ArticleEuropean radiology2023
Nonalcoholic fatty liver disease (NAFLD) detection and deep learning in a Chinese community-based population.
Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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18 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 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
- Comparison of liver T1 estimates generated by breath-hold 3D FSPGR and free-breathing 3D radial stack-of-stars FSPGR variable flip angle T1 mapping sequences.European journal of radiology open · 2026Article
- Deep learning for non-invasive detection of steatosis and fibrosis in MASLD: a multicenter study with a new fibroscan-labelled ultrasound dataset.Abdominal radiology (New York) · 2026Article
- Attention-based graph neural network framework for non-invasive CAP score prediction in fatty liver disease via body modeling.Physical and engineering sciences in medicine · 2026Article
- Applications of Artificial Intelligence and Smart Devices in Metabolic Dysfunction-associated Steatotic Liver Disease.Journal of clinical and translational hepatology · 2026Article
- A comprehensive narrative review of artificial intelligence use in the diagnosis and management of metabolic dysfunction-associated steatotic liver disease.Translational gastroenterology and hepatology · 2026Review
- The association between the ZJU index and short-term prognosis in patients with acute ischemic stroke: a cohort study.Frontiers in endocrinology · 2026Article
- AI-driven diagnostic and prognostic models for metabolic dysfunction-associated steatotic liver disease: insights from clinical, imaging, and multi-omics studies-a scoping review.Frontiers in medicine · 2026Review
- [Research progress and future prospects for artificial intelligence in the diagnosis and treatment of fatty liver disease].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
- Diagnostic accuracy of convolutional neural networks in classifying hepatic steatosis from B-mode ultrasound images: a systematic review with meta-analysis and novel validation in a community setting in Telangana, India.The Lancet regional health. Southeast Asia · 2025Article
- Machine Learning-Based Non-Invasive Prediction of Metabolic Dysfunction-Associated Steatohepatitis in Obese Patients: A Retrospective Study.Diagnostics (Basel, Switzerland) · 2025Article
- Establishing predictive models for malignant and inflammatory pulmonary nodules using clinical data and CT imaging features.Quantitative imaging in medicine and surgery · 2025Article
- Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications.Abdominal radiology (New York) · 2025Review
- Smoking-related gut microbiota alteration is associated with obesity and obesity-related diseases: results from two cohorts with sibling comparison analyses.BMC medicine · 2025Article
- Artificial Intelligence in Clinics: Enhancing Cardiology Practice.JMA journal · 2025Review
- Association between trans-palmitoleic acid and metabolic dysfunction-associated steatotic liver disease-related hepatocellular carcinoma: NHANES 1999-2018.Frontiers in nutrition · 2025Article
- Review
- Steatosis Quantification on Ultrasound Images by a Deep Learning Algorithm on Patients Undergoing Weight Changes.Diagnostics (Basel, Switzerland) · 2023Article
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20 authors at 7 institutions in 2 countries.
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Abstract
objectivesWe aimed to develop and validate a deep learning system (DLS) by using an auxiliary section that extracts and outputs specific ultrasound diagnostic features to improve the explainable, clinical relevant utility of using DLS for detecting NAFLD.
methodsIn a community-based study of 4144 participants with abdominal ultrasound scan in Hangzhou, China, we sampled 928 (617 [66.5%] females, mean age: 56 years ± 13 [standard deviation]) participants (2 images per participant) to develop and validate DLS, a two-section neural network (2S-NNet). Radiologists' consensus diagnosis classified hepatic steatosis as none steatosis, mild, moderate, and severe. We also explored the NAFLD detection performance of six one-section neural network models and five fatty liver indices on our data set. We further evaluated the influence of participants' characteristics on the correctness of 2S-NNet by logistic regression.
resultsArea under the curve (AUROC) of 2S-NNet for hepatic steatosis was 0.90 for ≥ mild, 0.85 for ≥ moderate, and 0.93 for severe steatosis, and was 0.90 for NAFLD presence, 0.84 for moderate to severe NAFLD, and 0.93 for severe NAFLD. The AUROC of NAFLD severity was 0.88 for 2S-NNet, and 0.79-0.86 for one-section models. The AUROC of NAFLD presence was 0.90 for 2S-NNet, and 0.54-0.82 for fatty liver indices. Age, sex, body mass index, diabetes, fibrosis-4 index, android fat ratio, and skeletal muscle via dual-energy X-ray absorptiometry had no significant impact on the correctness of 2S-NNet (p > 0.05).
conclusionsBy using two-section design, 2S-NNet had improved the performance for detecting NAFLD with more explainable, clinical relevant utility than using one-section design. KEY POINTS: • Based on the consensus review derived from radiologists, our DLS (2S-NNet) had an AUROC of 0.88 by using two-section design and yielded better performance for detecting NAFLD than using one-section design with more explainable, clinical relevant utility. • The 2S-NNet outperformed five fatty liver indices with the highest AUROCs (0.84-0.93 vs. 0.54-0.82) for different NAFLD severity screening, indicating screening utility of deep learning-based radiology may perform better than blood biomarker panels in epidemiology. • The correctness of 2S-NNet was not significantly influenced by individual's characteristics, including age, sex, body mass index, diabetes, fibrosis-4 index, android fat ratio, and skeletal muscle via dual-energy X-ray absorptiometry.
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