A prospective validation of a deep learning-based automated workflow for the interpretation of the echocardiogram
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Us2.ai Background. Deep learning can automate the interpretation of medical imaging tests. This study aimed to prospectively assess the interchangeability of deep learning algorithms with expert human measurements for interpreting echocardiographic studies, the primary method for assessing cardiac structure and function. Methods. We compared a deep learning interpretation of 23 echocardiographic parameters—including cardiac volumes, ejection fraction, and Doppler measurements—with three repeated measurements by core lab human experts in a prospective study for submission to the United States Food and Drug Administration (FDA). The primary outcome metric was the individual equivalence coefficient (IEC), which compares the disagreement between deep learning and human readers relative to the disagreement among human readers. The pre-determined non-inferiority criterion was 0.25 for the upper bound of the 95% confidence interval (CI). Secondary outcomes included measures of agreement, including the mean absolute deviation. Results. We included 602 studies from 600 participants (421 with heart failure, 179 controls, 69% women) with a mean age of 57 ± 16 years. The point estimates of IEC were all <0, indicating that the disagreement between the deep learning and human measures were lower than the disagreement among three core lab readers, and the upper bound of the 95% CI of IECs fell below the prespecified success criterion of 0.25. Secondary endpoints showed good agreement of automated with human expert measurements (Figure), with comparable or lower mean absolute deviations between automated and human experts relative to the mean absolute deviation among human experts. Conclusion. This prospective validation study demonstrated excellent agreement between deep learning and expert human interpretation for a wide range of echocardiographic measurements. These results highlight the potential of deep learning algorithms to improve efficiency and reduce costs of echocardiography. Abstract Figure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.067 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".