Kaviani Parisa, Digumarthy Subba R, Bizzo Bernardo C, Reddy Bhargava, Tadepalli Manoj, Putha Preetham, Jagirdar Ammar, Ebrahimian Shadi, Kalra Mannudeep K, Dreyer Keith J
Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
MGH & BWH Center for Clinical Data Science, Boston, MA 02114, USA.
Diagnostics (Basel). 2022 Aug 28;12(9):2086. doi: 10.3390/diagnostics12092086.
: We assessed whether a CXR AI algorithm was able to detect missed or mislabeled chest radiograph (CXR) findings in radiology reports. : We queried a multi-institutional radiology reports search database of 13 million reports to identify all CXR reports with addendums from 1999-2021. Of the 3469 CXR reports with an addendum, a thoracic radiologist excluded reports where addenda were created for typographic errors, wrong report template, missing sections, or uninterpreted signoffs. The remaining reports contained addenda (279 patients) with errors related to side-discrepancies or missed findings such as pulmonary nodules, consolidation, pleural effusions, pneumothorax, and rib fractures. All CXRs were processed with an AI algorithm. Descriptive statistics were performed to determine the sensitivity, specificity, and accuracy of the AI in detecting missed or mislabeled findings. : The AI had high sensitivity (96%), specificity (100%), and accuracy (96%) for detecting all missed and mislabeled CXR findings. The corresponding finding-specific statistics for the AI were nodules (96%, 100%, 96%), pneumothorax (84%, 100%, 85%), pleural effusion (100%, 17%, 67%), consolidation (98%, 100%, 98%), and rib fractures (87%, 100%, 94%). : The CXR AI could accurately detect mislabeled and missed findings. : The CXR AI can reduce the frequency of errors in detection and side-labeling of radiographic findings.
我们评估了一种胸部X光人工智能算法是否能够检测放射学报告中遗漏或错误标注的胸部X光（CXR）检查结果。我们查询了一个包含1300万份报告的多机构放射学报告搜索数据库，以识别1999年至2021年期间所有带有补遗的CXR报告。在3469份带有补遗的CXR报告中，一位胸科放射科医生排除了因排版错误、错误报告模板、缺失部分或未解读签字而创建补遗的报告。其余报告包含与侧别差异或遗漏检查结果相关的补遗（279名患者），如肺结节、实变、胸腔积液、气胸和肋骨骨折。所有CXR均采用人工智能算法进行处理。进行描述性统计以确定人工智能在检测遗漏或错误标注检查结果方面的敏感性、特异性和准确性。人工智能在检测所有遗漏和错误标注的CXR检查结果方面具有高敏感性（96%）、特异性（100%）和准确性（96%）。人工智能针对特定检查结果的相应统计数据为结节（96%，100%，96%）、气胸（84%，100%，85%）、胸腔积液（100%，17%，67%）、实变（98%，100%，98%）和肋骨骨折（87%，100%，94%）。胸部X光人工智能能够准确检测错误标注和遗漏的检查结果。胸部X光人工智能可以减少放射学检查结果检测和侧别标注中的错误频率。
