1. Predicting long-term time to cardiovascular incidents using myocardial perfusion imaging and deep convolutional neural networks
- Author
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Yi-Lian Li, Hsin-Bang Leu, Chien-Hsin Ting, Su-Shen Lim, Tsung-Ying Tsai, Cheng-Hsueh Wu, I-Fang Chung, and Kung-Hao Liang
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End-to-end survival training ,Survival analysis ,Risk score ,Multiresolution ,Medicine ,Science - Abstract
Abstract Myocardial perfusion imaging (MPI) is a clinical tool which can assess the heart's perfusion status, thereby revealing impairments in patients' cardiac function. Within the MPI modality, the acquired three-dimensional signals are typically represented as a sequence of two-dimensional grayscale tomographic images. Here, we proposed an end-to-end survival training approach for processing gray-scale MPI tomograms to generate a risk score which reflects subsequent time to cardiovascular incidents, including cardiovascular death, non-fatal myocardial infarction, and non-fatal ischemic stroke (collectively known as Major Adverse Cardiovascular Events; MACE) as well as Congestive Heart Failure (CHF). We recruited a total of 1928 patients who had undergone MPI followed by coronary interventions. Among them, 80% (n = 1540) were randomly reserved for the training and 5- fold cross-validation stage, while 20% (n = 388) were set aside for the testing stage. The end-to-end survival training can converge well in generating effective AI models via the fivefold cross-validation approach with 1540 patients. When a candidate model is evaluated using independent images, the model can stratify patients into below-median-risk (n = 194) and above-median-risk (n = 194) groups, the corresponding survival curves of the two groups have significant difference (P
- Published
- 2024
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