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Table 1 Previous AI studies of echocardiography

From: Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis

  Aim of study AI algorithm or commercial technique Detail method/software Diagnostic Efficiencya Reference
Section recognition/ image acquisition Automatic classification of cardiac views AI algorithm CNN ACC = 98.3% Ultrasound Med Biol, 2019 [19]
AI algorithm CNN ACC = 97.8% NPJ Digital Med, 2018 [12]
Segmentation Left ventricular segmentation (provide accuracy improvement of the endocardial boundary recognition) AI algorithm Ant colony optimization N/A Biomed Mater Eng, 2014 [13]
AI algorithm Radial active contour method Snake N/A Comput Methods Programs Biomed, 2014 [20]
AI algorithm Iterative 3-D cross-correlation algorithm N/A Ultrasound Med Biol, 2014 [21]
AI algorithm Ant colony optimization N/A Biomed Mater Eng, 2014 [13]
Right ventricular segmentation AI algorithm Sparse matrix transform/ wall thickness constraint feature Dice = 90.8% (epicardial boundary), 87.3% (endocardial boundary) Phys Med Biol, 2013 [22]
Atrial and multi-chamber heart segmentation AI algorithm Active shape model/ fusion-imaging technology Mean dice = 83.3% ~ 91.3% (LV) Ultrasound Med Biol, 2015; IEEE Trans Ultrason Ferroelectr Freq Control, 2015 [23, 24]
LV assessment Assessment of left heart function Commercial technique HeartModel (Philips) r = 0.87 ~ 0.96/ r = 0.98 JACC: Cardiovascular Imaging, 2016; J Am Soc Echocardiogr, 2017 [25, 26]
Commercial technique AutoLV (TomTec Imaging system) ICC = 1.0 J Am Coll Cardiol, 2015 [27]
Commercial technique AutoEF (BayLabs) r = 0.95 Circ Cardiovasc Imaging, 2019 [28]
AI algorithm 3D CNN AUC = 0.92 J Am Soc Echocardiogr, 2020 [29]
AI algorithm PWC-Net ACC = 97% ~ 98% JACC Cardiovasc Imaging, 2021 [30]
AI algorithm Neural network Intraclass correlation = 0.86 – 0.95 (AI and physician), 0.84 (novice using AI and physician) Circ Cardiovasc Imaging, 2021 [31]
Cardiac disease diagnosis Valvular heart disease Commercial technique Proximal isovelocity surface area (PISA) Intraclass correlation coefficients = 0.96 Circ Cardiovasc Imaging, 2013; J Am Soc Echocardiogr, 2012 [32, 33]
Commercial technique Real-time 3D volume color-flow Doppler (RT-VCFD) r = 0.93 Int J Cardiovasc Imaging, 2015 [34]
Commercial technique Mitral Valve Navigator (Philips) N/A Echocardiography, 2016 [35]
Commercial technique Anatomical Intelligence in ultrasound (AIUS) ACC = 89% J Am Soc Echocardiogr, 2016 [36]
AI algorithm Anatomical affine optical flow Intra- and interobserver variability = 0.85 and 0.65 Int J Cardiovasc Imaging, 2019 [37]
AI algorithm 2D/3D CNN Sensitivity (Recall) = 67%, PPV = 74% ~ 77% Nat Commun, 2021 [38]
Cardiomyopathy Commercial technique Myocardial strain analysis N/A Circ J, 2019 [39]
AI algorithm Associative memory classifier AUC = 89.2% Circ Cardiovasc Imaging, 2016 [40]
AI algorithm Support Vector Machine AUC = 77.8% AMIA Annu Symp Proc, 2014 [41]
Coronary atherosclerotic heart disease AI algorithm discrete wavelet transform and texture feature analysis AUC = 90% ~ 99% JACC Cardiovasc Imaging, 2019 [42]
Commercial technique EchoPAC PC (GE) r = 0.67 ~ 0.99 Echocardiography, 2015 [43]
AI algorithm A specialised software for MCE quantification Sensitivity = 77%
Specificity = 94%
Ultrasound Med Biol, 2017 [15]
AI algorithm Texture analysis Highest accuracy = 79% IEEE, 2016 [44]
Intracardiac masses AI algorithm Feed-forward neural network Accuracy = 91% Comput Med Imaging Graph, 2006 [45]
AI algorithm Gray level co-occurrence matrix-based features/ ANN (Classification) Mean sensitivity = 0.955
Specificity = 0.970
J Ultrasound Med, 2014 [46]
  1. CNN Convolutional neural network, ACC Accuracy, AUC Area under the curve, ANN Artificial Neural Network, LV Left Ventricular
  2. aDiagnostic Efficiency: including recognition/diagnostic accuracies, correlation coefficient, PPV (positive predictive value), sensitivity (recall) and specificity, etc.