人工智能可以通过远程指导的护理点超声辅助诊断吗?利用肺超声评估新冠肺炎诊断新计算机算法的初步研究。

IF 3.1 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE AI (Basel, Switzerland) Pub Date : 2023-12-01 Epub Date: 2023-10-10 DOI:10.3390/ai4040044
Laith R Sultan, Allison Haertter, Maryam Al-Hasani, George Demiris, Theodore W Cary, Yale Tung-Chen, Chandra M Sehgal
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引用次数: 0

摘要

随着2019冠状病毒病(新冠肺炎)大流行,对远程监测技术的需求越来越大,以减少患者和提供者的接触。一个潜力越来越大的领域是远程引导超声,远程医疗和护理点超声(POCUS)融合在一起,创造了这种新的范围。远程引导POCUS可以最大限度地减少工作人员的接触,同时在床边程序中保护患者的安全和监督。在本文中,我们建议使用人工智能技术支持的远程引导POCUS,由无经验的人员对新冠肺炎患者进行远程监测,包括患者自己进行自我监测。我们的假设是,人工智能技术可以通过使用POCUS设备来促进对新冠肺炎患者的远程监测,即使是由未经正式医疗培训的个人操作。为了实现这一目标,我们使用基于计算机的系统进行了初步分析,以评估具有不同临床背景的用户的表现,该系统用于使用肺部超声检测新冠肺炎。分析的目的是强调所提出的人工智能技术在提高诊断性能方面的潜力,特别是对于经验较少的用户。
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Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound.

With the 2019 coronavirus disease (COVID-19) pandemic, there is an increasing demand for remote monitoring technologies to reduce patient and provider exposure. One field that has an increasing potential is teleguided ultrasound, where telemedicine and point-of-care ultrasound (POCUS) merge to create this new scope. Teleguided POCUS can minimize staff exposure while preserving patient safety and oversight during bedside procedures. In this paper, we propose the use of teleguided POCUS supported by AI technologies for the remote monitoring of COVID-19 patients by non-experienced personnel including self-monitoring by the patients themselves. Our hypothesis is that AI technologies can facilitate the remote monitoring of COVID-19 patients through the utilization of POCUS devices, even when operated by individuals without formal medical training. In pursuit of this goal, we performed a pilot analysis to evaluate the performance of users with different clinical backgrounds using a computer-based system for COVID-19 detection using lung ultrasound. The purpose of the analysis was to emphasize the potential of the proposed AI technology for improving diagnostic performance, especially for users with less experience.

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CiteScore
7.20
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审稿时长
11 weeks
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