Why the study?
Marfan syndrome is diagnosed by aortic enlargement, which can lead to complications such as aortic dilation, aortic dissection, and aortic wall rupture.
Does a CNN system accurately detect aortic enlargement on chest X-rays to identify potential Marfan syndrome?
Does a CNN system accurately detect aortic enlargement on chest X-rays to identify potential Marfan syndrome?
A deep learning system using chest X-rays can accurately detect aortic enlargement, offering a potential initial screening tool for Marfan syndrome.
May support AI screening for aortic enlargement; hypothesis-generating and requires prospective validation before clinical adoption.
Marfan syndrome is diagnosed by aortic enlargement, which can lead to complications such as aortic dilation, aortic dissection, and even rupture of the aortic wall in extreme cases. As part of this work, we developed a CNN system whose function is to interpret chest X-ray images and determine whether they show an enlarged aorta. Two architectures were studied for best hyper-parameters that affect the performance of DenseNet and Inception-v3. In most tests, the system obtained high accuracy rates, low loss rates, and no overfitting, making it a valuable tool for medical practitioners to help provide an initial diagnosis to potential patients.
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Rosenwasser et al. (2022) studied this question.
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