ArticleBMC nephrology2022
Automated lung ultrasound image assessment using artificial intelligence to identify fluid overload in dialysis patients.
Article in BMC nephrology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Artificial intelligence for lung ultrasound interpretation: a systematic review.Frontiers in radiology · 2026Pooled it
- Lung ultrasound for fluid assessment in patients receiving dialysis-a systematic review.Journal of nephrology · 2025Pooled it
- Artificial Intelligence in Point-of-Care Ultrasound: Domains, Barriers and a Framework for Future Development.Journal of the American College of Emergency Physicians open · 2026Article
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- Multimodal Diagnostic Ultrasound for Congestion, Perfusion, and Ultrafiltration Tolerance in Maintenance Hemodialysis: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Management Strategies for Congestive Heart Failure in Chronic Kidney Disease: Narrative Review.Biomedicines · 2026Review
- Computer vision-based support system for B-line detection in lung ultrasound.Frontiers in digital health · 2026Article
- Construction and validation of a nomogram to predict 1-year mortality risk in patients with HIV/AIDS undergoing maintenance hemodialysis.Renal failure · 2025Article
- Unmasking fluid overload in children on peritoneal dialysis: a multimodal diagnostic approach.Pediatric nephrology (Berlin, Germany) · 2025Article
- Correlation between positive fluid balances with venous excess ultrasound scores and lung ultrasound in critically ill children with fluid overload.BMC pediatrics · 2025Article
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Performance Comparison of a Neural Network and a Regression Linear Model for Predictive Maintenance in Dialysis Machine Components.Bioengineering (Basel, Switzerland) · 2025Article
- The diagnostic performance of automatic B-lines detection for evaluating pulmonary edema in the emergency department among novice point-of-care ultrasound practitioners.Emergency radiology · 2025Article
- Critical care ultrasound: development, evolution, current and evolving clinical concepts in critical care medicine.Frontiers in medicine · 2025Review
- Artificial Intelligence-Based Left Ventricular Ejection Fraction by Medical Students for Mortality and Readmission Prediction.Diagnostics (Basel, Switzerland) · 2024Article
- Ultrasonographic Applications of Novel Technologies and Artificial Intelligence in Critically Ill Patients.Journal of personalized medicine · 2024Review
- Can Crowdsourced Annotations Improve AI-based Congestion Scoring For Bedside Lung Ultrasound?Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Processing Ultrasound Scans of the Inferior Vena Cava: Techniques and Applications.Bioengineering (Basel, Switzerland) · 2023Review
- Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound.IEEE journal of biomedical and health informatics · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFluid assessment is challenging, and fluid overload poses a significant problem among dialysis patients, with pulmonary oedema being the most serious consequence. Our study aims to develop a simple objective fluid assessment strategy using lung ultrasound (LUS) and artificial intelligence (AI) to assess the fluid status of dialysis patients.
methodsThis was a single-centre study of 76 hemodialysis and peritoneal dialysis patients carried out between July 2020 to May 2022. The fluid status of dialysis patients was assessed via a simplified 8-point LUS method using a portable handheld ultrasound device (HHUSD), clinical examination and bioimpedance analysis (BIA). The primary outcome was the performance of 8-point LUS using a portable HHUSD in diagnosing fluid overload compared to physical examination and BIA. The secondary outcome was to develop and validate a novel AI software program to quantify B-line count and assess the fluid status of dialysis patients.
resultsOur study showed a moderate correlation between LUS B-line count and fluid overload assessed by clinical examination (r = 0.475, p < 0.001) and BIA (r = 0.356. p < 0.001). The use of AI to detect B-lines on LUS in our study for dialysis patients was shown to have good agreement with LUS B lines observed by physicians; (r = 0.825, p < 0.001) for the training dataset and (r = 0.844, p < 0.001) for the validation dataset.
conclusionOur study confirms that 8-point LUS using HHUSD, with AI-based detection of B lines, can provide clinically useful information on the assessment of hydration status and diagnosis of fluid overload for dialysis patients in a user-friendly and time-efficient way.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.