Optical coherence tomography-based system for diagnosis of high-risk lesion and diagnosis method therefor
AssigneesThe Asan Foundation, University of Ulsan Foundation for Industry Cooperation
InventorsSoo Jin KANG, Hyun Seok MIN
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Abstract
The disclosure purposes to provide an optical coherence tomography (OCT)-based system for diagnosing a high risk lesion such as a vulnerable atheromatous plaque by using an artificial intelligence model through deep learning. A deep learning-based diagnostic method of diagnosing a high risk lesion of a coronary artery includes: acquiring an OCT image of a coronary artery lesion of a patient; extracting a first feature of a thin cap from the OCT image; setting a region of interest included in the OCT image on a basis of the first feature; and determining whether the region of interest includes a high risk lesion.
Claims6
A deep learning-based diagnostic method of diagnosing a high risk lesion of a coronary artery, the deep learning-based diagnostic method comprising:
- an image acquiring step of acquiring an optical coherence tomography (OCT) image of a coronary artery lesion of a patient;
- a feature extraction step of extracting a first feature of a thin cap from the OCT image;
- a region-of-interest setting step of setting a region of interest included in the OCT image on a basis of the first feature;
- and
- a high risk determination step of determining whether the region of interest includes a high risk lesion.
A deep learning-based diagnostic apparatus of diagnosing a high risk lesion of a coronary artery, the deep learning-based diagnostic apparatus comprising:
- an image acquiring unit configured to acquire an optical coherence tomography (OCT) image of a coronary artery lesion of a patient;
- a feature extraction unit configured to extract a first feature of a thin cap from the OCT image;
- a region-of-interest setting unit configured to set a region of interest included in the OCT image on a basis of the first feature;
- and
- a high risk determination unit configured to determine whether the region of interest includes a high risk lesion.
Dependencies are inferred from the claim text.