# Automatic Quantitative Coronary Analysis Based on Deep Learning
## 1 Introduction  
Coronary artery disease (CAD) is one of the leading causes of mortality in the world. According to the World Health Organization (WHO), in 2021, approximately 17.9 million people died from CAD, accounting for 31% of all global deaths. Furthermore, of these deaths, over 80% occurred in low-income and middle-income countries. Due to the lack of expertise and technology, small hospitals in rural and low-income countries may not be able to properly diagnose and treat CAD patients, which will lead to poor outcomes and high mortality rates. This emphasizes the importance of automatic analysis for coronary imaging data in smart auxiliary medical systems based on artificial intelligence (AI). In this study, we mainly focus on the AI-enabled automatic quantitative coronary analysis (QCA), a crucial technique for the diagnosis of CAD based on coronary angiogram (CAG) images. In clinical practice, CAG is the gold standard for percutaneous coronary intervention (PCI) surgery. It provides essential diagnostic imaging for interventional cardiologists to understand the patient's heart condition and make credible treatment decisions. QCA is a set of techniques used to measure the diameter and assess the stenosis degree of the coronary arteries on CAG images, which could promote rational clinical decision-making, risk assessment, and stent placement. QCA was first introduced in 1977 [1]. With the development of the digital imaging and communications in medicine (DICOM) system and computer image processing algorithms, QCA has become a more and more popular method for quantitatively assessing the results of percutaneous coronary intervention (PCI) surgeries [2; 3; 4]. Nowadays, the most commonly used QCA systems are CAAS II (PIE Medical, Maastricht, The Netherlands) and QAngio XA (Medis, Leiden, The Netherlands) [5; 6; 7], both of which have a similarprocess as follows: First, cardiologists must identify the coronary segment of interest, which focuses on the stenosis or lesion as reference segments. The proximal and distal edges of the coronary area should be relatively free of disease, and the mean value of the diameters of the lumen in these two areas should be calculated as the reference diameter. Then, based on the location of the area of interest, analysts manually trace the centerline, and edge detection algorithms are used to identify the margins of coronary artery segments. A series of diameters are calculated along the entire segment, and a trend line of the luminal diameters is drawn. Additionally, an interpolation algorithm is applied to the region between the generated margin line and a hypothetically normal coronary margin, highlighting the area of stenosis [8]. As a result, several parameters [9] are figured out by the QCA system: (1) lesion length (LL, mm), defined as the length of a coronary segment of interest between the proximal point and distal point; (2) minimal luminal diameter (MLD, mm), defined as the smallest diameter of lumen; (3) reference vessel diameter (RVD, mm), defined as the average diameter of coronary lumen free from the atherosclerotic illness; and (4) diameter stenosis (DS, \(n\%\)), defined as (RVD- MLD)/RVD. However, the QCA process and the existing systems heavily rely on manual operation, which is time-consuming and can even take cardiologists up to 20-30 min to complete accurate analyses for only one single CAG. Although many researchers have tried to improve corresponding algorithms by using various filter-based methods, such as the Hessian matrix-based features [10], radon-like features [11], and Gabor wavelet features [12], these methods are not efficient for clinical applications due to their complex computation and pixel-wise operations. Fortunately, with the advancements in artificial intelligence (AI) and computer vision in the field of medical imaging, QCA algorithms have the potential to be more automated and intelligent. In recent years, there has been a growing interest in using AI techniques to analyze CAG. [PERSON] et al.