Large annotated ultrasound dataset of non-alcoholic fatty liver from Saudi hospitals for analysis and applicationsOpen Science framework or (OSF)

Large annotated ultrasound dataset of non-alcoholic fatty liver from Saudi hospitals for analysis and applicationsOpen Science framework or (OSF)

This study presents a comprehensive ultrasound image dataset for Non-Alcoholic Fatty Liver Disease (NAFLD), addressing the critical need for standardized resources in AI-assisted diagnosis. The dataset comprises 10,352 high-resolution ultrasound images from 384 patients collected at King Saud Univer...

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Bibliographic Details
Main Authors: Fahad Alshagathrh, Mahmood Alzubaidi, Khalid Alswat, Ali Aldhebaib, Bushra Alahmadi, Meteb Alkubeyyer, Abdulaziz Alosaimi, Amani Alsadoon, Maram Alkhamash, Jens Schneider, Mowafa Househ
Format: Article
Language:English
Published: Elsevier 2025-02-01
Series:Data in Brief
Subjects:
Liver ultrasound imaging
Steatosis grading
Liver fibrosis Staging
Ultrasound dataset
Computer vision
Online Access:http://www.sciencedirect.com/science/article/pii/S2352340924012289
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Summary:This study presents a comprehensive ultrasound image dataset for Non-Alcoholic Fatty Liver Disease (NAFLD), addressing the critical need for standardized resources in AI-assisted diagnosis. The dataset comprises 10,352 high-resolution ultrasound images from 384 patients collected at King Saud University Medical City and National Guard Health Affairs in Saudi Arabia. Each image is meticulously annotated with NAFLD Activity Score (NAS) fibrosis staging and steatosis grading based on corresponding liver biopsy results. Unlike other datasets that rely on bounding boxes, we opted for full-image labelling based on biopsy findings, which link to histopathological results, ensuring more precise representation of liver conditions. Rigorous pre-processing ensures high-quality image preservation, including expert radiologist assessment, DICOM to PNG conversion, and standardization to 768 × 1024 pixels. This resource supports various computer vision tasks, enabling the development of AI algorithms for accurate NAFLD diagnosis and staging. A large, diverse, and well-annotated dataset like ours is essential for enhancing model performance and generalization, providing a valuable resource for researchers to develop robust AI models in medical imaging.
ISSN:2352-3409

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