TABLE 5:
Summary of automated CVD classification methods for B-mode and Doppler. LV (left ventricle), WMA (wall motion abnormalities), A2C-A3C-A4C (apical 2–3-4 view), CW (continuous wave), CAD (coronary artery disease), DCM (dilated cardiomyopathy), HCM (Hypertrophic cardiomyopathy), ATH (physiological hypertrophy in athletes), MI (myocardial infarction), AS (aortic stenosis), AR (aortic regurgitation), GLCM (gray level co-occurrence matrix), GLRLM (gray level run length matrix ), GLDS (gray level difference statistics), SM (statistical feature matrix), LCP (Local Configuration Pattern), PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), DCT (Discrete cosine transform), DT (Decision Tree), RF (Random Forest), NN (neural network), SVM (support vector machine), k-NN (k-nearest neighbors), IG (information gain), KS-test (Kolmogorov-Smirnov test), mRMR (Max-Relevancy and Min-Redundancy Feature).
| Work | ROI | Objective | Data & Labels | Method | Features & Markers | Top Features | Performance |
|---|---|---|---|---|---|---|---|
| [103] | NA | LV WMA Detection; A2C, A4C B-mode | 129 patients, 65 patients (train), 64 patients (test); LV contours and Abnormalities scores by 2 expert readers | LV modeling using PCA; shape modes describe variations in the population; LDA classifier | Features: statistical parameters extracted from shape models; Biomarkers: NA | 8 PCA parameters; | Avg. accuracy (Correctly classified cases): 88.9 |
| [104] | NA | LV WMA Detection; A2C, A3C, A4C B-mode | Data of normal & abnormal (hypokinetic, akinetic, dyskinetic, aneurysm) patients; 220/125, train/test Abnormalities scores | Hand-initialized dual-contours (endocardium and epicardium) tracked over time; bayesian networks (binary) | Features extracted from contour: circumferential strain, radial strain, local, global, and segmental volume markers | 6 features (global & local) based on KS-test | Sensitivity (Section 3.1): 80 to 90 |
| [105] | Manual | LV WMA Detection; A2C & A4C B-mode | Data of 10 healthy & and 14 patients with ischemic; 336 segments: 55% normal, 13% hypo -kinetic, 31% akinetic; 220/125, train/test; Abnormalities scores | Affine registration and B-spline snake to model LV; threshold classifier | Novel regional index computed from control points of B-spline snake; Biomarkers: NA | New Quantitative Regional Index | Agreement between 2 experts and automated: Absolute, 83 Relative, 99 |
| [106] | Manual | CAD risk assessment; B-mode | Stroke-risk (>0.9mm) to label patients as: High risk CAD (9), Low risk CAD (6); 1508 frames high risk, 1357 frames low risk; ROIs by 2 experts | 56 grayscale feature extracted: GLCM, GLRLM, GLDS, SM, invariant moment; SVM classifier; k-fold cross validation | Derived 6 Feature Combinations: FC1, FC2, FC3, F4, F5, F6; Biomarkers: NA | Best feature set was chosen based on classification accuracy (FC6) | Avg. accuracy (Section 3.1): 94.95; AUC: 0.95; |
| [111] | NA | MI stage detection; A4C B-mode | WMSI & LVEF to label patients as: normal (40), 200 moderate (40), severe (40); 600 images, 200 per class; age: 21–75 | Curvelet Transform and LCP features; LDA, SVM, DT, NB, kNN, NN for classification; 10-fold cross validation | 17,850 LCP features extracted from 46,200 CT coefficients; Biomarkers: NA | mRMR method: 30 coefficients, 6 features; proposed Myocardial Infarction Risk Index (MIRI) | Accuracy: 98.99; sensitivity: 98.48; specificity: 100% (SVM, RBF) |
| [114] | Auto. Fuzzy c-means (FCM) | DCM & HCM detection; LV, PSAX, B-mode | Data of 20 normal, 30 DCM, and 10 HCM patients; 60 (4–6 seconds) videos, 46 fps | LV segmentation by FCM clustering; shape & statistical (PCA & DCT) features; NN, SVM & combine k-NN for classification | DCT & PCA features; Biomarkers: EF, EDV, ESV, mass, septal thickness | PCA features is better than DCT and LV biomarkers | TPR: 92.04 (normal, abnormal) (NN) |
| [115] | NA | Distinguish HCM & ATH; LV, A4C, B-mode | 139 male subjects, 77 with ATH, 62 with HCM; poor quality images excluded | TomTec software for LV speckle tracking; ensemble of NN, SVM, RF for classification; 10 cross validation | Speckle-tracking based geometric (e.g., volume) & mechanical (e.g., velocity) parameters | Based on info. gain (IG): Volume (0.24), MLVS (0.134), ALS (0.13) | Sensitivity: overall (87), adjusted for age (96); Specificity: overall (82), adjusted for age (77) |
| [83] | NA | AR assessment; CW, Doppler | 9 male & 2 female subjects with mild, moderate, severe AR; 22 images; 3 age groups: G1 (20–35), G2 (36–50), G3 (51–65); ground truth by experts | Envelope delineation: filtering, morphological operations, thresholding, edge detection | Parameters computed from detected envelope: peak velocity, pressure gradient, pressure half time | Pressure half time (PHT) | High CC between automated and manual: r=0.95 |
| [63] | NA | Valves dysfunctions quantification; CW, Doppler | 60 patients: 30 with aortic/mitral stenosis; 20 with normal sinus rhythm; 10 with atrial fibrillation; ground truth: manual indices by expert | Envelope delineation: Active contour for envelope delineation | Doppler indices computed from detected envelope: Peak velocity (PV ), Mean velocity (MV ), Velocity time integral (VTI) | Mean velocity (MV ) | B & A, LOA (Section 3.2): (−3.9 to +0.5), (−4.6 to −1.4), (−3.6 to +4.4) for PV, MV, and VTI (acceptable) |