ArticleJournal of clinical medicine2023
Can Dialysis Patients Identify and Diagnose Pulmonary Congestion Using Self-Lung Ultrasound?
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
- AI-enhanced guidance demonstrated improvement in novices' Apical-4-chamber and Apical-5-chamber views.BMC medical education · 2025Trial
- Multimodal Diagnostic Ultrasound for Congestion, Perfusion, and Ultrafiltration Tolerance in Maintenance Hemodialysis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- AI-Enhanced POCUS in Emergency Care.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence-Enabled Point-of-Care Echocardiography: Bringing Precision Imaging to the Bedside.Current atherosclerosis reports · 2025Review
- Continuing professional development for primary care physicians: a pre-post analysis of a focused abdominal point-of-care ultrasound pilot training.BMC medical education · 2025Article
- Dialysis Patients' Evaluation of Lung Edema at Home Using a Mobile Phone Tele-Ultrasound Application: A Pilot Study.Journal of clinical medicine · 2025Article
- Continuing professional development for primary care physicians: a pre-post study on lung point-of-care ultrasound curriculum.BMC medical education · 2024Article
- Revolutionizing Bladder Health: Artificial-Intelligence-Powered Automatic Measurement of Bladder Volume Using Two-Dimensional Ultrasound.Diagnostics (Basel, Switzerland) · 2024Article
- The effect of real-time EF automatic tool on cardiac ultrasound performance among medical students.PloS one · 2024Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith 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.
methodsThis 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.
resultsA 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.
conclusionsOur 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.
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