CheXED: Comparison of a Deep Learning Model to a Clinical Decision Support System for Pneumonia in the Emergency Department.
CheXED: Comparison of a Deep Learning Model to a Clinical Decision Support System for Pneumonia in the Emergency Department.
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DOI：
10.1097/rti.0000000000000622
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发表时间：
2022-05-01
影响因子：
3.3
通讯作者：
Dean NC
中科院分区：
文献类型：
--
作者：
Irvin JA;Pareek A;Long J;Rajpurkar P;Eng DK;Khandwala N;Haug PJ;Jephson A;Conner KE;Gordon BH;Rodriguez F;Ng AY;Lungren MP;Dean NC
肺炎患者经常出现在急诊科，需要及时诊断和治疗。诊断和管理肺炎的临床决策支持系统通常用于急诊科，以改善患者护理。本研究的目的是探讨用于检测放射学肺炎和胸腔积液的深度学习模型是否可以改善20个急诊科的肺炎管理临床决策支持系统（ePNa）的功能。在这项回顾性队列研究中，使用了来自6,551名急诊科患者的7,434个先前胸部x线片研究数据集来开发和验证深度学习模型，以识别x线片肺炎、胸膜积液和多叶性肺炎的证据。根据三名放射科医生的裁决解释评估模型的性能，并与ePNa使用的放射学报告的自然语言处理性能进行比较。深度学习模型检测放射性肺炎的受试者工作特征曲线下面积为0.833 (95% CI 0.795, 0.868)，检测胸腔积液的受试者工作特征曲线下面积为0.939 (95% CI 0.911, 0.962)，识别多叶性肺炎的受试者工作特征曲线下面积为0.847 （95% CI 0.800, 0.890）。在所有三项任务中，与ePNa相比，该模型与经裁定的放射科医生解释达成了更高的一致性。与ePNa临床决策支持系统相比，深度学习模型在检测放射学肺炎和相关发现方面与放射科医生的一致性更高。将深度学习模型纳入肺炎临床决策支持系统可以提高诊断性能并改善肺炎管理。
Patients with pneumonia often present to the emergency department and require prompt diagnosis and treatment. Clinical decision support systems for the diagnosis and management of pneumonia are commonly utilized in emergency departments to improve patient care. The purpose of this study is to investigate whether a deep learning model for detecting radiographic pneumonia and pleural effusions can improve functionality of a clinical decision support system for pneumonia management (ePNa) operating in 20 emergency departments. In this retrospective cohort study, a dataset of 7,434 prior chest radiographic studies from 6,551 emergency department patients was used to develop and validate a deep learning model to identify radiographic pneumonia, pleural effusions, and evidence of multilobar pneumonia. Model performance was evaluated against three radiologists’ adjudicated interpretation and compared to performance of the natural language processing of radiology reports used by ePNa. The deep learning model achieved an area under the receiver operating characteristic curve of 0.833 (95% CI 0.795, 0.868) for detecting radiographic pneumonia, 0.939 (95% CI 0.911, 0.962) for detecting pleural effusions, and 0.847 (95% CI 0.800, 0.890) for identifying multilobar pneumonia. On all three tasks, the model achieved higher agreement with the adjudicated radiologist interpretation compared to ePNa. A deep learning model demonstrated higher agreement with radiologists than the ePNa clinical decision support system in detecting radiographic pneumonia and related findings. Incorporating deep learning models into pneumonia clinical decision support systems could enhance diagnostic performance and improve pneumonia management.