Automatic Calculation of Cardiometric Coefficients on Chest X-Ray Images
Bibliographic record
Abstract
Chest radiography is an indispensable diagnostic method for detecting a variety of medical conditions, such as infections, tumors, injuries, etc. Millions of chest X-ray examinations are conducted annually, providing crucial information about the functioning of the respiratory and circulatory systems. The conventional approach to quantifying cardiothoracic indices, such as the Lupi and Moore indices and the Cardiothoracic Index (CTI), requires considerable time and effort from radiologists. Consequently, it calls for the exploration of computational methods for improvement through deep learning. In this study, we addressed the challenge of automating the calculation of these cardiometric indices. We engaged four experienced radiologists to manually label 800 chest X-ray images each. Using these labeled images, we trained a deep learning model that achieved the level of performance comparable to that of a professional radiologist. Additionally, we have replaced the central points of the indices with landmarks based on the vertebrae, improving the accuracy. The use of AI led to improved accuracy of correct predictions, increasing it from 85.94% to 87.34% for the MOORE coefficient and from 87.55% to 90.67% for the LUPI coefficient.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".