## 4 Conclusions and Future Plan  
Catheterization is a procedure used to diagnose and treat various cardiovascular diseases. Intracardiac echocardiography (ICE) is an emerging imaging modality that has gained popularity in cardiac catheterization procedures due to its ability to provide high-resolution images of the heart and its surrounding structures in a minimally invasive manner. However, given its limited field of view, understanding its orientation within the heart is difficult simply from observing the acquired images. Therefore, ICE catheter tracking would be useful as a guide during the procedure. There are two traditional tracking methods for 3D tracking, (1) Electro-Magnetic (EM) sensors and (2) Bi-plane fluoroscopy. EM sensors offer the convenience of portability and real-time tracking, but their accuracy is limited to approximately 5 mm and they are restricted to certain types of catheters. Hence, they may not be well-suited for all cardiac catheterization procedures. For the second option, we have bi-plane fluoroscopy, and although it is more accurate than EM sensors and compatible with all catheter types, it provides only 5-DOF tracking, thereby omitting information about the roll angle of the catheter. To overcome these limitations, the current study presented a machine learning-based approach to predict the roll angle of an ICE catheter using bi-plane fluoroscopy images to have a full 6 DOF tracking system. The innovative approach of using machine learning models to track the catheter has several advantages over traditional EM sensors. Machine learning algorithms can analyze vast amounts of data and identify complex patterns that may be difficult for traditional sensors to detect. Additionally, this approach does not require any additional hardware, making it a cost-effective solution for catheter tracking. The results of the study showed that the developed model has a low error rate and a high degree of accuracy with Normalized Mean Square Error values of \(\sim\)\(3\times 10^{-3}\), an average error of \(\sim\)1.25% with Mean Absolute Error values of \(\sim\)4.5 degrees, and a standard deviation of less than 5 degrees. Additionally, the model demonstrated a high degree of accuracy with R-squared values of approximately 0.99. While the results of the study are promising, there are some limitations that need to be addressed in future research. Firstly, the model should be trained on clinical images with a greater complexity of background features to improve its accuracy in real-world scenarios. Secondly, it needs to be combined with co-registration algorithms that align the ICE catheter to the patient's anatomy for VR/AR/MR applications. Finally, a larger dataset that extracts values from images acquired from various machines, sites, and settings needs to be used to validate the model's generalizability. The results suggest that the developed model has the potential to provide 6-DOF tracking of an ICE catheter, enhancing the accuracy of VR/AR/MR-based real-time guidance systems that provide visualization and navigation during clinical procedures. This has significant implications for cardiac catheterization procedures, which are increasingly relying on these advanced imaging modalities. Accurate tracking of the catheter during the procedure will not only improve the accuracy of the diagnosis and treatment but also reduce the risks and complications associated with the procedure. In our future plan, the focus will be on incorporating feature extraction through image analysis of clinical data. The current study's predictive model employs scalar data obtained via open-source tracking software as its inputs, which does not necessitate feature extraction via image analysis. However, to facilitate the operation of the current model, the team is working on the development of a deep learning-based model that automatically extracts the relevant features from clinical images.