胸部X光AI评估统计分析数据集

数据集获取（Figshare）

• 源站标题：Statistical analysis
• DOI：10.6084/m9.figshare.27138981.v1
• 发布日期：2024-09-30
• 文件数：5 个
• 文件总体积：39.76 KB
• 源站页面：https://figshare.com/articles/dataset/Statistical_analysis/27138981

命令行下载

curl -L -o "1. TORAX Descriptivo y lectura Adjunto (Gold Estandar).txt" "https://ndownloader.figshare.com/files/49515504"
curl -L -o "2. TORAX Validez lectura del Residente.log" "https://ndownloader.figshare.com/files/49515507"
curl -L -o "3. TORAX  Validez lectura Arterys.log" "https://ndownloader.figshare.com/files/49515510"
curl -L -o "4. TORAX Concordancia Arterys y Residente.log" "https://ndownloader.figshare.com/files/49515513"
curl -L -o "Neumo_Corregido.txt" "https://ndownloader.figshare.com/files/49515516"

文件清单

• 1. TORAX Descriptivo y lectura Adjunto (Gold Estandar).txt（4.50 KB）
• 2. TORAX Validez lectura del Residente.log（15.27 KB）
• 3. TORAX Validez lectura Arterys.log（10.50 KB）
• 4. TORAX Concordancia Arterys y Residente.log（3.90 KB）
• Neumo_Corregido.txt（5.58 KB）

文件总体积 39.76 KB（源站 API 实测）。

数据说明

The integration of AI in the evaluation of Chest X-rays could assist clinicians and enhance accuracy and workflows. We evaluated sensitivity (Se) and specificity (Sp) of an AI software and a radiology resident in interpreting Chest X-ray examinations referred from the Emergency Department (ED) against a senior radiologist (Gold Standard, GS). We also assessed the concordance rate between AI and the resident, described the frequency of doubtful cases in each category and determine how many were considered positive by the GS and evaluated other variables AI is not trained to detect. We conducted an observational, retrospective study, analyzing thoracic radiographs from a sample of 784 patients referred from the ED at our hospital. AI is trained to detect five categorical variables (pulmonary nodule, pulmonary opacity, pleural effusion, pneumothorax, and fracture) and associate them with a confidence label (“positive,” “doubtful,” or “negative”). Se for fracture and pneumothorax was high (100%), moderate for pulmonary opacity (AI= 76%, resident= 71%), and reasonable for pleural effusion (AI= 60%, resident=67%), with NPV>95% and AUC>0.8. For pulmonary nodule, the resident’‘s Se was moderate (75%), while AI’'s was low (33%). When AI doubted, only a few of these doubtful diagnoses were considered positive by the GS. The resident doubted less. The Kappa Coefficient between resident and AI was fair (0.3) for all variables, except for pleural effusion, which was moderate (0.5). The prevalence of other variables was: 16% mediastinal abnormalities, 20% surgical material, and 20% other pulmonary findings. AI would be useful to detect most of the aforementioned items, except for pulmonary nodules. Its high NPV stands out, making it useful for screening. When AI doubts, most of the findings are negative. It would be useful to train AI to detect additional findings.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.27138981.v1（source: https://figshare.com/articles/dataset/Statistical_analysis/27138981）。