TABLE 2:

Quantitative comparison of automated methods for echo quality assessment. A2C (apical 2 view), A3C (apical 3 view), A4C (apical 4 view), PLAX_A (parasternal long axis view, aortic valve), PLAX_PM (parasternal long axis view, papillary muscle), GHT (Generalized Hough Transform), CNN (Convolutional neural network), TPR (true positive rate), CC (correlation coefficient).

Work	ROI Method	Mode & View	Method	System & Data	Train & Test	Ground Truth	Performance
[27]	NA	B-mode: A4C	Model-based: B-splines to model four chambers; goodness-of-fit	GE Vivid E9 system 95 videos	Train: 4 patients 35 cases Test: 2 patients 60 cases	Scores by 2 cardiologists: Good, fair, and poor	TPR (Section 3.1): Good quality: 22% Fair quality: 20% Poor quality: 15%
[28]	NA	B-mode: PLAX	Model-based: GHT applied to input image compared with Atlas: created from images segmented manually	GE Vivid 7 system 133 images 35 patients	Train: 89 images to create PLAX Atlas Test: 44	Scores by expert sonographer: Good (score 3) Poor (score 0)	CC (Section 3): 0.84 correlation between manual and automated scores
[29]	NA	B-mode: A4C	Deep Learning: Customized regression CNN	NA system; 2,904 A4C images	Train: 80% 2,345 images; Test: 20% 560 images;	Scores by expert cardiologist: Good and Poor	Mean Absolute Error (MAE): 0.87 ± 0.72
[30]	NA	B-mode: AP2, AP3, AP4, PLAXA, PLAXPM	Deep Learning: Customized regression CNN	Different GE and Philips systems; 2,450 cines: A2C (478), A3C (455), A4C (575), PLAXA(480), PLAXP(462)	Train: 80% # videos per view = 935 Total (4,675); Test: 20% # videos per view = 228 Total (1,144); 20 frames videos	Scores by physicians: A2C (0–8), A3C (0–7), A4C (0–10),PLAXA(0–4),PLAXPM(0–5) scores normalized	View accuracy: $(1-{\sum }_1^T|A-M|)$ T: cases per view A-M: auto-hand, A2C (86±9)A3C (89±9)A4C (83±14)PLAXA(84±12)PLAXP(83±13)