Clinical Validation of a Deep Learning-Based 2D Ultrasound Steatosis Algorithm: Cutoff Transferability, Scanner Generalizability, and Comparison with FibroScan

<b>Background:</b> Liver steatosis assessment by 2D ultrasound is widely used but remains subjective. We previously developed a deep learning (DL) algorithm for objective steatosis quantification. This study aimed to (1) establish histology-based cutoffs, (2) evaluate their transferability across different imaging views, and (3) validate performance on a new scanner not included in training. <b>Methods:</b> We retrospectively analyzed 588 ultrasound studies from 457 histology-proven cases and prospectively collected paired scans using a new scanner (Philips Affiniti 70). Images from right intercostal, left hepatic lobe, and subcostal views were processed with the DL algorithm, and mean values from 3-5 images per view were correlated with histology. <b>Results:</b> Across three views, the DL algorithm achieved AUROCs of 0.891-0.936 across steatosis grades, consistently outperforming FibroScan's controlled attenuation parameter (0.840-0.905), especially in moderate-to-severe steatosis (<i>p</i> < 0.001). Cutoffs established from right intercostal images (N = 565) were applied to images from left hepatic lobe (N = 464) and subcostal views (N = 341), yielding accuracies of 0.792-0.850. On Affiniti 70 images, AUROCs remained high (0.838-0.896), supporting scanner generalizability. <b>Conclusions:</b> The DL algorithm provides accurate, view-independent steatosis grading across different ultrasound scanners and outperforms CAP, supporting its real-world use for objective, reproducible quantification.