Evidence map›Paper›PMID 40628914›Full record

ArticleScientific reports2025

Machine learning models using non-invasive tests & B-mode ultrasound to predict liver-related outcomes in metabolic dysfunction-associated steatotic liver disease.

Heather Mary-Kathleen Kosick, Chris McIntosh, Chinmay Bera, Mina Fakhriyehasl, Mohamed Shengir, Oyedele Adeyi, Leila Amiri, Giada Sebastiani, Kartik Jhaveri, Keyur Patel

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Review
  6. [Research progress and future prospects for artificial intelligence in the diagnosis and treatment of fatty liver disease].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025
    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

10 authors.

Heather Mary-Kathleen KosickDivision of Gastroenterology, University Health Network Toronto, Toronto General Hospital, 200 Elizabeth Street, Toronto, ON, M5G 2C4, Canada. heather.evans@medportal.ca.
Chris McIntoshUniversity of Toronto, 27 King's College Circle, Toronto, ON, M5S 1A1, Canada.
Chinmay BeraDivision of Gastroenterology, University Health Network Toronto, Toronto General Hospital, 200 Elizabeth Street, Toronto, ON, M5G 2C4, Canada.
Mina FakhriyehaslJoint Department of Medical Imaging, University Health Network, Mount Sinai Hospital, Women's College Hospital, 200 Elizabeth Street, Toronto, ON, M5G 2C4, Canada.
Mohamed ShengirDivision of Gastroenterology and Hepatology, McGill University Health Centre, 1001 Boul Decarie, Montreal, QC, H4A 3J1, Canada.
Oyedele AdeyiDepartment of Laboratory Medicine and Pathology, University of Minnesota, Minnesota, MN, 55455, USA.
Leila AmiriDivision of Gastroenterology, University Health Network Toronto, Toronto General Hospital, 200 Elizabeth Street, Toronto, ON, M5G 2C4, Canada.
Giada SebastianiDivision of Gastroenterology and Hepatology, McGill University Health Centre, 1001 Boul Decarie, Montreal, QC, H4A 3J1, Canada.
Kartik JhaveriUniversity of Toronto, 27 King's College Circle, Toronto, ON, M5S 1A1, Canada.
Keyur PatelDivision of Gastroenterology, University Health Network Toronto, Toronto General Hospital, 200 Elizabeth Street, Toronto, ON, M5G 2C4, Canada.

Funding

Fonds de Recherche du Quebec - Sante (FRQS) 296306
6 · The paper itself

Abstract

Advanced metabolic-dysfunction-associated steatotic liver disease (MASLD) fibrosis (F3-4) predicts liver-related outcomes. Serum and elastography-based non-invasive tests (NIT) cannot yet reliably predict MASLD outcomes. The role of B-mode ultrasound (US) for outcome prediction is not yet known. We aimed to evaluate machine learning (ML) algorithms based on simple NIT and US for prediction of adverse liver-related outcomes in MASLD. Retrospective cohort study of adult MASLD patients biopsied between 2010-2021 at one of two Canadian tertiary care centers. Random forest was used to create predictive models for outcomes-hepatic decompensation, liver-related outcomes (decompensation, hepatocellular carcinoma (HCC), liver transplant, and liver-related mortality), HCC, liver-related mortality, F3-4, and fibrotic metabolic dysfunction-associated steatohepatitis (MASH). Diagnostic performance was assessed using area under the curve (AUC). 457 MASLD patients were included with 44.9% F3-4, diabetes prevalence 31.6%, 53.8% male, mean age 49.2 and BMI 32.8 kg/m

Indexed as

Fatty LiverLiverLiver CirrhosisMachine LearningAdultAgedCanadaCarcinoma, HepatocellularFemaleHumansLiver NeoplasmsMaleMiddle AgedRetrospective StudiesUltrasonographyArtificial intelligenceFibrosisHepatic decompensationLiver biopsySteatosis

Identifiers

PMID40628914
PMCPMC12238363

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.