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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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924012289 |
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| author | Fahad Alshagathrh Mahmood Alzubaidi Khalid Alswat Ali Aldhebaib Bushra Alahmadi Meteb Alkubeyyer Abdulaziz Alosaimi Amani Alsadoon Maram Alkhamash Jens Schneider Mowafa Househ |
| author_facet | Fahad Alshagathrh Mahmood Alzubaidi Khalid Alswat Ali Aldhebaib Bushra Alahmadi Meteb Alkubeyyer Abdulaziz Alosaimi Amani Alsadoon Maram Alkhamash Jens Schneider Mowafa Househ |
| author_sort | Fahad Alshagathrh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-de4f21d5408c4ad2890c07d8ec204dee |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-de4f21d5408c4ad2890c07d8ec204dee2025-01-31T05:11:44ZengElsevierData in Brief2352-34092025-02-0158111266Large annotated ultrasound dataset of non-alcoholic fatty liver from Saudi hospitals for analysis and applicationsOpen Science framework or (OSF)Fahad Alshagathrh0Mahmood Alzubaidi1Khalid Alswat2Ali Aldhebaib3Bushra Alahmadi4Meteb Alkubeyyer5Abdulaziz Alosaimi6Amani Alsadoon7Maram Alkhamash8Jens Schneider9Mowafa Househ10College of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarLiver Disease Research Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia; Radiological Sciences Program, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi ArabiaRadiological Sciences Program, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi ArabiaDepartment of Pathology and Laboratory Medicine, King Abdulaziz Medical City, Riyadh, Saudi ArabiaRadiology Department, King Saud University Medical City, Riyadh, Saudi ArabiaMedical Imaging Department, King Abdulaziz Medical City, Riyadh, Saudi ArabiaLiver Disease Research Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia; Radiological Sciences Program, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi ArabiaLiver Disease Research Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia; Radiological Sciences Program, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi ArabiaCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2352340924012289Liver ultrasound imagingSteatosis gradingLiver fibrosis StagingUltrasound datasetComputer vision |
| spellingShingle | Fahad Alshagathrh Mahmood Alzubaidi Khalid Alswat Ali Aldhebaib Bushra Alahmadi Meteb Alkubeyyer Abdulaziz Alosaimi Amani Alsadoon Maram Alkhamash Jens Schneider Mowafa Househ Large annotated ultrasound dataset of non-alcoholic fatty liver from Saudi hospitals for analysis and applicationsOpen Science framework or (OSF) Data in Brief Liver ultrasound imaging Steatosis grading Liver fibrosis Staging Ultrasound dataset Computer vision |
| title | Large annotated ultrasound dataset of non-alcoholic fatty liver from Saudi hospitals for analysis and applicationsOpen Science framework or (OSF) |
| title_full | Large annotated ultrasound dataset of non-alcoholic fatty liver from Saudi hospitals for analysis and applicationsOpen Science framework or (OSF) |
| title_fullStr | Large annotated ultrasound dataset of non-alcoholic fatty liver from Saudi hospitals for analysis and applicationsOpen Science framework or (OSF) |
| title_full_unstemmed | Large annotated ultrasound dataset of non-alcoholic fatty liver from Saudi hospitals for analysis and applicationsOpen Science framework or (OSF) |
| title_short | Large annotated ultrasound dataset of non-alcoholic fatty liver from Saudi hospitals for analysis and applicationsOpen Science framework or (OSF) |
| title_sort | large annotated ultrasound dataset of non alcoholic fatty liver from saudi hospitals for analysis and applicationsopen science framework or osf |
| topic | Liver ultrasound imaging Steatosis grading Liver fibrosis Staging Ultrasound dataset Computer vision |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924012289 |
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