Findings in the field of echocardiography and machine learning
| Reference | Study year | Application | Machine learning model used | Training/ validation set | Test set | Sensitivity/specificity/accuracy |
| 37 | 2018 | Recognise 15 echocardiography views | Convolutional neural network | 200 000 images | 20 000 images | –/–/91.7% |
| 38 | 2018 | Quantification of wall motion abnormalities | Double density-dual tree discrete wavelet transform | 279 images | – | 96.12%/96%/96.05% |
| 39 | 2016 | Classification/ discrimination of pathological patterns (HCM vs ATH) | Support vector machine, random forest, artificial neural network | – | – | 96%/77%/– |
| 40 | 2016 | Quantification of MR | Support vector machine | 5004 frames | – | 99.38%/99.63%/99.45% |
| 13 | 2015 | Calculation of EF and LS | AutoEF Software | – | 255 patients | – |
| 41 | 2013 | Automated detection of LV border | Random forest classifier with an active shape model | 50 images | 35 images | –/–/90.09% |
ATH, athletes’ heart; EF, ejection fraction; HCM, hypertrophic cardiomyopathy; LS, longitudinal strain; LV, left ventricle; MR, mitral regurgitation.