Image analysis of cardiac hepatopathy secondary to heart failure: Machine learning vs gastroenterologists and radiologists

Figure 1 Imaging features of congestive liver. A: Ascites and slightly blunted liver edges in severe cases (orange arrow); B: Enlargement of the hepatic veins and Right and Left lobe enlargement (orange bidirectional arrow); C: Images at the paraumbilical vein, dilation of the paraumbilical vein, a component of the portal venous system, is observed (orange arrow).

Figure 2 Transthoracic echocardiographic images demonstrating three grades of tricuspid regurgitation severity using color Doppler flow mapping. A: Mild tricuspid regurgitation (TR): A narrow, faint regurgitant jet confined to the tricuspid valve plane; B: Moderate TR: A more prominent jet extending into the right atrium but not reaching the posterior wall; C: Severe TR: A broad, dense jet filling more than half of the right atrium with systolic flow reversal in the hepatic veins. TR: Tricuspid regurgitation.

Figure 3 Convolutional neural network (RandomShift) - applies random shifts to the image. RandomFlip: Performs random flipping along specified axes. MulScalar & AddScalar: Normalization steps for pixel values. Convolution: Multiple convolution layers. BatchNormalization: Normalizes activations between layers. RepeatStart & RepeatEnd: Suggests looped operations, likely for iterative feature extraction.

Figure 4  Numbers on the Y-label are assigned to Y'0, Y'1, and Y'2 for the tricuspid regurgitation group.

Figure 5 Representative cases of congestive liver associated with varying severity of tricuspid regurgitation. A: Mild tricuspid regurgitation (TR); B: Moderate TR; C: Severe TR.