Yang Yuezhe, Chen Yonglin, Dong Xingbo, Zhang Junning, Long Chihui, Jin Zhe, Dai Yong
Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, School of Artificial Intelligence, Anhui University, Hefei, 230601, China.
School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, 230601, China.
Sci Data. 2025 Jan 25;12(1):148. doi: 10.1038/s41597-025-04464-4.
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.
超声是一种常用的主要诊断工具，用于评估体内结构，包括器官、血管、肌肉骨骼系统和胎儿发育情况。由于存在诸如依赖操作人员、噪声、视野有限、难以透过骨骼和空气成像以及不同系统之间存在差异等挑战，对于经验不足的临床医生而言，在超声图像中诊断异常情况极具挑战性。人工智能（AI）技术的发展有助于超声图像的诊断。然而，许多数据库是使用单一设备类型和采集地点创建的，这限制了机器学习模型的通用性。因此，我们收集了一个大型的、可公开访问的超声挑战数据库，旨在显著提高人工智能辅助超声诊断的性能。该数据库源自互联网上的公开可用数据，总共包含1833个不同的超声数据。它包括13种不同的超声图像异常情况，并且所有数据均已匿名化。我们的数据共享计划旨在支持多中心环境下超声疾病诊断的基准测试。
