Accurate and generalizable quantitative scoring of liver steatosis from ultrasound images via scalable deep learning

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

¹Labels 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.