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利用人工智能的能力:接受过新手神经内科培训的操作员在急性脑损伤患者中进行心脏 POUS。

Leveraging the Capabilities of AI: Novice Neurology-Trained Operators Performing Cardiac POCUS in Patients with Acute Brain Injury.

机构信息

Department of Neurology, Weill Cornell Medicine, New York, NY, USA.

Department of Anesthesiology, Weill Cornell Medicine, New York, NY, USA.

出版信息

Neurocrit Care. 2024 Oct;41(2):523-532. doi: 10.1007/s12028-024-01953-z. Epub 2024 Mar 20.

Abstract

BACKGROUND

Cardiac point-of-care ultrasound (cPOCUS) can aid in the diagnosis and treatment of cardiac disorders. Such disorders can arise as complications of acute brain injury, but most neurologic intensive care unit (NICU) providers do not receive formal training in cPOCUS. Caption artificial intelligence (AI) uses a novel deep learning (DL) algorithm to guide novice cPOCUS users in obtaining diagnostic-quality cardiac images. The primary objective of this study was to determine how often NICU providers with minimal cPOCUS experience capture quality images using DL-guided cPOCUS as well as the association between DL-guided cPOCUS and change in management and time to formal echocardiograms in the NICU.

METHODS

From September 2020 to November 2021, neurology-trained physician assistants, residents, and fellows used DL software to perform clinically indicated cPOCUS scans in an academic tertiary NICU. Certified echocardiographers evaluated each scan independently to assess the quality of images and global interpretability of left ventricular function, right ventricular function, inferior vena cava size, and presence of pericardial effusion. Descriptive statistics with exact confidence intervals were used to calculate proportions of obtained images that were of adequate quality and that changed management. Time to first adequate cardiac images (either cPOCUS or formal echocardiography) was compared using a similar population from 2018.

RESULTS

In 153 patients, 184 scans were performed for a total of 943 image views. Three certified echocardiographers deemed 63.4% of scans as interpretable for a qualitative assessment of left ventricular size and function, 52.6% of scans as interpretable for right ventricular size and function, 34.8% of scans as interpretable for inferior vena cava size and variability, and 47.2% of scans as interpretable for the presence of pericardial effusion. Thirty-seven percent of screening scans changed management, most commonly adjusting fluid goals (81.2%). Time to first adequate cardiac images decreased significantly from 3.1 to 1.7 days (p < 0.001).

CONCLUSIONS

With DL guidance, neurology providers with minimal to no cPOCUS training were often able to obtain diagnostic-quality cardiac images, which informed management changes and significantly decreased time to cardiac imaging.

摘要

背景

心脏即时超声(cPOCUS)可辅助心脏疾病的诊断和治疗。此类疾病可能是急性脑损伤的并发症,但大多数神经重症监护病房(NICU)的医护人员并未接受过 cPOCUS 的正规培训。基于人工智能的字幕(caption)使用新颖的深度学习(DL)算法,指导新手 cPOCUS 用户获取具有诊断质量的心脏图像。本研究的主要目的是确定经验有限的 NICU 医护人员在使用基于 DL 的 cPOCUS 时,获得高质量图像的频率,以及基于 DL 的 cPOCUS 与管理变化和 NICU 中正式超声心动图检查时间之间的关联。

方法

从 2020 年 9 月至 2021 年 11 月,神经科培训的医师助理、住院医师和研究员在学术性三级 NICU 中使用 DL 软件进行临床指征的 cPOCUS 扫描。认证的超声心动图医师独立评估每个扫描,以评估图像质量以及左心室功能、右心室功能、下腔静脉大小和心包积液的整体可解读性。使用精确置信区间的描述性统计计算出足够质量的获得图像的比例,以及改变管理的比例。使用来自 2018 年的相似人群比较首次获得足够心脏图像(cPOCUS 或正式超声心动图)的时间。

结果

在 153 名患者中,共进行了 184 次扫描,总共获得了 943 个图像视图。3 名认证超声心动图医师认为,63.4%的扫描可定性评估左心室大小和功能,52.6%的扫描可定性评估右心室大小和功能,34.8%的扫描可定性评估下腔静脉大小和变异性,47.2%的扫描可定性评估心包积液的存在。37%的筛查扫描改变了管理方案,最常见的是调整液体目标(81.2%)。首次获得足够心脏图像的时间从 3.1 天显著缩短至 1.7 天(p<0.001)。

结论

在基于 DL 的指导下,经验有限或无经验的神经科医护人员通常能够获得具有诊断质量的心脏图像,这有助于管理决策的改变,并显著缩短了心脏影像学检查的时间。

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