Abstract Body (Do not enter title and authors here): Backround:
Patients with Ischemia with non-obstructive coronary artery (INOCA) have symptoms of chronic myocardial ischemia without comorbid obstructive coronary artery disease. As a result, they often fail to receive timely diagnosis and treatment, increasing the risk of poor prognosis. Hyperspectral imaging (HSI), developed on the basis of multispectral remote sensing, can provide information on the spatial distribution of various tissue structures, analyze the chemical composition and physical characteristics of different diseases.
Objective:
In this study, we attempted to analyze the functional status of peripheral microvessels by HSI and thus identify patients with INOCA.
Methods:
This study was an observational cross-sectional study. The study included 500 patients with chest pain who underwent coronary angiography from December 2023 to May 2024 at Renmin Hospital of Wuhan University. We acquire HSI of the patient's face, palms and ears prior to coronary angiography. Patients were divided into a control group, an INOCA group, and a coronary artery blockage group according to guideline diagnostic criteria.
Results:
1. The data model (Model 1) was built by deep learning the waveband and texture data of HSI, and it showed good sensitivity and specificity for recognizing INOCA patients;2. The image model (Model 2) is built after deep learning of HSI features, and it also has good sensitivity and specificity for the diagnosis of INOCA patients. The specificity of the image model is higher than the data model, but the sensitivity is slightly worse;3. We constructed the composite model (Model 3) by fusing the data model with the image model. Compared to the data model and image model, the composite model showed higher sensitivity and specificity in the identification of INOCA patients.
Conclusion:
The model constructed based on deep learning of peripheral microvascular HSI can diagnose INOCA patients with high sensitivity and specificity.