ArticleJournal of thoracic disease2024
Deep learning to predict long-term mortality from plain chest X-ray in patients referred for suspected coronary artery disease.
Article in Journal of thoracic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Utility of artificial intelligence in predicting clinical outcomes of angiographic procedures: a comprehensive scoping review.Journal of cardiovascular imaging · 2026Review
- Deep learning to predict long-term mortality from plain chest X-ray in patients referred for suspected coronary artery disease.Journal of thoracic disease · 2024Article
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Authors and funding
12 authors.
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Abstract
Background: The hypothesis that a deep learning (DL) model can produce long-term prognostic information from chest X-ray (CXR) has already been confirmed within cancer screening programs. We summarize our experience with DL prediction of long-term mortality, from plain CXR, in patients referred for angina and coronary angiography. Methods: Data of patients referred to an Italian academic hospital were analyzed retrospectively. We designed a deep convolutional neural network (DCNN) that, from CXR, could predict long-term mortality. External validation was performed on patients referred to a Dutch academic hospital. Results: A total of 6,031 were used for model training (71%; n=4,259) and fine-tuning/validation (10%; n=602). Internal validation was performed with the remaining patients (19%; n=1,170). Patients' stratification followed the DL-CXR risk score quartiles division. Median follow-up was 6.1 years [interquartile range (IQR), 3.3-8.7 years]. We observed an increment in estimated mortality with the increase of DL-CXR risk score (low-risk 5%, moderate 17%, high 29%, very high 46%; P<0.001). The DL-CXR risk score predicted median follow-up outcome with an area under the curve (AUC) of 0.793 [95% confidence interval (CI): 0.759-0.827, sensitivity 78%, specificity 68%]. Prediction was better than that achieved using coronary angiography findings (AUC: 0.569, 95% CI: 0.52-0.61, P<0.001) and age (AUC: 0.735, 95% CI: 0.69-0.77, P<0.004). At Cox regression, the DL-CXR risk score predicted follow-up mortality (P<0.005, hazard ratio: 3.30, 95% CI: 2.35-4.64). External validation confirmed the DL-CXR risk score performance (AUC: 0.71, 95% CI: 0.49-0.92; sensitivity 0.838; specificity 0.338). Conclusions: In patients referred for coronary angiogram because of angina, the DL-CXR risk score could be used to stratify mortality risk and predict long-term outcome better than age and coronary artery disease status.
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