Evidence map›Paper›PMID 40481190›Full record

ArticleNPJ digital medicine2025

Foundation versus domain-specific models for left ventricular segmentation on cardiac ultrasound.

Chieh-Ju Chao, Yunqi Richard Gu, Wasan Kumar, Tiange Xiang, Lalith Appari, Justin Wu, Juan M Farina, Rachael Wraith, Jiwoon Jeong, Reza Arsanjani and 6 more

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PubMed Full text at PMC Publisher
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Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

16 authors.

Chieh-Ju ChaoDepartment of Cardiovascular Medicine, Mayo Clinic Rochester, Rochester, MN, USA.
Yunqi Richard GuStanford Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA.
Wasan KumarStanford Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA.
Tiange XiangStanford Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA.
Lalith AppariDepartment of Radiology, Mayo Clinic Arizona, Scottsdale, AZ, USA.
Justin WuStanford Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA.
Juan M FarinaDepartment of Cardiovascular Medicine, Mayo Clinic Arizona, Scottsdale, AZ, USA.
Rachael WraithDepartment of Cardiovascular Medicine, Mayo Clinic Arizona, Scottsdale, AZ, USA.
Jiwoon JeongDepartment of Radiology, Mayo Clinic Arizona, Scottsdale, AZ, USA.
Reza ArsanjaniDepartment of Cardiovascular Medicine, Mayo Clinic Arizona, Scottsdale, AZ, USA.
Garvan C KaneDepartment of Cardiovascular Medicine, Mayo Clinic Rochester, Rochester, MN, USA.
Jae K OhDepartment of Cardiovascular Medicine, Mayo Clinic Rochester, Rochester, MN, USA.
Curtis P LanglotzCenter of Artificial Intelligence in Medical Imaging, Stanford University, Stanford, CA, USA.
Imon BanerjeeDepartment of Radiology, Mayo Clinic Arizona, Scottsdale, AZ, USA.
Li Fei-FeiStanford Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA. feifeili@cs.stanford.edu.
Ehsan AdeliStanford Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA. eadeli@stanford.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Segment Anything Model (SAM) was fine-tuned on the EchoNet-Dynamic dataset and evaluated on external transthoracic echocardiography (TTE) and Point-of-Care Ultrasound (POCUS) datasets from CAMUS (University Hospital of St Etienne) and Mayo Clinic (99 patients: 58 TTE, 41 POCUS). Fine-tuned SAM was superior or comparable to MedSAM. The fine-tuned SAM also outperformed EchoNet and U-Net models, demonstrating strong generalization, especially on apical 2-chamber (A2C) images (fine-tuned SAM vs. EchoNet: CAMUS-A2C: DSC 0.891 ± 0.040 vs. 0.752 ± 0.196, p < 0.0001) and POCUS (DSC 0.857 ± 0.047 vs. 0.667 ± 0.279, p < 0.0001). Additionally, SAM-enhanced workflow reduced annotation time by 50% (11.6 ± 4.5 sec vs. 5.7 ± 1.7 sec, p < 0.0001) while maintaining segmentation quality. We demonstrated an effective strategy for fine-tuning a vision foundation model for enhancing clinical workflow efficiency and supporting human-AI collaboration.

Identifiers

PMID40481190
PMCPMC12144204

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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.