ArticleNPJ digital medicine2019
Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning.
Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers, 4 of them syntheses that pooled it.
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Who cites it
96 citing papers in PubMed, 4 syntheses or guidelines pooled it, 222 citations in OpenAlex.
- Applications of artificial intelligence in chronic kidney disease: a systematic review.Revista de saude publica · 2026Pooled it
- Advanced applications in chronic disease monitoring using IoT mobile sensing device data, machine learning algorithms and frame theory: a systematic review.Frontiers in public health · 2025Pooled it
- Artificial intelligence in predicting chronic kidney disease prognosis. A systematic review and meta-analysis.Renal failure · 2024Pooled it
- Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.Journal of nephrology · 2023Pooled it
- Diagnostic accuracy of AI-augmented renal ultrasound for degenerative kidney disorders: a systematic review and meta-analysis.Future science OA · 2026Review
- MFFC-Net: Multi-feature Fusion Deep Networks for Classifying Pulmonary Edema of a Pilot Study by Using Lung Ultrasound Image with Texture Analysis and Transfer Learning Technique.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- A machine learning-based screening tool for classifying post-traumatic stress disorder in firefighters: Firefighter Latent Risk Evaluation (FLARE).BMC psychiatry · 2026Article
- AI-powered insights in pediatric nephrology: current applications and future opportunities.Pediatric nephrology (Berlin, Germany) · 2026Review
- Hypertension-Induced Renal Injury: From Pathophysiology to Therapeutic Perspectives.Biomedicines · 2026Review
- Application of ultrasound localization microscopy in the diagnosis and treatment of kidney diseases.Journal of ultrasound · 2026Review
- Spatiotemporal Deep Video-Phenomapping Decodes Microvascular Rarefaction in Middle-Aged and Elder Renovascular Hypertension: A Multi-Modal Study Integrating Spatial Transcriptomics and Mitochondrial Pyroptosis.Research (Washington, D.C.) · 2026Article
- US-derived Pediatric Kidney Length and Volume Percentiles by Age: A Big Data Approach.Radiology. Artificial intelligence · 2026Article
- Natural language processing for kidney ultrasound analysis: correlating imaging reports with chronic kidney disease diagnosis.Renal failure · 2025Article
- Deep learning framework for automated frame selection in kidney ultrasound.Scientific reports · 2025Article
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
- Recent advancements in patented technologies for the diagnosis and treatment of chronic kidney disease.International urology and nephrology · 2025Review
- A noninvasive model for chronic kidney disease screening and common pathological type identification from retinal images.Nature communications · 2025Article
- Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques.BMC medical informatics and decision making · 2025Article
- Identification and validation of an explainable early-stage chronic kidney disease prediction model: a multicenter retrospective study.EClinicalMedicine · 2025Article
36 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of kidney function and chronic kidney disease (CKD) through kidney ultrasound imaging has long been considered desirable in clinical practice because of its safety, convenience, and affordability. However, this highly desirable approach is beyond the capability of human vision. We developed a deep learning approach for automatically determining the estimated glomerular filtration rate (eGFR) and CKD status. We exploited the transfer learning technique, integrating the powerful ResNet model pretrained on an ImageNet dataset in our neural network architecture, to predict kidney function based on 4,505 kidney ultrasound images labeled using eGFRs derived from serum creatinine concentrations. To further extract the information from ultrasound images, we leveraged kidney length annotations to remove the peripheral region of the kidneys and applied various data augmentation schemes to produce additional data with variations. Bootstrap aggregation was also applied to avoid overfitting and improve the model's generalization. Moreover, the kidney function features obtained by our deep neural network were used to identify the CKD status defined by an eGFR of <60 ml/min/1.73 m
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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.