# Real-Time Prediction of Transarterial Drug Delivery Based on a Deep Convolutional Neural Network
## 3 Results
### Prediction Results of the Network Model
#### 3.1.2 The Human Hepatic Arterial System  
Next, the network model was constructed for the hepatic artery system, building upon our previous study of the straight and bifurcated blood vessels. First, we present the prediction results at time t = 2.1 (t/T), for different injection locations, to investigate the prediction ability of the network model on different drug trajectories in this more complex flow geometry. Three representative concentration fields in the test dataset are presented in Figure 7, for three injection site locations: 0.2 R, \(-\)0.36 R and \(-\)0.8 R, measured from the central axis of the main vessel. As can be seen from the figure, the distribution of liquid drug into the different branches is highly dependent on the injection location. The error cloud map is unified to the same error range for the convenience of observation indicating the maximum relative errors of all computational domains are less than 6%. The greatest error occurs locally at very few points, which results in an extremely high average accuracy, more than 99.9% for all the three data points. Furthermore, Figure 8A-E compares the concentration distribution of the numerical simulation and the network prediction, with  
Figure 6: Drug concentration field and error distribution in the bifurcated vessel: (**a**–**d**) are respectively 0.8 R, 0.7 R, 0.55 R, \(-\)0.14 R from the central axis of the blood vessel.  
three various injection sites in Figure 7 at specified cross-sections, the schematic of the sections being presented in Figure 8F. Figure 8G compares the statistics of the total amount of drug in the various cross-sections between the numerical simulation and the network prediction. As previously, it is deduced that the proposed CNN-based model accurately predicts the drug concentration field and the quantity of drug delivered into each branch of the hepatic artery. In transarterial chemoembolization therapy, the amount of drug that ultimately flows to the targeted branch is critical to successful treatment. Hence, the good agreement in Figure 8G demonstrates the significant potential of the proposed model for assisting in clinical TACE treatment.  
Further studies are carried out to test the performance of the proposed model on the prediction of the concentration field at different times in the hepatic artery system. The three selected times are: t = 0.15 [t/T], t = 2.1 [t/T] and t = 2.175 [t/T], the distance between the injection site and the central axis of blood vessels being \(-\)0.25 R. It can be seen from Figure 9 that the maximum relative error is less than 2.5% and the average error is less than 0.025%. The area with high concentration shows a greater relative error and as the drug continues to spread, the concentration decreases and the accuracy of the prediction in the network model is more accurate.  
Figure 7: Drug concentration field and error distribution for the human hepatic artery at time t = 2.1 and three injection locations (**a**) 0.2 R, (**b**) \(-\)0.36 R and (**c**) \(-\)0.8 R from the central axis of the blood vessel.  
Figure 8: **(A–E)** Drug concentration profile along the specified cross-sections in the human hepatic artery; **(F)** schematic diagrams of the specified cross-sections corresponding to **(A–E)**; **(G)** statistics of the total amount of drug in the cross-sections.  
Figure 9: Drug concentration field and error distribution in the human hepatic artery at different times when the injection site is \(-0.25\) R away from the central axis of the blood vessel.