ArticleNPJ digital medicine2025
EchoGraph system for automated quality assessment of echocardiography reports.
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 2 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
2 citing papers in PubMed.
- Suboptimal Echocardiographic Image Quality Predicts in-Hospital Mortality Across the Adult Age Spectrum, Independent of Admission Type: A Large-Scale NLP Study.Echocardiography (Mount Kisco, N.Y.) · 2026Article
- Development and Clinical Evaluation of a Large Language Model-Based System for Generating Patient-Friendly Echocardiography Reports: Two-Stage Retrospective Validation and Prospective Survey Study.Journal of medical Internet research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
Generative AI needs automatic clinical text accuracy metrics, but none exist for echocardiography. To address this, we developed EchoGraph, a BERT-based model trained on 600 densely annotated echocardiography reports from the Mayo Clinic (2017), split 7:2:1 for training, validation, and testing, using a tailored schema with 48,256 entities and 29,731 relations annotated. Sixty random MIMIC-EchoNote reports were annotated (3672 entities and 2360 relations) for external validation. EchoGraph demonstrated strong performance predicting entities (micro F1 0.85) and relations (micro F1 0.70), maintaining performance on external validation (entity micro F1 0.80, relation micro F1 0.52). EchoGraph F1 score showed superior error sensitivity versus RadGraph F1, with 2.8-fold higher slope magnitude (-0.817 vs -0.291) and better variance explained (R
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.