ArticleQuantitative imaging in medicine and surgery2026
Early assessment of myocardial injury in patients with coronavirus disease 2019 using a two-stage deep learning framework based on non-contrast chest computed tomography.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Risk stratification is particularly important for the prognosis of coronavirus disease 2019 (COVID-19), particularly regarding myocardial injury (MI). Non-contrast chest computed tomography (CT) is useful for diagnosing lung injury, but its potential for one-stop evaluation of MI remains unclear. This study aimed to develop a deep learning (DL) framework for the one-step prediction of MI in COVID-19 patients using non-contrast chest CT. This approach seeks to enable early MI screening, direct high-risk patients toward further diagnostic tests, and optimize the allocation of medical resources. Methods: A group of 453 patients with COVID-19, including 230 patients with MI and 223 patients without MI, were retrospectively recruited. A two-stage DL framework was developed to first segment the left ventricle (LV) in the non-contrast chest CT images using fully convolutional networks with a ResNet-101 backbone (FCN-ResNet-101) module and then classify the status of MI using densely connected convolutional network with the structure type of 121 (DenseNet-121) module. The framework was trained in a training-validation dataset of 413 patients (MI or non-MI) with a cross-validation approach, and evaluated in a testing dataset of 40 patients. Results: The proposed DL framework accurately obtained the segmentations of LV in non-contrast chest CT images with an intersection over union (IoU) of 0.8041, an accuracy (ACC) of 0.9949, and a Dice coefficient of 0.8672. Based on the segmentation, the DL framework further accurately determined MI status and obtained an area under the curve (AUC) of 0.8618 [95% confidence interval (CI): 0.8049-0.9187], an ACC of 0.7763, a sensitivity (SEN) of 0.8750, a specificity (SPE) of 0.6071, and an F1 score of 0.8317. Conclusions: DL could determine the status of MI in non-contrast chest CT images of patients with COVID-19, providing one-stop convenience for early screening of MI.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.