Department of Radiology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland.
Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland.
Eur Radiol. 2024 Jun;34(6):3624-3634. doi: 10.1007/s00330-023-10424-6. Epub 2023 Nov 20.
To automatically label chest radiographs and chest CTs regarding the detection of pulmonary infection in the report text, to calculate the number needed to image (NNI) and to investigate if these labels correlate with regional epidemiological infection data.
All chest imaging reports performed in the emergency room between 01/2012 and 06/2022 were included (64,046 radiographs; 27,705 CTs). Using a regular expression-based text search algorithm, reports were labeled positive/negative for pulmonary infection if described. Data for regional weekly influenza-like illness (ILI) consultations (10/2013-3/2022), COVID-19 cases, and hospitalization (2/2020-6/2022) were matched with report labels based on calendar date. Positive rate for pulmonary infection detection, NNI, and the correlation with influenza/COVID-19 data were calculated.
Between 1/2012 and 2/2020, a 10.8-16.8% per year positive rate for detecting pulmonary infections on chest radiographs was found (NNI 6.0-9.3). A clear and significant seasonal change in mean monthly detection counts (102.3 winter; 61.5 summer; p < .001) correlated moderately with regional ILI consultations (weekly data r = 0.45; p < .001). For 2020-2021, monthly pulmonary infection counts detected by chest CT increased to 64-234 (23.0-26.7% per year positive rate, NNI 3.7-4.3) compared with 14-94 (22.4-26.7% positive rate, NNI 3.7-4.4) for 2012-2019. Regional COVID-19 epidemic waves correlated moderately with the positive pulmonary infection CT curve for 2020-2022 (weekly new cases: r = 0.53; hospitalizations: r = 0.65; p < .001).
Text mining of radiology reports allows to automatically extract diagnoses. It provides a metric to calculate the number needed to image and to track the trend of diagnoses in real time, i.e., seasonality and epidemic course of pulmonary infections.
Digitally labeling radiology reports represent previously neglected data and may assist in automated disease tracking, in the assessment of physicians' clinical reasoning for ordering radiology examinations and serve as actionable data for hospital workflow optimization.
• Radiology reports, commonly not machine readable, can be automatically labeled with the contained diagnoses using a regular-expression based text search algorithm. • Chest radiograph reports positive for pulmonary infection moderately correlated with regional influenza-like illness consultations (weekly data; r = 0.45; p < .001) and chest CT reports with the course of the regional COVID-19 pandemic (new cases: r = 0.53; hospitalizations: r = 0.65; p < 0.001). • Rendering radiology reports into data labels provides a metric for automated disease tracking, the assessment of ordering physicians clinical reasoning and can serve as actionable data for workflow optimization.
自动标记胸部 X 光和胸部 CT 报告中关于肺部感染的检测，计算所需的成像次数（NNI），并研究这些标记是否与区域流行病学感染数据相关。
纳入 2012 年 1 月至 2022 年 6 月期间在急诊室进行的所有胸部成像报告（64046 张 X 光片；27705 张 CT 片）。使用基于正则表达式的文本搜索算法，如果报告中有描述，将报告标记为肺部感染阳性/阴性。根据日历日期，将区域每周流感样疾病（ILI）咨询（2013 年 10 月至 2022 年 3 月）、COVID-19 病例和住院治疗（2020 年 2 月至 2022 年 6 月）的数据与报告标签相匹配。计算肺部感染检测的阳性率、NNI 以及与流感/COVID-19 数据的相关性。
在 2012 年至 2020 年 2 月期间，每年通过胸部 X 光片检测肺部感染的阳性率为 10.8-16.8%（NNI 为 6.0-9.3）。每月检测计数的明显季节性变化（冬季 102.3；夏季 61.5；p<.001）与区域 ILI 咨询中度相关（每周数据 r=0.45；p<.001）。对于 2020-2021 年，与 2012-2019 年相比，通过胸部 CT 检测到的每月肺部感染数量增加到 64-234（阳性率为 23.0-26.7%，NNI 为 3.7-4.3）。区域 COVID-19 疫情与 2020-2022 年的阳性肺部感染 CT 曲线中度相关（每周新增病例：r=0.53；住院治疗：r=0.65；p<.001）。
放射学报告的文本挖掘允许自动提取诊断。它提供了一种计算所需成像次数的指标，并实时跟踪诊断的趋势，即肺部感染的季节性和流行过程。
数字化标记放射学报告代表了以前被忽视的数据，可能有助于自动疾病跟踪，评估医生对放射学检查的临床推理，并作为医院工作流程优化的可操作数据。
放射学报告通常不可机器读取，但可以使用基于正则表达式的文本搜索算法自动标记其中包含的诊断。
胸部 X 光片报告中肺部感染阳性与区域 ILI 咨询中度相关（每周数据；r=0.45；p<.001），胸部 CT 报告与区域 COVID-19 大流行的进程相关（新增病例：r=0.53；住院治疗：r=0.65；p<.001）。
将放射学报告转换为数据标签为自动疾病跟踪提供了一个指标，评估了医生的临床推理，并可以作为工作流程优化的可操作数据。
