Automated, standardized, quantitative analysis of cardiovascular borders on chest X-rays using deep learning for assessing cardiovascular disease
Notice bibliographique
Résumé
ABSTRACTS OBJECTIVE The analysis of cardiovascular borders (CVBs) on chest X-rays (CXRs) has traditionally relied on subjective assessment, and the cardiothoracic (CT) ratio, its sole quantitative marker, does not reflect great vessel changes and lacks established normal ranges. This study aimed to develop a deep learning-based method for quantifying CVBs on CXRs and to explore its clinical utility. DESIGN Diagnostic/prognostic study SETTING Pre-validated deep learning for quantification and z-score standardization of CVBs: the superior vena cava/ascending aorta (SVC/AO), right atrium (RA), aortic arch, pulmonary artery, left atrial appendage (LAA), left ventricle (LV), descending aorta, and carinal angle. PARTICIPANTS A total of 96,129 normal CXRs from 4 sites were used to establish age- and sex-specific normal ranges of CVBs. The clinical utility of the z-score analysis was tested using 44,567 diseased CXRs from 3 sites. MAIN OUTCOMES MEASURES The area under the curve (AUC) for detecting disease, differences in z-scores for classifying subtypes, and hazard ratio (HR) for predicting 5-year risk of death or myocardial infarction. RESULTS A total of 44,567 patients with disease (9964 valve disease; 32,900 coronary artery disease; 1299 congenital heart disease; 294 aortic aneurysm; 110 mediastinal mass) were analyzed. For distinguishing valve disease from normal controls, the AUC for the CT ratio was 0.79 (95% CI, 0.78-0.80), while the combination of RA and LV had an AUC of 0.82 (95% CI, 0.82-0.83). Between mitral and aortic stenosis, z-scores of CVBs were significantly different in LAA (1.54 vs. 0.33, p<0.001), carinal angle (1.10 vs. 0.67, p<0.001), and SVC/AO (0.63 vs. 1.02, p<0.001), reflecting distinct disease pathophysiology (dilatation of LA vs. AO). CT ratio was independently associated with a 5-year risk of death or myocardial infarction in the coronary artery disease group (z-score ≥2, adjusted HR 3.73 [95% CI, 2.09-6.64], reference z-score <-1). CONCLUSIONS Fully automated, deep learning-derived z-score analysis of CXR showed potential in detecting, classifying, and stratifying the risk of cardiovascular abnormalities. Further research is needed to determine the most beneficial clinical scenarios for this method. What is already known on this topic? Previous deep learning research in the diagnosis of cardiovascular disease using chest X-rays has focused on predicting specific disease categories, forecasting cardiovascular outcomes, and automatically measuring the cardiothoracic (CT) ratio. The end-to-end learning methods that predict disease categories or outcomes are typically limited to specific conditions and often lack explainability. While the CT ratio is traditionally used in chest X-ray analysis, it often lacks well-defined normal ranges and may not effectively detect conditions such as aortic dilatation or pulmonary trunk enlargement. What this study adds To the best of our knowledge, this is the first study to propose age- and sex-specific normal values for all cardiovascular borders (CVBs) as well as the CT ratio. Utilizing 96,129 normal chest X-rays from multiple centers, we have established normal ranges for CVBs and standardized these values into z-score mapping. This approach simplifies and enhances the practicality of clinical application. The z-score mapping of CVBs has demonstrated clinical utility in diagnosing and categorizing diseases, as well as in predicting prognosis. The AI software that automatically analyzes CVBs from CXR is available for external validation and free trial use through our dedicated research website ( www.adcstudy.com ). This study has transformed the interpretation of cardiovascular configuration on chest X-ray from subjective expert assessments to objective, quantifiable, and standardized measurements expressed as z-scores.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,010 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».