An annotated heterogeneous ultrasound database
Abstract Ultrasound is a primary diagnostic tool commonly used to evaluate internal body structures, including organs, blood vessels, the musculoskeletal system, and fetal development. Due to challenges such as operator dependence, noise, limited field of view, difficulty in imaging through bone and...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04464-4 |
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| _version_ | 1832586051258941440 |
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| author | Yuezhe Yang Yonglin Chen Xingbo Dong Junning Zhang Chihui Long Zhe Jin Yong Dai |
| author_facet | Yuezhe Yang Yonglin Chen Xingbo Dong Junning Zhang Chihui Long Zhe Jin Yong Dai |
| author_sort | Yuezhe Yang |
| collection | DOAJ |
| description | Abstract Ultrasound is a primary diagnostic tool commonly used to evaluate internal body structures, including organs, blood vessels, the musculoskeletal system, and fetal development. Due to challenges such as operator dependence, noise, limited field of view, difficulty in imaging through bone and air, and variability across different systems, diagnosing abnormalities in ultrasound images is particularly challenging for less experienced clinicians. The development of artificial intelligence (AI) technology could assist in the diagnosis of ultrasound images. However, many databases are created using a single device type and collection site, limiting the generalizability of machine learning models. Therefore, we have collected a large, publicly accessible ultrasound challenge database that is intended to significantly enhance the performance of AI-assisted ultrasound diagnosis. This database is derived from publicly available data on the Internet and comprises a total of 1,833 distinct ultrasound data. It includes 13 different ultrasound image anomalies, and all data have been anonymized. Our data-sharing program aims to support benchmark testing of ultrasound disease diagnosis in multi-center environments. |
| format | Article |
| id | doaj-art-45b7fc249b44479a8a67e46cc2d53c96 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-45b7fc249b44479a8a67e46cc2d53c962025-01-26T12:14:35ZengNature PortfolioScientific Data2052-44632025-01-011211810.1038/s41597-025-04464-4An annotated heterogeneous ultrasound databaseYuezhe Yang0Yonglin Chen1Xingbo Dong2Junning Zhang3Chihui Long4Zhe Jin5Yong Dai6Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Artificial Intelligence, Anhui UniversitySchool of Electronic and Information Engineering, Anhui Jianzhu UniversityAnhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Artificial Intelligence, Anhui UniversitySchool of Public Health, Anhui University of Science and TechnologyDepartment of Radiology, Wuhan Third Hospital/Tongren Hospital of Wuhan UniversityAnhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Artificial Intelligence, Anhui UniversitySchool of Medicine, Anhui University of Science and TechnologyAbstract Ultrasound is a primary diagnostic tool commonly used to evaluate internal body structures, including organs, blood vessels, the musculoskeletal system, and fetal development. Due to challenges such as operator dependence, noise, limited field of view, difficulty in imaging through bone and air, and variability across different systems, diagnosing abnormalities in ultrasound images is particularly challenging for less experienced clinicians. The development of artificial intelligence (AI) technology could assist in the diagnosis of ultrasound images. However, many databases are created using a single device type and collection site, limiting the generalizability of machine learning models. Therefore, we have collected a large, publicly accessible ultrasound challenge database that is intended to significantly enhance the performance of AI-assisted ultrasound diagnosis. This database is derived from publicly available data on the Internet and comprises a total of 1,833 distinct ultrasound data. It includes 13 different ultrasound image anomalies, and all data have been anonymized. Our data-sharing program aims to support benchmark testing of ultrasound disease diagnosis in multi-center environments.https://doi.org/10.1038/s41597-025-04464-4 |
| spellingShingle | Yuezhe Yang Yonglin Chen Xingbo Dong Junning Zhang Chihui Long Zhe Jin Yong Dai An annotated heterogeneous ultrasound database Scientific Data |
| title | An annotated heterogeneous ultrasound database |
| title_full | An annotated heterogeneous ultrasound database |
| title_fullStr | An annotated heterogeneous ultrasound database |
| title_full_unstemmed | An annotated heterogeneous ultrasound database |
| title_short | An annotated heterogeneous ultrasound database |
| title_sort | annotated heterogeneous ultrasound database |
| url | https://doi.org/10.1038/s41597-025-04464-4 |
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