用于腹主动脉瘤超声筛查的深度学习前瞻性临床评估

IF 12.4 1区 医学 Q1 HEALTH CARE SCIENCES & SERVICES NPJ Digital Medicine Pub Date : 2024-10-15 DOI:10.1038/s41746-024-01269-4
I-Min Chiu, Tien-Yu Chen, You-Cheng Zheng, Xin-Hong Lin, Fu-Jen Cheng, David Ouyang, Chi-Yung Cheng
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摘要

由于接受超声诊断的机会有限,腹主动脉瘤(AAA)往往在破裂前一直未被发现。这项试验对深度学习(DL)算法进行了评估,以指导没有超声波检查经验的新手护士进行 AAA 筛查。在深度学习物体检测算法的辅助下,10 名护士分别对 65 岁以上的患者进行了 15 次扫描,并与医生进行的扫描进行了比较。超声扫描质量是主要结果,由三位盲法专家医师进行评估。在 184 名患者中,DL 引导的新手在 87.5% 的病例中达到了适当的扫描质量,与医生 91.3% 的扫描质量相当(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 毫米。这项研究表明,DL 引导的 POCUS 有可能实现 AAA 筛查的民主化,其效果可与经验丰富的医生媲美,并能提高早期检测率。
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Prospective clinical evaluation of deep learning for ultrasonographic screening of abdominal aortic aneurysms
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.
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来源期刊
CiteScore
25.10
自引率
3.30%
发文量
170
审稿时长
15 weeks
期刊介绍: npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.
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