Liu Jeffrey, Varghese Bino, Taravat Farzaneh, Eibschutz Liesl S, Gholamrezanezhad Ali
Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Diagnostics (Basel). 2022 May 30;12(6):1351. doi: 10.3390/diagnostics12061351.
Imaging in the emergent setting carries high stakes. With increased demand for dedicated on-site service, emergency radiologists face increasingly large image volumes that require rapid turnaround times. However, novel artificial intelligence (AI) algorithms may assist trauma and emergency radiologists with efficient and accurate medical image analysis, providing an opportunity to augment human decision making, including outcome prediction and treatment planning. While traditional radiology practice involves visual assessment of medical images for detection and characterization of pathologies, AI algorithms can automatically identify subtle disease states and provide quantitative characterization of disease severity based on morphologic image details, such as geometry and fluid flow. Taken together, the benefits provided by implementing AI in radiology have the potential to improve workflow efficiency, engender faster turnaround results for complex cases, and reduce heavy workloads. Although analysis of AI applications within abdominopelvic imaging has primarily focused on oncologic detection, localization, and treatment response, several promising algorithms have been developed for use in the emergency setting. This article aims to establish a general understanding of the AI algorithms used in emergent image-based tasks and to discuss the challenges associated with the implementation of AI into the clinical workflow.
急诊环境中的影像学检查风险很高。随着对现场专用服务需求的增加,急诊放射科医生面临着越来越多的图像,需要快速周转时间。然而,新型人工智能(AI)算法可能有助于创伤和急诊放射科医生进行高效、准确的医学图像分析,为增强人类决策提供机会,包括结果预测和治疗规划。虽然传统的放射学实践涉及对医学图像进行视觉评估以检测和表征病变,但人工智能算法可以自动识别细微的疾病状态,并根据形态学图像细节(如几何形状和流体流动)提供疾病严重程度的定量表征。综上所述,在放射学中应用人工智能所带来的好处有可能提高工作流程效率,为复杂病例带来更快的周转结果,并减轻繁重的工作量。虽然腹部盆腔影像学中人工智能应用的分析主要集中在肿瘤检测、定位和治疗反应方面,但已经开发了几种有前景的算法用于急诊环境。本文旨在建立对急诊基于图像任务中使用的人工智能算法的总体理解,并讨论将人工智能应用于临床工作流程所面临的挑战。