Julyana Dantas, Giovana Barros, Antonio Mutarelli, Caroline Dagostin, Pedro Romeiro, Giulia Almirón, Nicole Felix, Agostinho Pinheiro, Matheus A Bannach
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引用次数: 0
Abstract
Background and purpose: Large vessel occlusion (LVO) accounts for a third of all ischemic strokes. Artificial intelligence (AI) has shown good accuracy in identifying LVOs on computed tomography angiograms (CTA). We sought to analyze whether AI-adjudicated CTA improves workflow times and clinical outcomes in patients with confirmed LVOs.
Materials and methods: We systematically searched PubMed, Embase, and Web of Science for studies comparing initial radiological assessment assisted by AI softwares versus standard assessment of patients with acute LVO strokes. Results were pooled using a random-effects model as mean differences for continuous outcomes or odds ratio (OR) for dichotomous outcomes, along with 95% confidence intervals (CI).
Results: We included 9 studies comprising 1,270 patients, of whom 671 (52.8%) had AI-assisted radiological assessment. AI consistently improved treatment times when compared to standard assessment, as evidenced by a mean reduction of 20.55 minutes in door-to-groin time (95% CI -36.69 to -4.42 minutes; p<0.01) and a reduction of 14.99 minutes in CTA to reperfusion (95% CI -28.45 to -1.53 minutes; p=0.03). Functional independence, defined as a modified Rankin scale 0-2, occurred at similar rates in the AI-supported group and with the standard workflow (OR 1.27; 95% CI 0.92 to 1.76; p=0.14), as did mortality (OR 0.71; 95% CI 0.27 to 1.88; p=0.49).
Conclusions: The incorporation of AI softwares for LVO detection in acute ischemic stroke enhanced workflow efficiency and was associated with decreased time to treatment. However, AI did not improve clinical outcomes as compared with standard assessment.
背景和目的:大血管闭塞(LVO)占缺血性卒中的三分之一。人工智能(AI)在计算机断层扫描血管造影(CTA)上识别lvo方面显示出良好的准确性。我们试图分析人工智能判定的CTA是否改善了确诊lvo患者的工作流程时间和临床结果。材料和方法:我们系统地检索了PubMed、Embase和Web of Science,以比较人工智能软件辅助下的初始放射学评估与急性左心室卒中患者的标准评估。使用随机效应模型作为连续结果的平均差异或二分类结果的优势比(or)以及95%置信区间(CI)对结果进行汇总。结果:我们纳入了9项研究,包括1,270例患者,其中671例(52.8%)进行了人工智能辅助放射学评估。与标准评估相比,人工智能持续改善了治疗时间,从门静脉到腹股沟的时间平均缩短了20.55分钟(95% CI -36.69至-4.42分钟;结论:人工智能软件在急性缺血性卒中LVO检测中的应用提高了工作效率,并缩短了治疗时间。然而,与标准评估相比,人工智能并没有改善临床结果。
期刊介绍:
Neuroradiology aims to provide state-of-the-art medical and scientific information in the fields of Neuroradiology, Neurosciences, Neurology, Psychiatry, Neurosurgery, and related medical specialities. Neuroradiology as the official Journal of the European Society of Neuroradiology receives submissions from all parts of the world and publishes peer-reviewed original research, comprehensive reviews, educational papers, opinion papers, and short reports on exceptional clinical observations and new technical developments in the field of Neuroimaging and Neurointervention. The journal has subsections for Diagnostic and Interventional Neuroradiology, Advanced Neuroimaging, Paediatric Neuroradiology, Head-Neck-ENT Radiology, Spine Neuroradiology, and for submissions from Japan. Neuroradiology aims to provide new knowledge about and insights into the function and pathology of the human nervous system that may help to better diagnose and treat nervous system diseases. Neuroradiology is a member of the Committee on Publication Ethics (COPE) and follows the COPE core practices. Neuroradiology prefers articles that are free of bias, self-critical regarding limitations, transparent and clear in describing study participants, methods, and statistics, and short in presenting results. Before peer-review all submissions are automatically checked by iThenticate to assess for potential overlap in prior publication.