Quantitative Ultrasound Assessment of Liver Fat Using Deep Learning and Clinical Data Integration

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dc.contributor.authorJamrasnarodom, Jirakorn
dc.contributor.authorApiparakoon, Terapap
dc.contributor.authorMarukatat, Sanparith
dc.contributor.authorChaichuen, Oracha
dc.contributor.authorSukchareon, Sasima
dc.contributor.authorChaiteerakij, Roongruedee
dc.date.accessioned2026-08-06T10:52:56Z
dc.date.available2026-08-06T10:52:56Z
dc.date.issued2025-12-01
dc.description.abstractPurpose: The increasing prevalence of Metabolic Dysfunction-Associated Steatosis Liver Disease (MASLD) highlights the need for effective assessment tools, particularly for the quantitative evaluation of hepatic steatosis. Given the limited availability of transient elastography (TE), especially in resource-limited settings, we aimed to develop an Artificial Intelligence (AI) model to quantify hepatic steatosis using conventional ultrasound, which is widely available in most healthcare facilities. Methods: Liver ultrasonographic images and Controlled Attenuation Parameter (CAP) scores obtained from TE were collected from patients between 2017 and 2023. A predictive model was developed by integrating YOLOv8 for image classification with Principal Component Analysis and Lasso regression to estimate CAP scores from the ultrasonographic images. The dataset was randomly divided into training (80%), validation (10%), and test (10%) sets. Baseline patient characteristics and laboratory data were also incorporated to enhance model performance. The model’s predictive ability was evaluated using the coefficient of determination (R²) and mean squared error (MSE). Results: A total of 1065 images from 352 patients were included. The initial model achieved an R² of 0.55 and an MSE of 1004.07. Subgroup analysis revealed that the right intercostal view yielded the best performance (R²=0.74, MSE = 637.99). After incorporating patient characteristics and laboratory data, the model’s performance improved significantly (R²=0.90, MSE = 245.79). Conclusion: The AI-assisted model showed promise for accessible and non-invasive assessment of hepatic steatosis, particularly when using the right intercostal view and supplemental clinical data. Further validation is warranted to improve its accuracy and generalizability.
dc.identifier.citationJournal of Medical and Biological Engineering, 45(6), 817-825, 2025
dc.identifier.doi10.1007/s40846-025-00986-9
dc.identifier.issn16090985
dc.identifier.other2-s2.0-105021229088
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17430
dc.sourceJournal of Medical and Biological Engineering
dc.subjectAI-assisted image analysis
dc.subjectArtificial intelligence
dc.subjectFatty liver
dc.subjectMetabolic Dysfunction-Associated steatosis liver disease
dc.subjectUltrasound
dc.titleQuantitative Ultrasound Assessment of Liver Fat Using Deep Learning and Clinical Data Integration
dc.typeArticle

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