Abdominal aortic aneurysm (AAA) often remains undetected until rupture due to limited access to diagnostic ultrasound. This trial evaluated a deep learning (DL) algorithm to guide AAA screening by novice nurses with no prior ultrasonography experience. Ten nurses performed 15 scans each on patients over 65, assisted by a DL object detection algorithm, and compared against physician-performed scans. Ultrasound scan quality, assessed by three blinded expert physicians, was the primary outcome. Among 184 patients, DL-guided novices achieved adequate scan quality in 87.5% of cases, comparable to the 91.3% by physicians (p = 0.310). The DL model predicted AAA with an AUC of 0.975, 100% sensitivity, and 97.8% specificity, with a mean absolute error of 2.8 mm in predicting aortic width compared to physicians. This study demonstrates that DL-guided POCUS has the potential to democratize AAA screening, offering performance comparable to experienced physicians and improving early detection.

由于诊断性超声检查的机会有限，腹主动脉瘤 （AAA） 在破裂之前通常未被发现。该试验评估了一种深度学习 （DL） 算法，以指导之前没有超声检查经验的新手护士进行 AAA 筛查。10 名护士在 DL 对象检测算法的协助下，对 65 岁以上的患者每人进行了 15 次扫描，并与医生进行的扫描进行了比较。由 3 名盲法专家医生评估的超声扫描质量是主要结局。在 184 名患者中，DL 指导的新手在 87.5% 的病例中取得了足够的扫描质量，与医生的 91.3% 相当 （p = 0.310）。DL 模型预测 AAA 的 AUC 为 0.975，灵敏度为 100%，特异性为 97.8%，与医生相比，预测主动脉宽度的平均绝对误差为 2.8 mm。这项研究表明，DL 引导的 POCUS 有可能使 AAA 筛查大众化，提供与经验丰富的医生相当的性能并改善早期检测。