ReviewPeerJ2025
Update of machine learning for ultrasound diagnosis of metabolic dysfunction-associated steatotic liver disease: a bright future for deep learning.
Review in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of machine learning in the diagnosis of non-alcoholic fatty liver disease: a systematic review and meta-analysis.BMC gastroenterology · 2026Pooled it
- CT Attenuation Thresholds for Grading Hepatic Steatosis in Adults with Overweight or Obesity and Metabolic Dysfunction-Associated Steatotic Liver Disease.Diagnostics (Basel, Switzerland) · 2026Article
- Targeting GPR68 Alleviates Inflammation and Lipid Accumulation in Metabolic Dysfunction-Associated Steatohepatitis.Biology · 2026Article
- Advances in quantitative ultrasound for metabolic dysfunction-associated steatotic liver disease diagnosis.Frontiers in physiology · 2026Review
- Interpretable Machine Learning Models for Early Detection of Metabolic Dysfunction-Associated Steatotic Liver Disease Using Non-Invasive Routine Clinical and Laboratory Data.International journal of general medicine · 2026Article
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common liver disease and the burden is increasing around the world. Ultrasound diagnosis of MASLD is the preferred method due to its convenience, absence of radiation, and high accuracy. The application of artificial intelligence (AI) in MASLD diagnosis has been explored across electronic medical records, laboratory tests, ultrasound and radiographic imaging, and liver histopathological data. Notably, AI's application in ultrasound diagnosis has garnered significant attention. Deep learning (DL), known for its exceptional image recognition and classification capabilities, has become a focal point in ultrasound research. This paper reviews and analyzes the application of various machine learning (ML) algorithms in the ultrasound diagnosis of MASLD, highlighting the advantages and potential of AI in this field. It is intended for clinicians, AI researchers, and healthcare innovators, aiming to enhance diagnostic accuracy, expand MASLD screening in primary care, and support early diagnosis, prevention, and treatment.
Indexed as
Identifiers
What OpenQuestion holds
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.