AIM To analyse the performance of a deep-learning (DL) algorithm currently deployed as diagnostic decision support software in two NHS Trusts used to identify normal chest x-rays in active clinical pathways. MATERIALS AND METHODS A DL algorithm has been deployed in Somerset NHS Foundation Trust (SFT) since December 2022, and at Calderdale & Huddersfield NHS Foundation Trust (CHFT) since March 2023. The algorithm was developed and trained prior to deployment, and is used to assign abnormality scores to each GP-requested chest x-ray (CXR). The algorithm classifies a subset of examinations with the lowest abnormality scores as High Confidence Normal (HCN), and displays this result to the Trust. This two-site study includes 4,654 CXR continuous examinations processed by the algorithm over a six-week period. RESULTS When classifying 20.0% of assessed examinations (930) as HCN, the model classified exams with a negative predictive value (NPV) of 0.96. There were 0.77% of examinations (36) classified incorrectly as HCN, with none of the abnormalities considered clinically significant by auditing radiologists. The DL software maintained fast levels of service to clinicians, with results returned to Trusts in a mean time of 7.1 seconds. CONCLUSION The DL algorithm performs with a low rate of error and is highly effective as an automated diagnostic decision support tool, used to autonomously report a subset of CXRs as normal with high confidence. Removing 20% of all CXRs reduces workload for reporters and allows radiology departments to focus resources elsewhere.

翻译：目的 分析一种目前作为诊断决策支持软件部署于两家NHS信托机构、用于在活跃临床路径中识别正常胸片的深度学习算法的性能。材料与方法 该深度学习算法自2022年12月起部署于萨默塞特NHS基金会信托，自2023年3月起部署于卡尔德代尔与哈德斯菲尔德NHS基金会信托。该算法在部署前已开发并训练完成，用于对全科医生申请的每张胸片进行异常评分。算法将异常评分最低的部分检查分类为"高置信度正常"，并将此结果反馈给信托机构。这项双中心研究纳入了算法在六周内连续处理的4,654例胸片检查。结果 当将20.0%的评估检查（930例）分类为高置信度正常时，模型的阴性预测值为0.96。0.77%的检查（36例）被错误分类为高置信度正常，经审计放射科医师确认其中无具有临床意义的异常。该深度学习软件为临床医生保持了快速服务水平，平均7.1秒内将结果返回至信托机构。结论 该深度学习算法错误率低，作为自动化诊断决策支持工具效果显著，可自主将部分胸片报告为高置信度正常。通过移除20%的胸片，减少了报告人员的工作负荷，使放射科能够将资源集中于其他领域。