人口统计信息
包含958例儿童胸片人口统计信息及AI诊断性能评估数据。
基本信息
资源简介
人口统计信息
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 5.63 KB，主题涵盖 yet dedicated solutions、support radiological workflows、specialized pediatric radiologists、pediatric chest x、original reports validated，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Demographic information.》。源站首次发布：2025-07-24。
【数据内容】
源站原始描述：The number of commercially available artificial intelligence (AI) tools to support radiological workflows is constantly increasing, yet dedicated solutions for children are largely unavailable. Here, we repurposed an AI-tool developed for chest radiograph interpretation in adults (Lunit INSIGHT CXR) and investigated its diagnostic performance in a real-world pediatric clinical dataset. 958 consecutive frontal chest radiographs of children aged 2−14 years were included and analyzed with the comme…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 1.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0328295.t001。
【主题与分类】
源站学科分类：Space Science、Medicine、Molecular Biology、Sociology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified。主题标签：yet dedicated solutions、support radiological workflows、specialized pediatric radiologists、pediatric chest x、original reports validated、optimized thresholds identified、mediastinal widening due、lunit insight cxr、dedicated reading session、chest radiograph interpretation。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Demographic_information_/29638068
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Agarwal, Prerana; Rau, Alexander; Ngo, Helen; Seth, Ambika; Bamberg, Fabian; Kotter, Elmar; et al. (2025). Demographic information.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Demographic information.
- DOI：10.1371/journal.pone.0328295.t001
- 发布日期：2025-07-24
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Demographic_information_/29638068
命令行下载
curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/56533005"
文件清单
Table 1.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
The number of commercially available artificial intelligence (AI) tools to support radiological workflows is constantly increasing, yet dedicated solutions for children are largely unavailable. Here, we repurposed an AI-tool developed for chest radiograph interpretation in adults (Lunit INSIGHT CXR) and investigated its diagnostic performance in a real-world pediatric clinical dataset. 958 consecutive frontal chest radiographs of children aged 2−14 years were included and analyzed with the commercially available AI-tool. The reference standard was determined in a dedicated reading session by a board-certified radiologist. The original reports validated by specialized pediatric radiologists, were considered as second readings. All discordant findings were reanalyzed in consensus. The diagnostic performance of the AI-tool was validated using standard measures of accuracy. For this, the continuous AI output (ranging from 0−100) was binarized using vendor recommended thresholds recommended for adults and optimized thresholds identified for children. Relevant findings were defined as consolidation, atelectasis, nodule, cardiomegaly, mediastinal widening due to mass, pleural effusion and pneumothorax. 200 radiographs [20.9%] demonstrated at least one relevant pathology. Using the adult threshold, the AI-tool showed a high performance for all relevant findings with an AUC 0.94 (95% CI: 0.92–0.95) and. In stratified analysis by age (2−7 vs. 7–14-years-old) a significantly higher performance (p < 0.001) was found for older children with an AUC of 0.96 (95% CI: 0.94–0.98) with a sensitivity and specificity of 87.5% and 82.3% respectively, which further increased using optimized thresholds for children. Repurposing existing AI-tools developed for adult application to pediatric patients could support clinical workflows until dedicated solutions become available.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0328295.t001（source: https://plos.figshare.com/articles/dataset/Demographic_information_/29638068）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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