## 4 Conclusions  
We have shown that landmark detection using coordinate regression deep learning models can be used to perform catheter localizing on fluoroscopy images. Our results suggest that these models can provide \(\sim\)1% positional accuracy at speeds much faster (>10\(\times\)) than commercial acquisition systems used in cardiac interventions, and have the fastest training and inference times compared to other models. Furthermore, our method requires less time in preparing ground truth training datasets as compared to semantic segmentation methods (as typically used for U-Net). Although the results are promising, there are several limitations that need future improvement before these models can be used for clinical applications: (i) Nonclinical images: These models were trained on images acquired from 3D-printed models and, thus, do not have clinically relevant backgrounds; thus, clinical use should only be performed after the model has been optimized and validated on clinical images. (ii) Limited degrees of freedom: Currently, the model only predicts a single point on the catheter, giving a single 3D coordinate but no orientation; future models should predict two or more points to allow for the catheter's 3D orientation to be calculated. (iii) Generalizability: This model was trained on a set of images acquired from a single type of catheter, imaging system, and phantom model; future work needs to train the model on an expanded set of data. (iv) Accelerating and parallel processing must be considered in future works. Despite the limitations listed above, this work provides the primary advantage of developing deep learning models that can be optimized without the tedious task of developing image masks, as typically needed by U-Net models [22]. Furthermore, these models can be used to perform catheter tracking for training purposes for custom 3D-printed heart models [15] or on commercial training systems (Biomodex Inc., Paris, France). M.A. : Designed and implemented the DL model, programming code; [PERSON]: wrote the paper, statistical analysis; S.H. : programming code, designed the DL model; [PERSON] and A.C.: developed the control U-Net model results; [PERSON] and H.R. : designed and implemented the DL model; [PERSON] and M.R. : acquired the ground truth images; [PERSON], B.L., A.W. and M.S. : statistical analysis, manuscript revision; B.M. : project overview, wrote the paper. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The datasets and models used and/or analyzed during the current study are available on Github from the following link: [[https://github.com/mosadeghlabwcm/2023--Publication_Coordinate-Regression-Tracking](https://github.com/mosadeghlabwcm/2023--Publication_Coordinate-Regression-Tracking)]([https://github.com/mosadeghlabwcm/2023--Publication_Coordinate-Regression-Tracking](https://github.com/mosadeghlabwcm/2023--Publication_Coordinate-Regression-Tracking)), accessed on 16 February 2023. We thank the Dalio Institute of Cardiovascular Imaging for their continued support. The authors declare no conflict of interest. * [PERSON] _et al._ (2015) [PERSON]; [PERSON]; [PERSON] Global burden of CVD: Focus on secondary prevention of cardiovascular disease. _Int. J. Cardiol._**2015**, _201_ (Suppl. S1), S1-S7. [CrossRef]
* [PERSON] _et al._ (2017) [PERSON] Structural Heart Interventions. _Methodist Debakey Cardiovasc. J._**2017**, _13_, 96-97. [CrossRef]
* [PERSON] _et al._ (2017) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Incidence and management of inadvertent puncture and sheath placement in the aorta during attempted transseptal puncture. _Europace_**2017**, _19_, 447-457. [CrossRef] [PubMed]
* [PERSON] _et al._ (2012) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Side-by-side comparison of fluoroscopy, 2D and 3D TEE during percutaneous edge-to-edge mitral valve repair. _JACC Cardiovasc.