Curated and Annotated Dataset of Lung US Images in Zambian Children with Clinical Pneumonia.

IF 8.1 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Radiology-Artificial Intelligence Pub Date : 2024-03-01 DOI:10.1148/ryai.230147

Lauren Etter, Margrit Betke, Ingrid Y Camelo, Christopher J Gill, Rachel Pieciak, Russell Thompson, Libertario Demi, Umair Khan, Alyse Wheelock, Janet Katanga, Bindu N Setty, Ilse Castro-Aragon

引用次数: 0

Abstract

See also the commentary by Sitek in this issue. Supplemental material is available for this article.

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赞比亚临床肺炎患儿肺部 US 图像的编辑和注释数据集。

"刚刚接受 "的论文经过同行评审，已被接受在《放射学》上发表：人工智能》上发表。这篇文章在以最终版本发表之前，还将经过校对、排版和校对审核。请注意，在制作最终稿件的过程中，可能会发现影响内容的错误。所提供的肺部 US 数据集包含从 200 名患有肺炎或重症肺炎的赞比亚儿童以及 200 名年龄和性别匹配的健康儿童身上获取的图像；此外，PedLUS 数据集中还注释了 57 名肺炎儿童的肺部合并模式。©RSNA，2024 年。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Radiology-Artificial Intelligence

CiteScore

16.20

自引率

1.00%

发文量

期刊介绍： Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.