Cotena Martina, Ayobi Angela, Zuchowski Colin, Junn Jacqueline C, Weinberg Brent D, Chang Peter D, Chow Daniel S, Soun Jennifer E, Roca-Sogorb Mar, Chaibi Yasmina, Quenet Sarah
Avicenna.AI, 375 Avenue du Mistral, 13600 La Ciotat, France.
Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1364 Clifton Road Northeast, Suite BG20, Atlanta, GA 30322, USA.
Diagnostics (Basel). 2024 Nov 28;14(23):2689. doi: 10.3390/diagnostics14232689.
Acute aortic dissection (AD) is a life-threatening condition in which early detection can significantly improve patient outcomes and survival. This study evaluates the clinical benefits of integrating a deep learning (DL)-based application for the automated detection and prioritization of AD on chest CT angiographies (CTAs) with a focus on the reduction in the scan-to-assessment time (STAT) and interpretation time (IT).
This retrospective Multi-Reader Multi-Case (MRMC) study compared AD detection with and without artificial intelligence (AI) assistance. The ground truth was established by two U.S. board-certified radiologists, while three additional expert radiologists served as readers. Each reader assessed the same CTAs in two phases: assessment unaided by AI assistance (pre-AI arm) and, after a 1-month washout period, assessment aided by device outputs (post-AI arm). STAT and IT metrics were compared between the two arms.
This study included 285 CTAs (95 per reader, per arm) with a mean patient age of 58.5 years ±14.7 (SD), of which 52% were male and 37% had a prevalence of AD. AI assistance significantly reduced the STAT for detecting 33 true positive AD cases from 15.84 min (95% CI: 13.37-18.31 min) without AI to 5.07 min (95% CI: 4.23-5.91 min) with AI, representing a 68% reduction ( < 0.01). The IT also reduced significantly from 21.22 s (95% CI: 19.87-22.58 s) without AI to 14.17 s (95% CI: 13.39-14.95 s) with AI ( < 0.05).
The integration of a DL-based algorithm for AD detection on chest CTAs significantly reduces both the STAT and IT. By prioritizing urgent cases, the AI-assisted approach outperforms the standard First-In, First-Out (FIFO) workflow.
急性主动脉夹层（AD）是一种危及生命的疾病，早期检测可显著改善患者预后和生存率。本研究评估了将基于深度学习（DL）的应用程序集成到胸部CT血管造影（CTA）上用于AD自动检测和优先级排序的临床益处，重点关注扫描至评估时间（STAT）和解读时间（IT）的减少。
这项回顾性多读者多病例（MRMC）研究比较了有无人工智能（AI）辅助下的AD检测。由两名美国董事会认证的放射科医生确定基本事实，另外三名专家放射科医生担任读者。每位读者分两个阶段评估相同的CTA：在无AI辅助的情况下进行评估（AI前组），以及在1个月的洗脱期后，在设备输出辅助下进行评估（AI后组）。比较两组之间的STAT和IT指标。
本研究纳入了285例CTA（每位读者每组95例），患者平均年龄为58.5岁±14.7（标准差），其中52%为男性，37%患有AD。AI辅助显著降低了检测33例真阳性AD病例的STAT，从无AI时的15.84分钟（95%CI：13.37 - 18.31分钟）降至有AI时的5.07分钟（95%CI：4.23 - 5.91分钟），减少了68%（<0.01）。IT也从无AI时的21.22秒（95%CI：19.87 - 22.58秒）显著降至有AI时的14.17秒（95%CI：13.39 - 14.95秒）（<0.05）。
将基于DL的算法集成到胸部CTA上用于AD检测可显著降低STAT和IT。通过对紧急病例进行优先级排序，AI辅助方法优于标准的先进先出（FIFO）工作流程。
