Evidence map›Paper›PMID 40248124›Full record

ArticleiScience2025

CT-based artificial intelligence system complementing deep learning model and radiologist for liver fibrosis staging.

Shuang Zheng, Wenao Ma, Lin Mu, Kan He, Jianfeng Cao, Tiffany Y So, Lei Zhang, Mingyang Li, Yanan Zhai, Feng Liu and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Shuang ZhengDepartment of Radiology, The First Hospital of Jilin University, Changchun, China.
Wenao MaDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.
Lin MuDepartment of Radiology, The First Hospital of Jilin University, Changchun, China.
Kan HeDepartment of Radiology, The First Hospital of Jilin University, Changchun, China.
Jianfeng CaoDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.
Tiffany Y SoDepartment of Imaging and Interventional Radiology, Prince of Wales Hospital, Hong Kong, China.
Lei ZhangDepartment of Radiology, The First Hospital of Jilin University, Changchun, China.
Mingyang LiDepartment of Radiology, The First Hospital of Jilin University, Changchun, China.
Yanan ZhaiDepartment of Radiology, The First Hospital of Lanzhou University, The First Clinical Medical College of Lanzhou University, Intelligent Imaging Medical Engineering Research Center of Gansu Province, Radiological Clinical Medicine Research Center of Gansu Province, Lanzhou, China.
Feng LiuThe First Clinical Medical College of Lanzhou University, Lanzhou, China.
Shunlin GuoDepartment of Radiology, The First Hospital of Lanzhou University, The First Clinical Medical College of Lanzhou University, Intelligent Imaging Medical Engineering Research Center of Gansu Province, Radiological Clinical Medicine Research Center of Gansu Province, Lanzhou, China.
Longlin YinDepartment of Radiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
Liming ZhaoDepartment of Radiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
Lei WangDepartment of Imaging and Interventional Radiology, Prince of Wales Hospital, Hong Kong, China.
Heather H C LeeDepartment of Diagnostic Radiology, Princess Margaret Hospital, Hong Kong, China.
Wei JiangNational Health Commission Capacity Building and Continuing Education Center, Department of Big Data, Beijing, China.
Junqi NiuDepartment of Hepatology, The First Hospital of Jilin University, Changchun, China.
Pujun GaoDepartment of Hepatology, The First Hospital of Jilin University, Changchun, China.
Qi DouDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.
Huimao ZhangDepartment of Radiology, The First Hospital of Jilin University, Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Noninvasive methods for liver fibrosis staging are urgently needed due to its significance in predicting significant morbidity and mortality. In this study, we developed an automated DL-based segmentation and classification model (Model-C). Test-time adaptation was used to address data distribution shifts. We then established a deep learning-radiologist complementarity decision system (DRCDS) via a decision model determining whether to adopt Model-C's diagnosis or defer to radiologists. Model-C (AUCs of 0.89-0.92) outperformed models based on liver (AUCs: 0.84-0.90) or spleen (AUCs: 0.69-0.70). With test-time adaptation, the Obuchowski index values of Model-C in three external sets improved from 0.81, 0.73, and 0.73 to 0.85, 0.85, and 0.81. DRCDS performed slightly better than Model-C or senior radiologists, with 73.7%-92.0% of cases adopting Model-C's diagnosis. In conclusion, DRCDS could diagnose liver fibrosis with high accuracy. Additionally, we provided solutions to model generalization and human-machine complementarity issues in multi-classification problems.

Indexed as

Artificial intelligencePathophysiologyPublic health

Identifiers

PMID40248124
PMCPMC12005311

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.