Automated estimation of echocardiogram image quality in hospitalized patients.
We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients. This study applied a machine learning model for automated transthoracic echo (TTE) image quality scoring in three inpatient groups. Our objectives were: (1) Assess the feasibility of a machine learning model for echo image quality analysis, (2) Establish the comprehensiveness of real-world TTE reporting by clinical group, and (3) Determine the relationship between machine learning image quality and comprehensiveness of TTE reporting. A machine learning model was developed and applied to TTEs from three matched cohorts for image quality of nine standard views. Case TTEs were comprehensive studies in mechanically ventilated patients between 01/01/2010 and 12/31/2015. For each case TTE, there were two matched spontaneously breathing controls (Control 1: Inpatients scanned in the lab and Control 2: Portable studies). We report the overall mean maximum and view specific quality scores for each TTE. The comprehensiveness of an echo report was calculated as the documented proportion of 12 standard parameters. An inverse probability weighted regression model was fit to determine the relationship between machine learning quality score and the completeness of a TTE report. 175 mechanically ventilated TTEs were included with 350 non-intubated samples (175 Control 1: Lab and 175 Control 2: Portable). In total, the machine learning model analyzed 14,086 echo video clips for quality. The overall accuracy of the model with regard to the expert ground truth for the view classification was 87.0%. The overall mean maximum quality score was lower for mechanically ventilated TTEs (0.55 [95% CI 0.54, 0.56]) versus 0.61 (95% CI 0.59, 0.62) for Control 1: Lab and 0.64 (95% CI 0.63, 0.66) for Control 2: Portable; p = 0.002. Furthermore, mechanically ventilated TTE reports were the least comprehensive, with fewer reported parameters. The regression model demonstrated the correlation of echo image quality and completeness of TTE reporting regardless of the clinical group. Mechanically ventilated TTEs were of inferior quality and clinical utility compared to spontaneously breathing controls and machine learning derived image quality correlates with completeness of TTE reporting regardless of the clinical group.
['Luong|Christina|C|', 'Liao|Zhibin|Z|', 'Abdi|Amir|A|', 'Girgis|Hany|H|', 'Rohling|Robert|R|', 'Gin|Kenneth|K|', 'Jue|John|J|', 'Yeung|Darwin|D|', 'Szefer|Elena|E|', 'Thompson|Darby|D|', 'Tsang|Michael Yin-Cheung|MY|', 'Lee|Pui Kee|PK|', 'Nair|Parvathy|P|', 'Abolmaesumi|Purang|P|', 'Tsang|Teresa S M|TSM|']
D000328:Adult D000368:Aged D000369:Aged, 80 and over D001331:Automation D016022:Case-Control Studies D004452:Echocardiography D005240:Feasibility Studies D005260:Female D006760:Hospitalization D006801:Humans D007090:Image Interpretation, Computer-Assisted D007297:Inpatients D000069550:Machine Learning D008297:Male D008875:Middle Aged D011237:Predictive Value of Tests D015203:Reproducibility of Results D012121:Respiration, Artificial D014741:Video Recording
Machine learning Artificial intelligence Echocardiography