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. 2022 Jun 14;28(22):2494–2508. doi: 10.3748/wjg.v28.i22.2494

Table 1.

Overview of development and testing datasets

Stage
Name
Purpose
Labels
Patients
Studies
Images
DL learning BD-L Big data, to train the neural network 2D US Dx 2899 17149 200654
DL validation BD-V Big data, to tune model performance 2D US Dx 411 2364 27421
Testing HP-U Histopathology-proven group, to (a) measure the trend between DL predictions and histology (b) measure reliability across 2D US liver viewpoints Histology 147 147 1647
TM Tri-machine data US Dx group, to (a) measure reliability across 2D US liver viewpoints and (b) measure reliability across scanners - 246 733 9215
HP-T1 Histology proven group to measure the trend between DL predictions and histology Histology, CAP 112 112 1996
1

Labels blind to deep learning researchers during course of algorithmic development.

DL: Deep learning; US: Ultrasound; BD-L: Big data learning group; BD-V: Big data validation group; HP-U: Histopathology unblinded test group; TM: Trimachine group; HP-T: Histopathology blinded test group; CAP: Control attenuation parameter; 2D: Two-dimensional; Dx: Diagnosis.