000			04782nam a22006137a 4500
008			220124s20212021 xxu||||| |||| 00| 0 eng d
024			_a10.1001/jamacardio.2021.0185 [doi]
024			_a2776714 [pii]
024			_aPMC8204203 [pmc]
040			_aOvid MEDLINE(R)
099			_a33599681
245			_aUtility of a Deep-Learning Algorithm to Guide Novices to Acquire Echocardiograms for Limited Diagnostic Use.
251			_aJAMA Cardiology. 6(6):624-632, 2021 06 01.
252			_aJAMA Cardiol. 6(6):624-632, 2021 06 01.
252			_zJAMA Cardiol. 6(6):624-632, 2021 06 01.
252			_zJAMA Cardiol. 6(6):624-632, 2021 06 01.
253			_aJAMA cardiology
260			_c2021
260			_fFY2021
260			_p2021 06 01
265			_sppublish
266			_d2021-03-10
268			_aJAMA Cardiology. 6(6):624-632, 2021 06 01.
269			_fFY2022
520			_aConclusions and Relevance: This DL algorithm allows novices without experience in ultrasonography to obtain diagnostic transthoracic echocardiographic studies for evaluation of left ventricular size and function, right ventricular size, and presence of a nontrivial pericardial effusion, expanding the reach of echocardiography to clinical settings in which immediate interrogation of anatomy and cardiac function is needed and settings with limited resources.
520			_aDesign, Setting, and Participants: This prospective, multicenter diagnostic study was conducted in 2 academic hospitals. A cohort of 8 nurses who had not previously conducted echocardiograms was recruited and trained with AI. Each nurse scanned 30 patients aged at least 18 years who were scheduled to undergo a clinically indicated echocardiogram at Northwestern Memorial Hospital or Minneapolis Heart Institute between March and May 2019. These scans were compared with those of sonographers using the same echocardiographic hardware but without AI guidance.
520			_aImportance: Artificial intelligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acquisition of ultrasonography images is a novel area of investigation. A novel deep-learning (DL) algorithm, trained on more than 5 million examples of the outcome of ultrasonographic probe movement on image quality, can provide real-time prescriptive guidance for novice operators to obtain limited diagnostic transthoracic echocardiographic images.
520			_aInterventions: Each patient underwent paired limited echocardiograms: one from a nurse without prior echocardiography experience using the DL algorithm and the other from a sonographer without the DL algorithm. Five level 3-trained echocardiographers independently and blindly evaluated each acquisition.
520			_aMain Outcomes and Measures: Four primary end points were sequentially assessed: qualitative judgement about left ventricular size and function, right ventricular size, and the presence of a pericardial effusion. Secondary end points included 6 other clinical parameters and comparison of scans by nurses vs sonographers.
520			_aObjective: To test whether novice users could obtain 10-view transthoracic echocardiographic studies of diagnostic quality using this DL-based software.
520			_aResults: A total of 240 patients (mean [SD] age, 61 [16] years old; 139 men [57.9%]; 79 [32.9%] with body mass indexes >30) completed the study. Eight nurses each scanned 30 patients using the DL algorithm, producing studies judged to be of diagnostic quality for left ventricular size, function, and pericardial effusion in 237 of 240 cases (98.8%) and right ventricular size in 222 of 240 cases (92.5%). For the secondary end points, nurse and sonographer scans were not significantly different for most parameters.
546			_aEnglish
650			_a*Algorithms
650			_a*Deep Learning
650			_a*Echocardiography
650			_a*Nursing Staff, Hospital/ed [Education]
650			_aArtificial Intelligence
650			_aFemale
650			_aHeart Ventricles/dg [Diagnostic Imaging]
650			_aHumans
650			_aInservice Training
650			_aMale
650			_aMiddle Aged
650			_aProspective Studies
651			_aMedStar Health Research Institute
651			_aMedStar Heart & Vascular Institute
657			_aJournal Article
657			_aResearch Support, Non-U.S. Gov't
700			_aGoldstein, Steven A
700			_aWeissman, Neil J
790			_aBae R, Cadieu C, Chaudhry A, Goldstein S, Hong H, Lang RM, Little SH, Martin RP, McCarthy PM, Narang A, Rubenson DS, Surette S, Thomas JD, Thomas Y, Weissman NJ
856			_uhttps://dx.doi.org/10.1001/jamacardio.2021.0185 _zhttps://dx.doi.org/10.1001/jamacardio.2021.0185
942			_cART _dArticle
999			_c6218 _d6218