Using Deep Learning to Detect Heart Scars from MRI Images
Author Information
Author(s): Francesca Righetti, Giulia Rubiu, Marco Penso, Sara Moccia, Maria L. Carerj, Mauro Pepi, Gianluca Pontone, Enrico G. Caiani
Primary Institution: Politecnico di Milano
Hypothesis
The use of parametric images derived from cine cardiac magnetic resonance images can improve the detection of myocardial scar tissue using deep learning.
Conclusion
The study demonstrates that a convolutional neural network can effectively classify the presence of myocardial scars in cardiac MRI images without the need for contrast agents.
Supporting Evidence
- The CNN achieved an accuracy of 0.79 and an area under the ROC curve of 0.86 for slice classification.
- At the patient level, the CNN exhibited a prediction accuracy of 1.0 for classifying patients as control or pathologic.
- The study suggests that the proposed approach could serve as a preliminary screening tool for LGE-CMR imaging.
Takeaway
Researchers created a computer program that helps doctors find scars in the heart using special pictures from MRI scans, without needing any dye.
Methodology
The study used a convolutional neural network to analyze cine cardiac MRI images and parametric images derived from them to classify the presence of myocardial scars.
Potential Biases
Potential bias due to reliance on a single expert for ground truth labeling and the limited diversity of the patient population.
Limitations
The study had a relatively small dataset and relied on the interpretation of images by a single expert, which may affect the generalizability of the results.
Participant Demographics
The study included 206 patients, with 158 having ischemic dilated cardiomyopathy and 48 control patients.
Statistical Information
P-Value
p<0.0001
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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