Evidence map›Paper›PMID 39882154›Full record

ArticleData in brief2025

Large annotated ultrasound dataset of non-alcoholic fatty liver from Saudi hospitals for analysis and applications.

Fahad Alshagathrh, Mahmood Alzubaidi, Khalid Alswat, Ali Aldhebaib, Bushra Alahmadi, Meteb Alkubeyyer, Abdulaziz Alosaimi, Amani Alsadoon, Maram Alkhamash, Jens Schneider and 1 more

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In one paragraph

Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Fahad AlshagathrhCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Mahmood AlzubaidiCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Khalid AlswatLiver Disease Research Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Ali AldhebaibRadiological Sciences Program, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
Bushra AlahmadiDepartment of Pathology and Laboratory Medicine, King Abdulaziz Medical City, Riyadh, Saudi Arabia.
Meteb AlkubeyyerRadiology Department, King Saud University Medical City, Riyadh, Saudi Arabia.
Abdulaziz AlosaimiMedical Imaging Department, King Abdulaziz Medical City, Riyadh, Saudi Arabia.
Amani AlsadoonLiver Disease Research Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Maram AlkhamashLiver Disease Research Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Jens SchneiderCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Mowafa HousehCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Computer visionLiver fibrosis StagingLiver ultrasound imagingSteatosis gradingUltrasound dataset

Identifiers

PMID39882154
PMCPMC11774810

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.