| 000 | 00997nam a22002657a 4500 | ||
|---|---|---|---|
| 008 | 210111s20192019 xxu||||| |||| 00| 0 eng d | ||
| 098 | _aC2019_451 | ||
| 245 | _aAccuracy and reproducibility of a novel artificial intelligence deep learning-based algorithm for automated calculation of ejection fraction in echocardiography. | ||
| 251 | _a2019/03/16/ | ||
| 260 |
_c2019 _fFY2019 |
||
| 266 | _d2021-01-11 | ||
| 658 | _aPoster presented at | ||
| 698 |
_a68th Annual Scientific Sessions of the American College of Cardiology: ACC.19 _cNew Orleans, LA. _eMedStar Health Research Institute |
||
| 700 | _aAbraham, Theodore | ||
| 700 | _aAdams, Mike | ||
| 700 | _aAsch, Federico | ||
| 700 | _aCleve, Jayne | ||
| 700 | _aHong, Ha | ||
| 700 | _aJankowski, Madelline | ||
| 700 | _aLang, Roberto | ||
| 700 | _aPolivert, Nicolas | ||
| 700 | _aRomano, Nathanael | ||
| 790 | _aAsch FM, Abraham T, Jankowski M, Cleve J, Adams M, Romano N, Polivert N, Hong H, Lang R. | ||
| 942 |
_cPOSTER _dPoster |
||
| 999 |
_c8798 _d8798 |
||