Ultrasound in medicine & biology · 2026
Analysis of B-Scan Ultrasonography Using Neural Networks to Predict Risk of Fibrosis in Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD).
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Stansch Josefine, Trung Kien Vu, Blank Valentin, Kather Jakob Nikolas, Herzog Moritz, Seibel Tobias, Koop Paul-Henry, Haase Robert, Berg Thomas, Wiegand Johannes, Karlas Thomas
Аннотация
OBJECTIVE: The prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to rise, underscoring the need for tools to stratify individual risk of disease progression. We evaluated whether logistic regression models augmented by deep learning-based predictions (DLPs) can improve the B-mode ultrasound-based identification of at-risk MASLD, defined as patients with increased fibrosis risk.
METHODS: We retrospectively analyzed 205 patients with a total of 636 ultrasound images. We developed a model that reproduces the LSM-based dichotomous fibrosis risk classification using clinical parameters and ultrasound image-derived deep learning pipelines. Patients were classified by same-day liver stiffness measurement (LSM) (<8 kPa: low fibrosis risk; ≥8 kPa: increased fibrosis risk). We assessed the incremental value of DLPs when added to the parameters sex, age, BMI, diabetes mellitus type 2 status and the fibrosis-4 score (FIB-4) based on accuracy, AUROC, and related statistics.
RESULTS: The logistic regression model combining the clinical parameters and the DLPs achieved acceptable performance with an AUROC of 0.73 and a test accuracy of 68%. The same model without DLPs showed an AUROC of 0.72 and a test accuracy of 61%. Including FIB-4 improved performance further (AUROC 0.92, accuracy 88%). Models based solely on image data demonstrated limited diagnostic performance.
CONCLUSION: B-mode ultrasound provides a weak fibrosis-related signal, yielding limited diagnostic performance. Meaningful discrimination required the incorporation of clinical parameters, with FIB-4 offering the greatest improvement among the parameters assessed. Deep learning predictions added only modest incremental value. Prospective validation is needed to clarify clinical utility.
METHODS: We retrospectively analyzed 205 patients with a total of 636 ultrasound images. We developed a model that reproduces the LSM-based dichotomous fibrosis risk classification using clinical parameters and ultrasound image-derived deep learning pipelines. Patients were classified by same-day liver stiffness measurement (LSM) (<8 kPa: low fibrosis risk; ≥8 kPa: increased fibrosis risk). We assessed the incremental value of DLPs when added to the parameters sex, age, BMI, diabetes mellitus type 2 status and the fibrosis-4 score (FIB-4) based on accuracy, AUROC, and related statistics.
RESULTS: The logistic regression model combining the clinical parameters and the DLPs achieved acceptable performance with an AUROC of 0.73 and a test accuracy of 68%. The same model without DLPs showed an AUROC of 0.72 and a test accuracy of 61%. Including FIB-4 improved performance further (AUROC 0.92, accuracy 88%). Models based solely on image data demonstrated limited diagnostic performance.
CONCLUSION: B-mode ultrasound provides a weak fibrosis-related signal, yielding limited diagnostic performance. Meaningful discrimination required the incorporation of clinical parameters, with FIB-4 offering the greatest improvement among the parameters assessed. Deep learning predictions added only modest incremental value. Prospective validation is needed to clarify clinical utility.