# Automatic Quantitative Coronary Analysis Based on Deep Learning
## 3 Results
### Evaluation of AI-QCA  
DS, MLD, RVD, and LL were evaluated on AI-QCA compared to QCA. The QCA results are generated from the reports by experts using the Medis QAngio XA 7.3 system. Figure 9 shows the comparison of AI-QCA and QCA, with root mean square error (RMSE) and Pearson coefficient taken as the measurements. As a result, the RMSE of DS, MLD, RVD, and LL are 0.064, 0.306, 0.421, and 3.984 for all vessels, 0.063, 0.321, 0.480, and 4.299 for LAD, 0.061, 0.290, 0.370, and 3.010 for LCX, 0.058, 0.281, 0.312, and 3.650 for RCA, respectively. The Pearson coefficients of DS, MLD, RVD, and LL are 0.765, 0.700, 0.664, and 0.777 for all vessels, 0.771, 0.742, 0.667, and 0.776 for LAD, 0.821, 0.725, 0.667, and 0.785 for LCX, and 0.802, 0.647, 0.782, and 0.763 for RCA, respectively. All the \(p\)-values are lower than 0.001.  
Examples of AI-QCA applied to LAD, LCX, and RCA are shown in Figures 10-12. According to the comparison of (a) and (b) in the figures, the stenosis areas, and fitting lines of AI-QCA are comparable to those of QCA, with similar fitting CAG and fitting diameter. The DS, MLD, RVD, and LL are also very close. The comparison of (a) and (b) in the figures demonstrates that the stenosis areas and fitting lines generated by the AI-QCA are consistent with those produced by the QCA, exhibiting similar fitting CAG and fitting diameters. Additionally, the DS, MLD, RVD, and LL values obtained from the AI-QCA are also in close agreement with those generated by the QCA. Furthermore, the comparison of (c) and (d) shows that the diameter curves and diameter fitting lines produced by AI-QCA are similar to those obtained by manual QCA, and the locations of stenosis areas generated by the AI-QCA (proximal, ostial, and distal) are also highly consistent with those generated by experts. Due to its fully automated nature, the AI-QCA process reduces labor costs and saves time, as it can analyze a CAG in just 8 s, while the manual QCA process can take up to 20-30 min to complete.  
Figure 8: Distance variance along the CAT generated by BasNet and Pix2 Pix. Subfigure (**a**–**c**) represents the distance variance of CAT for LAD, LCX and RCA, respectively. CAT = coronary artery tree; LAD = left anterior descending; LCX = left circumflex artery; RCA = right coronary artery.  
Figure 9: The correlation of AI-QCA and QCA. Row 1 to row 4 represent DS, MLD, RVD, and LL, and column 1 to column 4 represent all vessels, LAD, LCX, and RCA, respectively. The dotted green line represents the values when AI-QCA equals QCA. The Pearson coefficient and RMSE are shown in the lower right area. CAT = coronary artery tree; LAD = left anterior descending; LCX = left circumflex artery; RCA = right coronary artery.  
display the fitting diameter with reference fitting lines of QCA and AI-QCA. In (**c**), the proximal, ostial, and distal locations are indicated by the labels \"\(p\)\", \"\(o\)\", and \"\(d\)\". In (**d**), these locations are indicated by vertical lines in pink, dark red, and green, respectively. QCA = quantitative coronary analysis; LCX = left circumflex artery; DS = diameter stenosis; MLD = minimal luminal diameter; RVD = reference vessel diameter; and LL = lesion length.