ArticleJournal of the American Society of Echocardiography : official publication of the American Society of Echocardiography2025
Are AI Foundation Models Efficient for Segmentation of Echocardiograms?
Article in Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Comparative Evaluation of Deep Learning and Foundation Model Embeddings for Osteoarthritis Feature Classification in Knee Radiographs.Journal of imaging informatics in medicine · 2026Article
- Generative deep learning for foundational video translation in ultrasound.Scientific reports · 2026Article
- What's not to learn? AI meets parasitology.Journal of clinical microbiology · 2026Article
- Self-supervised learning for label-free segmentation in cardiac ultrasound.Nature communications · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
PubMed holds no abstract for this paper.
Identifiers
What OpenQuestion holds
Registered trials
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.