NDLI: Liver fibrosis stage classification in stacked microvascular images based on deep learning

Content Provider	Springer Nature : BioMed Central
Author	Miura, Daisuke Suenaga, Hiromi Hiwatashi, Rino Mabu, Shingo
Abstract	Background Monitoring fibrosis in patients with chronic liver disease (CLD) is an important management strategy. We have already reported a novel stacked microvascular imaging (SMVI) technique and an examiner scoring evaluation method to improve fibrosis assessment accuracy and demonstrate its high sensitivity. In the present study, we analyzed the effectiveness and objectivity of SMVI in diagnosing the liver fibrosis stage based on artificial intelligence (AI). Methods This single-center, cross-sectional study included 517 patients with CLD who underwent ultrasonography and liver stiffness testing between August 2019 and October 2022. A convolutional neural network model was constructed to evaluate the degree of liver fibrosis from stacked microvascular images generated by accumulating high-sensitivity Doppler (i.e., high-definition color) images from these patients. In contrast, as a method of judgment by the human eye, we focused on three hallmarks of intrahepatic microvessel morphological changes in the stacked microvascular images: narrowing, caliber irregularity, and tortuosity. The degree of liver fibrosis was classified into five stages according to etiology based on liver stiffness measurement: F0–1Low (< 5.0 kPa), F0–1High (≥ 5.0 kPa), F2, F3, and F4. Results The AI classification accuracy was 53.8% for a 5-class classification, 66.3% for a 3-class classification (F0–1Low vs. F0–1High vs. F2–4), and 83.8% for a 2-class classification (F0–1 vs. F2–4). The diagnostic accuracy for ≥ F2 was 81.6% in the examiner’s score assessment, compared with 83.8% in AI assessment, indicating that AI achieved higher diagnostic accuracy. Similarly, AI demonstrated higher sensitivity and specificity of 84.2% and 83.5%, respectively. Comparing human judgement with AI judgement, the AI analysis was a superior model with a higher F1 score in the 2-class classification. Conclusions In detecting significant fibrosis (≥ F2) using the SMVI method, AI-based assessments are more accurate than human judgement; moreover, AI-based SMVI analysis eliminating human subjectivity bias and determining patients with objective fibrosis development is considered an important improvement.
Related Links	https://bmcmedimaging.biomedcentral.com/counter/pdf/10.1186/s12880-024-01531-x.pdf
Ending Page	11
Page Count	11
Starting Page	1
File Format	HTM / HTML
ISSN	14712342
DOI	10.1186/s12880-024-01531-x
Journal	BMC Medical Imaging
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2025-01-07
Access Restriction	Open
Subject Keyword	Imaging Radiology Artificial intelligence Deep learning Liver cirrhosis Microvascular imaging Stacked microvascular imaging
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging
Journal Impact Factor	2.9/2023
5-Year Journal Impact Factor	2.8/2023

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