Ultrasound image-based contrastive fusion non-invasive liver fibrosis staging algorithm.
Objective: The diagnosis of liver fibrosis is usually based on histopathological examination of liver puncture specimens. Although liver puncture is accurate, it has invasive risks and high economic costs, which are difficult for some patients to accept. Therefore, this study uses deep learning tech...
| Publicado en: | Abdominal Radiology Vol. 50; no. 12; pp. 6135 - 6148 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189211979&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189211979 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Dec2025 vid: 50 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 189211979 185507969 10.1007/s00261-025-04991-z 189211979 ppf: 6135 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Ultrasound image-based contrastive fusion non-invasive liver fibrosis staging algorithm. aug: au: Dong, Xinyi Tan, Qinxiang Xu, Shu Zhang, Jie Zhou, Mingqiang affil: https://ror.org/05damtm70 Shenzhen Hospital, Beijing University of Chinese Medicine, Shenzhen, China sug: ab: Objective: The diagnosis of liver fibrosis is usually based on histopathological examination of liver puncture specimens. Although liver puncture is accurate, it has invasive risks and high economic costs, which are difficult for some patients to accept. Therefore, this study uses deep learning technology to build a liver fibrosis diagnosis model to achieve non-invasive staging of liver fibrosis, avoid complications, and reduce costs. Methods: This study uses ultrasound examination to obtain pure liver parenchyma image section data. With the consent of the patient, combined with the results of percutaneous liver puncture biopsy, the degree of liver fibrosis indicated by ultrasound examination data is judged. The concept of Fibrosis Contrast Layer (FCL) is creatively introduced in our experimental method, which can help our model more keenly capture the significant differences in the characteristics of liver fibrosis of various grades. Finally, through label fusion (LF), the characteristics of liver specimens of the same fibrosis stage are abstracted and fused to improve the accuracy and stability of the diagnostic model. Results: Experimental evaluation demonstrated that our model achieved an accuracy of 85.6%, outperforming baseline models such as ResNet (81.9%), InceptionNet (80.9%), and VGG (80.8%). Even under a small-sample condition (30% data), the model maintained an accuracy of 84.8%, significantly outperforming traditional deep-learning models exhibiting sharp performance declines. Conclusion: The training results show that in the whole sample data set and 30% small sample data set training environments, the FCLLF model's test performance results are better than those of traditional deep learning models such as VGG, ResNet, and InceptionNet. The performance of the FCLLF model is more stable, especially in the small sample data set environment. Our proposed FCLLF model effectively improves the accuracy and stability of liver fibrosis staging using non-invasive ultrasound imaging. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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