Page 281 - IJB-9-1
P. 281

International Journal of Bioprinting  Evaluation of advanced visual computing solutions for the left atrial appendage occlusion


               J Am Soc Echocardiogr, 33: 1316–1323.              https://doi.org/10.1016/j.tcm.2019.04.005
               https://doi.org/10.1016/j.echo.2020.08.005      23.  Nam HH, Herz C, Lasso A,  et al., 2020, Simulation of
                                                                  transcatheter atrial and ventricular septal defect device
            12.  Aguado AM, Olivares AL, Yagüe C,  et al., 2019,  In silico   closure  within  three-dimensional  echocardiography-
               optimization of left atrial appendage occluder implantation   derived heart models on screen and in virtual reality. J Am
               using interactive and modeling Tools. Front Physiol, 10: 237.
                                                                  Soc Echocardiogr, 33: 641–644.e2.
               https://doi.org/10.3389/fphys.2019.00237
                                                                  https://doi.org/10.1016/j.echo.2020.01.011
            13.  Vukicevic M, Mosadegh B, Min JK, et al., 2017, Cardiac 3D
               printing and its future directions. JACC Cardiovasc Imaging,   24.  Narang A, Hitschrich N, Mor-Avi V,  et al., 2020, Virtual
               10: 171–184.                                       reality analysis of three-dimensional  echocardiographic
                                                                  and cardiac computed tomographic data sets.  J Am Soc
               https://doi.org/10.1016/j.jcmg.2016.12.001         Echocardiogr, 33: 1306–1315.
            14.  Forte MNV, Hussain T, Roest A, et al., 2019, Living the heart      https://doi.org/10.1016/j.echo.2020.06.018
               in three dimensions: applications of 3D printing in CHD.   25.  Sanon S, Lim DS, 2019, Update  on left atrial appendage
               Cardiol Young, 29: 733–743.
                                                                  occlusion an overview of innovative preprocedural and
               https://doi.org/10.1017/S1047951119000398          intraprocedural imaging techniques, trial data, and current
            15.  Fan Y, Yang F, Cheung GSH, et al., 2019, Device sizing guided   laao devices and devices in development. Struct Heart Dis,
               by echocardiography-based three-dimensional printing   13: 35–41
               is associated with superior outcome after percutaneous   26.  Lindner S, Behnes M, Wenke A, et al., 2019, Assessment of
               left  atrial  appendage  occlusion.  J Am Soc Echocardiogr,   peri-device leaks after interventional left atrial appendage
               32: 708–719.e1.                                    closure using standardized imaging by cardiac computed
                                                                  tomography angiography.  Int J Cardiovasc Imaging,
               https://doi.org/10.1016/j.echo.2019.02.003
                                                                  35: 725–731.
            16.  Wang DD, Gheewala N, Shah R,  et al., 2018, Three-
               dimensional printing for planning of structural heart      https://doi.org/10.1007/s10554-018-1493-z
               interventions. Interv Cardiol Clin, 7: 415–423.  27.  Medina E, Aguado AM, Mill J, et al., 2020, VRIDAA: Virtual
                                                                  reality platform for  training and  planning  implantations
               https://doi.org/10.1016/j.iccl.2018.04.004
                                                                  of occluder devices in left atrial appendages. Eurographics
            17.  Eng MH, Wang DD, 2018, Computed tomography for left   Work Vis Comput Biol Med, 31–35.
               atrial appendage occlusion case planning.  Interv Cardiol
               Clin, 7: 367–378.                                  https://doi.org/10.2312/vcbm.20201168
                                                               28.  Ribeiro JM, Astudillo P, de Backer O, et al., 2022, Artificial
               https://doi.org/10.1016/j.iccl.2018.03.003
                                                                  intelligence  and  transcatheter  interventions  for  structural
            18.  Kim WD, Cho I, Kim YD, et al., 2022., Improving left atrial   heart disease: A glance at the (near) future, Trends Cardiovasc
               appendage occlusion device size determination by three-  Med, 32:153-159.
               dimensional printing-based preprocedural simulation.      https://doi.org/10.1016/j.tcm.2021.02.002
               Front Cardiovasc Med, 9: 830062.
                                                               29.  Corral-Acero J, Margara F, Marciniak M, et al., 2020, The
               https://doi.org/10.3389/fcvm.2022.830062
                                                                  “Digital Twin” to enable the vision of precision cardiology.
            19.  Conti M, Marconi S, Muscogiuri G, et al., 2019, Left atrial   Eur Heart J, 41: 4556–4564.
               appendage closure guided by 3d computed tomography
               printing technology: a case control study.  J Cardiovasc      https://doi.org/10.1093/eurheartj/ehaa159
               Comput Tomogr, 13: 336–339.                     30.  Garot P, Iriart X, Aminian A,  et al., 2020, Value of feops
                                                                  heartguide patient-specific computational simulations in the
               https://doi.org/10.1016/j.jcct.2018.10.024
                                                                  planning of left atrial appendage closure with the amplatzer
            20.  Mendez A, Hussain T, Hosseinpour AR, et al., 2019, Virtual   amulet closure device: Rationale and design of the predict-
               reality for preoperative planning in large ventricular septal   laa study. Open Hear, 7: e001326.
               defects. Eur Heart J, 40: 1092.
                                                                  https://doi.org/10.1136/openhrt-2020-001326
               https://doi.org/10.1093/eurheartj/ehy685
                                                               31.  Naci H, Salcher-Konrad M, Mcguire A, et al., 2019, Impact
            21.  Tandon A, Burkhardt BEU, Batsis  M,  et  al., 2019, Sinus   of predictive medicine on therapeutic decision making:
               venosus defects. JACC Cardiovasc Imaging, 12: 921–924.  A randomized controlled trial in congenital heart disease.
                                                                  NPJ Digit Med, 2: 17.
               https://doi.org/10.1016/j.jcmg.2018.10.013
                                                                  https://doi.org/10.1038/s41746-019-0085-1
            22.  Southworth MK, Silva JR, Silva JN, 2020, Use of extended
               realities in cardiology. Trends Cardiovasc Med, 30: 143–148.  32.  Otani T, Al-Issa A, Pourmorteza A,  et al., 2016, A


            Volume 9 Issue 1 (2023)                        273                      https://doi.org/10.18063/ijb.v9i1.640
   276   277   278   279   280   281   282   283   284   285   286