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

Journal: BMC medical imaging

PMID: 39773130

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).

Authors

Daisuke Miura

Department of Ultrasound and Clinical Laboratory, Fukuoka Tokushukai Hospital, Fukuoka, 816-0864, Japan.
Hiromi Suenaga

Department of Laboratory Science, Yamaguchi University Graduate School of Medicine, Yamaguchi, 755-8508, Japan. hiro1204@yamaguchi-u.ac.jp.
Rino Hiwatashi

Department of Ultrasound and Clinical Laboratory, Fukuoka Tokushukai Hospital, Fukuoka, 816-0864, Japan.
Shingo Mabu

Graduate School of Science and Engineering, Yamaguchi University, Tokiwadai 2-16-1, Ube, Yamaguchi, 755-8611, Japan. mabu@yamaguchi-u.ac.jp.

Keywords

Adult Aged Artificial Intelligence Cross-Sectional Studies Deep Learning Elasticity Imaging Techniques Female Humans Liver Cirrhosis Male Microvessels Middle Aged Neural Networks, Computer

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View on PubMed Access via DOI PubMed (39773130)

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

Abstract

Authors

Keywords

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