses, automation may improve efficiency and aid in clinical routine implementation.Trial registration: ClinicalTrials.gov, NCT00712101 and NCT01612312. More... »

PAGES

12220

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-022-16228-w

DOI

http://dx.doi.org/10.1038/s41598-022-16228-w

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1149539244

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/35851282


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20 schema:description Feasibility of automated volume-derived cardiac functional evaluation has successfully been demonstrated using cardiovascular magnetic resonance (CMR) imaging. Notwithstanding, strain assessment has proven incremental value for cardiovascular risk stratification. Since introduction of deformation imaging to clinical practice has been complicated by time-consuming post-processing, we sought to investigate automation respectively. CMR data (n = 1095 patients) from two prospectively recruited acute myocardial infarction (AMI) populations with ST-elevation (STEMI) (AIDA STEMI n = 759) and non-STEMI (TATORT-NSTEMI n = 336) were analysed fully automated and manually on conventional cine sequences. LV function assessment included global longitudinal, circumferential, and radial strains (GLS/GCS/GRS). Agreements were assessed between automated and manual strain assessments. The former were assessed for major adverse cardiac event (MACE) prediction within 12 months following AMI. Manually and automated derived GLS showed the best and excellent agreement with an intraclass correlation coefficient (ICC) of 0.81. Agreement was good for GCS and poor for GRS. Amongst automated analyses, GLS (HR 1.12, 95% CI 1.08–1.16, p < 0.001) and GCS (HR 1.07, 95% CI 1.05–1.10, p < 0.001) best predicted MACE with similar diagnostic accuracy compared to manual analyses; area under the curve (AUC) for GLS (auto 0.691 vs. manual 0.693, p = 0.801) and GCS (auto 0.668 vs. manual 0.686, p = 0.425). Amongst automated functional analyses, GLS was the only independent predictor of MACE in multivariate analyses (HR 1.10, 95% CI 1.04–1.15, p < 0.001). Considering high agreement of automated GLS and equally high accuracy for risk prediction compared to the reference standard of manual analyses, automation may improve efficiency and aid in clinical routine implementation.Trial registration: ClinicalTrials.gov, NCT00712101 and NCT01612312.
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26 schema:keywords AMI
27 Amongst
28 CMR data
29 GCS
30 GLS
31 GRS
32 LV function assessment
33 MACE
34 ST elevation
35 accuracy
36 acute myocardial infarction
37 acute myocardial infarction population
38 agreement
39 aid
40 analysis
41 area
42 artificial intelligence
43 assessment
44 automation
45 cardiac functional evaluation
46 cardiovascular magnetic resonance imaging
47 cardiovascular risk stratification
48 cine sequences
49 clinical practice
50 coefficient
51 correlation coefficient
52 curves
53 data
54 deformation
55 diagnostic accuracy
56 efficiency
57 evaluation
58 event prediction
59 excellent agreement
60 feasibility
61 function assessment
62 functional analysis
63 functional evaluation
64 high accuracy
65 high agreement
66 imaging
67 implementation
68 incremental value
69 independent predictors
70 infarction
71 intelligence
72 intraclass correlation coefficient
73 introduction
74 introduction of deformation
75 magnetic resonance imaging
76 manual analysis
77 months
78 multivariate analysis
79 myocardial infarction
80 myocardial infarction population
81 only independent predictor
82 population
83 practice
84 prediction
85 predictors
86 quantification
87 radial strain
88 reference standard
89 resonance imaging
90 risk prediction
91 risk stratification
92 routine implementation
93 sequence
94 similar diagnostic accuracy
95 standards
96 strain assessment
97 strain quantification
98 strains
99 stratification
100 values
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