ArticlePediatric nephrology (Berlin, Germany)2018
Artificial intelligence outperforms experienced nephrologists to assess dry weight in pediatric patients on chronic hemodialysis.
Article in Pediatric nephrology (Berlin, Germany), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 3 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Pediatric Predictive Artificial Intelligence Implemented in Clinical Practice from 2010 to 2021: A Systematic Review.Applied clinical informatics · 2025Pooled it
- Artificial intelligence in the care of children and adolescents with chronic diseases: a systematic review.European journal of pediatrics · 2024Pooled it
- Narrative update of clinical trials with antihypertensive drugs in children and adolescents.Frontiers in cardiovascular medicine · 2022Pooled it
- Lung ultrasound: a complementary dynamic method to estimate target weight in pediatric hemodialysis.Pediatric nephrology (Berlin, Germany) · 2026Article
- Transforming hemodialysis care: a tripartite collaboration model among medical staff, AI agents, and robots.Clinical and experimental nephrology · 2026Review
- AI-powered insights in pediatric nephrology: current applications and future opportunities.Pediatric nephrology (Berlin, Germany) · 2026Review
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Evaluating large language models in pediatric nephrology.Pediatric nephrology (Berlin, Germany) · 2025Article
- Assessing the performance of large language models (GPT-3.5 and GPT-4) and accurate clinical information for pediatric nephrology.Pediatric nephrology (Berlin, Germany) · 2025Article
- Performance Comparison of a Neural Network and a Regression Linear Model for Predictive Maintenance in Dialysis Machine Components.Bioengineering (Basel, Switzerland) · 2025Article
- Artificial intelligence and pediatric acute kidney injury: a mini-review and white paper.Frontiers in nephrology · 2025Review
- Effect of Remote and Virtual Technology on Home Dialysis.Clinical journal of the American Society of Nephrology : CJASN · 2024Review
- Precision Dialysis: Leveraging Big Data and Artificial Intelligence.Kidney medicine · 2024Review
- Unlocking the potential of big data and AI in medicine: insights from biobanking.Frontiers in medicine · 2024Article
- Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Machine learning models to predict post-dialysis blood pressure in children and young adults on maintenance hemodialysis.Scientific reports · 2023Article
- ESKD Risk Prediction Model in a Multicenter Chronic Kidney Disease Cohort in China: A Derivation, Validation, and Comparison Study.Journal of clinical medicine · 2023Article
- Wearables in Nephrology: Fanciful Gadgetry or Prêt-à-Porter?Sensors (Basel, Switzerland) · 2023Review
- Artificial intelligence and digital health for volume maintenance in hemodialysis patients.Hemodialysis international. International Symposium on Home Hemodialysis · 2022Review
- Machine Learning for Renal Pathologies: An Updated Survey.Sensors (Basel, Switzerland) · 2022Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDry weight is the lowest weight patients on hemodialysis can tolerate; correct dry weight estimation is necessary to minimize morbi-mortality, but is difficult to achieve. Here, we used artificial intelligence to improve the accuracy of dry weight assessment in hemodialysis patients. METHODS/
resultsWe designed a neural network which used bio-impedancemetry, blood volume monitoring, and blood pressure values as inputs; output was artificial intelligence dry weight. Fourteen pediatric patients were switched from nephrologist to artificial intelligence dry weight. Artificial intelligence dry weight was higher (28.6%), lower (50%), or identical to nephrologist dry weight. Mean difference between artificial intelligence and nephrologist dry weights was 0.497 kg (- 1.33 to + 1.29 kg). In patients for whom artificial intelligence dry weight was lower than nephrologist dry weight, systolic blood pressure significantly decreased after dry weight decrease to artificial intelligence dry weight (77th to 60th percentile, p = 0.022); anti-hypertensive treatments were successfully decreased or discontinued in 28.7% of cases. In patients for whom artificial intelligence dry weight was higher than nephrologist dry weight, no hypertension was observed after dry weight increase to artificial intelligence dry weight; when present, symptoms of dry weight underestimation receded.
conclusionsNeural network predictions outperformed those of experienced nephrologists in most cases, proving artificial intelligence is a powerful tool for predicting dry weight in hemodialysis patients.
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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.