EchoGraph system for automated quality assessment of echocardiography reports
Bibliographic record
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 2 = 0.803 vs 0.578). EchoGraph offers an effective solution for evaluating language model-based echocardiography applications, supporting more accurate AI-generated reports.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".