ArticleJournal of medical imaging (Bellingham, Wash.)2023
Automated fatty liver disease detection in point-of-care ultrasound B-mode images.
Article in Journal of medical imaging (Bellingham, Wash.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it, 10 citations in OpenAlex.
- Assessment of the Diagnostic Performance and Clinical Impact of AI in Hepatic Steatosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Pooled it
- Handheld Ultrasound: Technical Review and International Expert Consensus Recommendations.Diagnostics (Basel, Switzerland) · 2026Review
- Combining nutraceuticals and a mediterranean diet for managing metabolic dysfunction associated with steatotic liver disease.World journal of hepatology · 2025Article
- Adaptive Evolutionary Optimization of Deep Learning Architectures for Focused Liver Ultrasound Image Segmentation.Diagnostics (Basel, Switzerland) · 2025Article
- Is hepatosteatosis overlooked in ultrasound relying only on B-mode? The impact of incorporating the attenuation coefficient into the standard abdominal ultrasound protocol.Polish journal of radiology · 2025Article
- MASLD: What We Have Learned and Where We Need to Go-A Call to Action.Radiographics : a review publication of the Radiological Society of North America, Inc · 2024Review
- Critical Advances for Democratizing Ultrasound Diagnostics in Human and Veterinary Medicine.Annual review of biomedical engineering · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Non-alcoholic fatty liver disease (NAFLD) is an increasing global health concern, with a prevalence of 25% worldwide. The rising incidence of NAFLD, an asymptomatic condition, reinforces the need for systematic screening strategies in primary care. We present the use of non-expert acquired point-of-care ultrasound (POCUS) B-mode images for the development of an automated steatosis classification algorithm. Approach: We obtained a Health Insurance Portability and Accountability Act compliant dataset consisting of 478 patients [body mass index Results: On a hold-out test set of 81 patients, the final DenseNet-121 model yielded an area under the receiver operator characteristic curve of 90.1%, sensitivity of 95.0%, and specificity of 85.2% for the detection of liver steatosis. Average cross-validation performance in models using patches of liver parenchyma as input outperformed methods using complete B-mode frames. Conclusions: Despite minimal POCUS acquisition training, and low-quality B-mode images, it is possible to detect steatosis using DL algorithms. Implementation of this algorithm in POCUS software may offer an accessible, low-cost steatosis screening technology, for use by non-expert health care personnel.
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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.