AI-based measurement of cardiothoracic ratio in chest X-rays and prediction of echocardiographic congestive heart failure
Bibliographic record
Abstract
Background: This study presents an artificial intelligence (AI) model for automated cardiothoracic ratio (CTR) measurement from chest X-rays (CXRs) and evaluates its association with severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV) diagnosed by echocardiography. The study also assesses CTR's prognostic value for predicting future SLVH/DLV development. Methods: In this retrospective cohort study, an AI algorithm measured CTR on 71,129 CXRs from 24,673 patients from 2013 to 2018 in the CheXchoNet database. SLVH/DLV was defined using echocardiographic criteria. Diagnostic accuracy was assessed using AUROC and AUPRC alongside sensitivity and specificity at various CTR thresholds. Logistic regression was performed for CXR-echocardiogram pairs. Time-to-event analysis was performed on 9,890 patients without baseline SLVH/DLV. Results: Among 24,673 patients (mean age: 62.1 years; female sex: 56.9 %), mean CTR was higher in SLVH/DLV patients (0.56 ± 0.07) than those without (0.52 ± 0.07; p < 0.001). AUROC was 0.70 (95 % CI: 0.69-0.70). At a CTR threshold of 0.53, sensitivity was 70 % and specificity 60 %. Increased CTR was associated with SLVH/DLV risk on paired echocardiogram, with an odds ratio of 1.26 at a CTR of 0.65 compared to CTR at 0.50 (95 % CI: 1.24-1.27, p < 0.001). Time-to-event analysis on patients without baseline SLVH/DLV showed patients with baseline CTR > 0.65 had a 4.13-fold increased risk of developing SLVH/DLV in the future compared to patients with CTR ≤ 0.50 (adjusted HR: 4.13; 95 % CI: 2.48-6.89; p < 0.01). Conclusion: AI-based CTR measurement helps predict SLVH/DLV and could be used for risk stratification for cardiovascular care.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".