Schneider Eyal, Maimon Netta, Hasidim Ariel, Shnaider Alla, Migliozzi Gabrielle, Haviv Yosef S, Halpern Dor, Abu Ganem Basel, Fuchs Lior
Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 7747629, Israel.
Department of Nephrology, Soroka University Medical Center, Beer-Sheva 8457108, Israel.
J Clin Med. 2023 Jun 2;12(11):3829. doi: 10.3390/jcm12113829.
With the recent developments in automated tools, smaller and cheaper machines for lung ultrasound (LUS) are leading us toward the potential to conduct POCUS tele-guidance for the early detection of pulmonary congestion. This study aims to evaluate the feasibility and accuracy of a self-lung ultrasound study conducted by hemodialysis (HD) patients to detect pulmonary congestion, with and without artificial intelligence (AI)-based automatic tools.
This prospective pilot study was conducted between November 2020 and September 2021. Nineteen chronic HD patients were enrolled in the Soroka University Medical Center (SUMC) Dialysis Clinic. First, we examined the patient's ability to obtain a self-lung US. Then, we used interrater reliability (IRR) to compare the self-detection results reported by the patients to the observation of POCUS experts and an ultrasound (US) machine with an AI-based automatic B-line counting tool. All the videos were reviewed by a specialist blinded to the performer. We examined their agreement degree using the weighted Cohen's kappa (Kw) index.
A total of 19 patients were included in our analysis. We found moderate to substantial agreement between the POCUS expert review and the automatic counting both when the patient performed the LUS (Kw = 0.49 [95% CI: 0.05-0.93]) and when the researcher performed it (Kw = 0.67 [95% CI: 0.67-0.67]). Patients were able to place the probe in the correct position and present a lung image well even weeks from the teaching session, but did not show good abilities in correctly saving or counting B-lines compared to an expert or an automatic counting tool.
Our results suggest that LUS self-monitoring for pulmonary congestion can be a reliable option if the patient's count is combined with an AI application for the B-line count. This study provides insight into the possibility of utilizing home US devices to detect pulmonary congestion, enabling patients to have a more active role in their health care.
随着自动化工具的最新发展，用于肺部超声（LUS）的更小、更便宜的机器使我们有可能开展即时超声（POCUS）远程指导，以早期发现肺充血。本研究旨在评估血液透析（HD）患者在使用和不使用基于人工智能（AI）的自动工具的情况下，自行进行肺部超声检查以检测肺充血的可行性和准确性。
这项前瞻性试点研究于2020年11月至2021年9月进行。19名慢性HD患者被纳入索罗卡大学医学中心（SUMC）透析诊所。首先，我们检查了患者获取自行肺部超声检查的能力。然后，我们使用评分者间信度（IRR）将患者报告的自我检测结果与POCUS专家的观察结果以及带有基于AI的自动B线计数工具的超声（US）机器的结果进行比较。所有视频均由对执行者不知情的专家进行审查。我们使用加权科恩kappa（Kw）指数检查他们的一致程度。
共有19名患者纳入我们的分析。我们发现，当患者进行LUS检查时（Kw = 0.49 [95% CI：0.05 - 0.93]）以及研究者进行检查时（Kw = 0.67 [95% CI：0.67 - 0.67]），POCUS专家审查与自动计数之间存在中度至高度一致性。即使在教学课程数周后，患者仍能够将探头放置在正确位置并很好地呈现肺部图像，但与专家或自动计数工具相比，在正确保存或计数B线方面能力不佳。
我们的结果表明，如果将患者的计数与用于B线计数的AI应用相结合，LUS自我监测肺充血可能是一种可靠的选择。本研究为利用家用超声设备检测肺充血的可能性提供了见解，使患者能够在自身医疗保健中发挥更积极的作用。
