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Automated In-Line Artificial Intelligence Measured Global Longitudinal Shortening and Mitral Annular Plane Systolic Excursion: Reproducibility and Prognostic Significance.
Xue, Hui; Artico, Jessica; Davies, Rhodri H; Adam, Robert; Shetye, Abhishek; Augusto, João B; Bhuva, Anish; Fröjdh, Fredrika; Wong, Timothy C; Fukui, Miho; Cavalcante, João L; Treibel, Thomas A; Manisty, Charlotte; Fontana, Marianna; Ugander, Martin; Moon, James C; Schelbert, Erik B; Kellman, Peter.
Afiliação
  • Xue H; National Heart, Lung, and Blood InstituteNational Institutes of Health Bethesda MD.
  • Artico J; Barts Heart CentreBarts Health NHS Trust London United Kingdom.
  • Davies RH; University Hospital and University of Trieste Trieste Italy.
  • Adam R; Barts Heart CentreBarts Health NHS Trust London United Kingdom.
  • Shetye A; Barts Heart CentreBarts Health NHS Trust London United Kingdom.
  • Augusto JB; Barts Heart CentreBarts Health NHS Trust London United Kingdom.
  • Bhuva A; Barts Heart CentreBarts Health NHS Trust London United Kingdom.
  • Fröjdh F; University College London London United Kingdom.
  • Wong TC; Barts Heart CentreBarts Health NHS Trust London United Kingdom.
  • Fukui M; Department of Clinical Physiology Karolinska University Hospital, and Karolinska Institute Stockholm Sweden.
  • Cavalcante JL; UPMC Cardiovascular Magnetic Resonance CenterUPMC Pittsburgh PA.
  • Treibel TA; Department of Medicine University of Pittsburgh School of Medicine Pittsburgh PA.
  • Manisty C; Heart and Vascular InstituteUPMC Pittsburgh PA.
  • Fontana M; Clinical and Translational Science InstituteUniversity of Pittsburgh Pittsburgh PA.
  • Ugander M; Minneapolis Heart InstituteAbbott Northwestern Hospital Minneapolis MN.
  • Moon JC; Minneapolis Heart InstituteAbbott Northwestern Hospital Minneapolis MN.
  • Schelbert EB; Barts Heart CentreBarts Health NHS Trust London United Kingdom.
  • Kellman P; Barts Heart CentreBarts Health NHS Trust London United Kingdom.
J Am Heart Assoc ; 11(4): e023849, 2022 02 15.
Article em En | MEDLINE | ID: mdl-35132872
ABSTRACT
Background Global longitudinal shortening (GL-Shortening) and the mitral annular plane systolic excursion (MAPSE) are known markers in heart failure patients, but measurement may be subjective and less frequently reported because of the lack of automated analysis. Therefore, a validated, automated artificial intelligence (AI) solution can be of strong clinical interest. Methods and Results The model was implemented on cardiac magnetic resonance scanners with automated in-line processing. Reproducibility was evaluated in a scan-rescan data set (n=160 patients). The prognostic association with adverse events (death or hospitalization for heart failure) was evaluated in a large patient cohort (n=1572) and compared with feature tracking global longitudinal strain measured manually by experts. Automated processing took ≈1.1 seconds for a typical case. On the scan-rescan data set, the model exceeded the precision of human expert (coefficient of variation 7.2% versus 11.1% for GL-Shortening, P=0.0024; 6.5% versus 9.1% for MAPSE, P=0.0124). The minimal detectable change at 90% power was 2.53 percentage points for GL-Shortening and 1.84 mm for MAPSE. AI GL-Shortening correlated well with manual global longitudinal strain (R2=0.85). AI MAPSE had the strongest association with outcomes (χ2, 255; hazard ratio [HR], 2.5 [95% CI, 2.2-2.8]), compared with AI GL-Shortening (χ2, 197; HR, 2.1 [95% CI,1.9-2.4]), manual global longitudinal strain (χ2, 192; HR, 2.1 [95% CI, 1.9-2.3]), and left ventricular ejection fraction (χ2, 147; HR, 1.8 [95% CI, 1.6-1.9]), with P<0.001 for all. Conclusions Automated in-line AI-measured MAPSE and GL-Shortening can deliver immediate and highly reproducible results during cardiac magnetic resonance scanning. These results have strong associations with adverse outcomes that exceed those of global longitudinal strain and left ventricular ejection fraction.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Insuficiência Cardíaca Tipo de estudo: Guideline / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Insuficiência Cardíaca Tipo de estudo: Guideline / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article