Table 1.
Summary of studies applying deep learning to diffuse liver disease
| Study | Task | Data used for deep learning | US system | Total no. of images (total no. of patients) | No. of validation set images (no. of patients) | Ground truth | Method |
|---|---|---|---|---|---|---|---|
| Xue et al. (2020) [2] | Fibrosis | B-mode+elastography | One | 2,330 (466) | 510 (102) | Pathology | CNN |
| Lee et al. (2020) [3] | Fibrosis | B-mode | ≥4 | 14,583 (3,975) | 300 (266) for internal validation 1,232 (572) for external validation | Pathology, elastography, clinical diagnosis | CNN |
| Wang et al. (2019) [6] | Fibrosis | Elastography | One | 1,990 (398) | 660 (132) | Pathology | CNN |
| Treacher et al. (2019) [7] | Fibrosis | B-mode elastography image | One | 3,500 (326) | 524 (N/A) | Shear wave velocity | CNN |
| Byra et al. (2018) [8] | Fibrosis | B-mode | One | 550 (55) | Leave-one-out cross-validation | Pathology | CNN+SVM |
| Meng et al. (2017) [11] | Fibrosis | B-mode | N/A | 279 (279) | 77 (77) | Clinical diagnosis | CNN |
| Liu et al. (2017) [10] | Fibrosis | B-mode | One | 91 (91) | 3-fold cross-validation | Clinical diagnosis (Child-Pugh classification, CT, US) | CNN+SVM |
| Han et al. (2020) [4] | Steatosis | RF US data | One | 2,560 RF signals per participant (204) | 2,560 RF signals per participant (102) | MRI-derived proton density fraction | CNN |
| Cao et al. (2020) [5] | Steatosis | B-mode | One | 1,092 (N/A) | 240 (240) | US scoring system | CNN |
| Biswas et al. (2018) [9] | Steatosis | B-mode | One | 63 (63) | 10-fold cross-validation | Pathology | CNN |
US, ultrasonography; CNN, convolutional neural network; N/A, not available; SVM, support vector machine; CT, computed tomography; RF, radiofrequency; MRI, magnetic resonance imaging.