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Virtual Reality and Artificial Intelligence Paving the Future of Cardiac Interventions and Training, a Review of the Available Reviews

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Submitted:

24 September 2024

Posted:

26 September 2024

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Abstract

Several medical graduates, avoid surgical specialties especially those with complex and delicate anatomy due to their doubt in their visual spatial abilities. Many patients are deemed inoperable, particularly in the field of pediatric cardiology, due to the complexity of the anatomy. On a similar note, stratification of risk of cardiac interventions, and decision making, is a room of significant person-based and center-based bias. Two inter-related technologies, namely immersive virtual reality (VR) and artificial intelligence have made impossible things possible and can help to standardize decision making strategies. This review aimed at reviewing the main anatomical and lesion-based scopes of these advancements in the field of cardiac care, with a special focus on pediatric cardiology.

Keywords: 
Subject: Medicine and Pharmacology  -   Cardiac and Cardiovascular Systems

Background

Virtual reality (VR) and artificial intelligence (AI) are increasingly important in the field of cardiac interventions [1]. These technologies can potentially enhance medical training, improve surgical planning, and enable more precise procedures. By leveraging virtual reality, medical professionals can simulate complex surgical scenarios and practice difficult procedures in a safe and controlled environment [2]. Artificial intelligence, on the other hand, can assist in interpreting medical images, predicting patient outcomes, and optimizing treatment strategies. As these technologies continue to advance, they are expected to play a significant role in advancing the field of cardiac interventions and improving patient care [3].
Virtual reality technology has the potential to effectively address interpersonal gaps in surgical skills by providing a realistic and immersive training environment. By simulating surgical procedures and scenarios, VR can help bridge the gap in skills and experience among surgeons, ultimately leading to improved patient outcomes [4].
Moreover, artificial intelligence plays a crucial role in predicting patients’ outcomes through the analysis of vast amounts of medical data. By utilizing advanced algorithms, AI can identify patterns and trends that may not be apparent to human experts, enabling more accurate predictions of a patient’s prognosis and response to treatment [5]. This has the potential to significantly enhance personalized healthcare and improve overall patient care.
In this literature review, we aim to highlight the most important anatomic scopes of virtual reality and artificial intelligence in guiding pediatric cardiac interventions via analysis of the most important reviews of literature tackling this matter.

Main Body

Virtual Reality in Cardiology/Cardiac Surgery Training

The cognitive process of detecting targets in space, recognizing distance and directional relationships, and mentally altering their location is known as spatial skills. The ability to create, preserve, and work with mental pictures in space is known as visual spatial ability (VSA) [1].
Coming to the medical field, and while it is mandatory to evaluate the spatial ability for admission to undergraduate dental programs and aviation. surgical training programs do not require VSA testing before admission [6].
This might be one of the factors responsible for variations in outcomes of surgeries overall and pediatric cardiac surgeries.
An article by Kalun et al. suggested that “Measuring VSA in surgical trainees is important not only because the results suggest that individuals with higher VSA often demonstrate increased surgical performance, but also that those with higher VSA often require fewer training sessions to reach a certain performance point than their peers with lower VSA” [1].
The combination of virtual reality and artificial intelligence allows us to overcome the interpersonal variability of VSA. After traditional cross-sectional imaging is completed, a process called segmentation allows specialty engineers to convert 2D DICOM images to 3D VR Maker; which allows trainees and experienced surgeons to interact with those 3D models during pre-surgical planning and can theoretically improve the outcomes of cardiac surgeries and allow medical students with doubts over VSA to choose surgical specialties confidently.
One systematic review was found that discusses the results of VR training in tissue and non-tissue models, in cardiac surgical training [7].
We used this review to determine the number of studies involving immersive virtual reality training in cardiac interventions. We also analyzed the main fields where this technique is being implemented to improve the surgical skills of cardiothoracic residents.
The latter report depicted 27 studies and one catheter-based study was added during search [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35]; most of these involved training on mitral valve specimens (28%), and coronary surgeries (25%). Other fields of training included mimicking cardiopulmonary bypass, cardiac transplantation, TEE, and cardiac catheterization.
Figure 1 displays the respective percentages of each anatomical field of training.

Virtual Reality in Planning Cardiac Interventions

Current medical practice increasingly utilizes advanced 3D imaging modalities such as magnetic resonance imaging (MRI) and computed tomography (CT) to examine and assess complex congenital heart defects. These modalities enable comprehensive visualization of the cardiovascular anatomy and precise measurements of relevant intracardiac dimensions and volumes [36]. Nevertheless, the conventional representation of 3D anatomy through two-dimensional (2D) slices perpendicular to each other presents challenges in accurately assessing the intricate 3D structures and their spatial relationships. Overcoming these challenges involves reconstructing the patient-specific 3D anatomy using image processing techniques applied to CT or MRI data [37]. While various methods and software tools are available for this reconstruction, manual intervention is often necessary due to the intricate nature of the anatomy and the presence of imaging artifacts. The resulting 3D reconstructions can then be employed to enhance the comprehension of the complex anatomy [38].
Virtual reality has taken 3D reconstruction to a whole new level where reconstructed models are rendered interactable, with the aid of immersive virtual reality.
Steps include image acquisition and 3D modelling, then several programs are available, to import patient-specific models, and make them compatible with commercially available mounted headsets.
Our literature search identified a scoping review by Bakhuis et al. [39], which demonstrated that the number of case studies involving the use of VR in surgical planning for congenital heart disorders is lower than what is seen for education in training, with a total of ten studies, conotruncal anomalies, VSD open surgical closure, AV valve repair and pulmonary sequestration were the main field of the published reports (18%) (Figure 2) [40,41,42,43,44,45,46,47,48,49,50].
Another aspect worth noting was the imaging modality used for 3D modelling, CT accounted for 60% of the reported studies, while CMR for 30%, and only one case report was performed using 3D echocardiography. (Figure 3)
This shows the drawback of reconstructive imaging, which relies on either high radiation or lengthy imaging modalities.
There is still a long way to go to develop echocardiographic software, and 3D modalities in this bedside technique to be adaptable to immersive virtual reality and to spare the patients from lengthy, inconvenient procedures and from radiation.
Expertise also is another barrier to the generalization of VR reconstructive software, Figure 4 illustrates a VR-based reconstruction of a case of partial anomalous pulmonary venous return performed by non-trained individuals, by a user-friendly software [51].

Big Data, Is Artificial Intelligence, Substituting the Multidisciplinary Approach in Cardiac Surgery?

The development of advanced deep-learning technology now provides the chance to identify risk indicators that were not previously measurable. This technology can also evaluate intricate, interconnected patterns using easily accessible clinical data for predicting risks.
Sulague and colleagues [52], have published recently their preprint which served as a basis for identifying relevant AI-based studies in the field of cardiac interventions.
This review included 33 studies [53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85], that explored how machine learning can predict certain events such as major bleeding or mortality after different types of cardiac surgeries.
Regarding the specific fields tackled by the predictive model of machine learning, coronary surgeries, and heart transplantation were the most important point of focus, accounting for 39% of the studies tackling the role of AI in risk prediction (Figure 5).
A new software developed in Sinai Medical Center, aimed at using 12-lead ECG in risk prediction in non-cardiac surgeries. The newly manufactured software, PreOpnet was superior to the routinely used Revised Cardiac score index, in anticipating adverse events after non-cardiac surgeries [85].
Another recent review, published during the drafting of this manuscript, showed that >672 AI-based devices have already been approved by the FDA and might have an impact on the outcomes of cardiac patients, from planning to risk assessment.
Figure 6 is a flowchart illustrating the study selection process
Figure 7 is a flowchart illustrating the potential future uses of AI in cardiac care, many features are still under implementation.

Conclusion

AI and VR might be part of the present but the whole future. It is increasingly said in the small indoor chats of physicians that these emerging technologies will gradually reduce their jobs. But from another perspective, it is very unlikely that this would happen.
VR will help improve medical and surgical training. Nevertheless, it will allow better interaction with reconstructed models and will therefore improve surgical outcomes.
AI in the field of machine learning will help in better diagnosis and will shorten by its algorithm the interpretation time of images and other acquired data, which will not only improve risk prediction but will also give more time for healthcare personnel to develop their knowledge and get exposed to a larger number of patients.
Changing the perspective, from being replaced to absorbing the change and getting the advantages from it, is what physicians in all specialties need to start doing.
It is also wort mentioning that by the time this review was complete and written, many other VR and AI-based reports have been added to literature, which would not be included by the time this manuscript will be reviewed and published.

List of Abbreviations

AI Artificial intelligence
AV Atrioventricular
CMR Cardiac magnetic resonance
CT computed tomography
DM Decision making
DORV Double outlet right ventricle
KD Kawasaki disease
LVAD Left ventricular assist device
MAPCAs Main aorto-pulmonary connecting arteries
MV Mitral valve
TAVI Transcatheter aortic valve implantation
TEE Transesophageal echocardiography
Tx Transplantation
VR Virtual reality
VSA Visual spatial ability
VSD Ventricular septal defect
VSD Ventricular septal defect

Supplementary Materials

The following supporting information can be downloaded at the website of this paper posted on Preprints.org. Supplementary Table 1: Studies involving VR based training in cardiac surgery categorized by the anatomical scope. Supplementary Table 2: Studies involving immersive VR in pediatric cardiac surgery categorized by the type of treated lesion. Supplementary Table 3: Table 3: Studies involving use of AI in cardiac care classified by scope of interest.

Author Contributions

Conceptualization, AFA; Methodology, AFA, AN, GA, LM, MMA, RA; software, AFA, AN, GA, LM, MMA, RA; investigation, AFA, AN, GA, LM, MMA, RA; resources, AFA, AN, GA, LM, MMA, RA, data curation, AFA, AN, GA, LM, MMA, RA; writing—original draft preparation, AFA, AN, GA, LM, MMA, RA; writing—review and editing, AFA, AN, GA, LM, MMA, RA; supervision, AFA; project administration, AFA; funding acquisition, (non-applicable). All authors have read and agreed to the published version of the manuscript.”

Institutional Review Board Statement

not applicable as this study is a hypothesis/Review article.

Informed Consent Statement

not applicable as this study is a viewpoint/editorial

Data Availability Statement

All data is made available within the manuscript.

Acknowledgement

To the peacekeepers in every part of the world, in every community, every family and every tiny relationship. Peace keeping might sometimes look like weakness, but it requires utmost strength, We also wanted to thank Dr. Nadine El Husseiny for providing us with her artwork in the figures of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Sonnadara R, Kalun P, Dunn K, Wagner N, Pulakunta T (2020) Canadian Medical Education Journal Recent evidence on visual-spatial ability in surgical education: A scoping review Des preuves récentes sur les habiletés visuo-spatiales pour la formation en chirurgie : revue exploratoire. Can Med Educ J 2020:111–127.
  2. Ntakakis G, Plomariti C, Frantzidis C, Antoniou PE, Bamidis PD, Tsoulfas G (2023) Exploring the use of virtual reality in surgical education. World J Transplant 13:36–43. [CrossRef]
  3. Laspro M, Groysman L, Verzella AN, Kimberly LL, Flores RL (2023) The Use of Virtual Reality in Surgical Training: Implications for Education, Patient Safety, and Global Health Equity. Surgeries 4:635–646. [CrossRef]
  4. Khor WS, Baker B, Amin K, Chan A, Patel K, Wong J (2016) Augmented and virtual reality in surgery—the digital surgical environment: applications, limitations and legal pitfalls. Ann Transl Med 4:454–454. [CrossRef]
  5. Giordano C, Brennan M, Mohamed B, Rashidi P, Modave F, Tighe P (2021) Accessing Artificial Intelligence for Clinical Decision-Making. Front Digit Heal 3:. [CrossRef]
  6. Khine MS (2016) Visual-spatial ability in STEM education: Transforming research into practice. Vis Abil STEM Educ Transform Res into Pract 1–263. [CrossRef]
  7. Arjomandi Rad A, Hajzamani D, Sardari Nia P (2023) Simulation-based training in cardiac surgery: A systematic review. Interdiscip Cardiovasc Thorac Surg 37:. [CrossRef]
  8. Brandão CM de A, Dallan LRP, Dinato FJ, Monteiro R, Fiorelli AI, Jatene FB (2021) Evaluation method of training simulation on biological models for cardiovascular surgery residents. J Card Surg 36:2247–2252. [CrossRef]
  9. Sharma VJ, Barton C, Page S, Ganesh JS, Patel N, Pirone F, Lin Z, Kejriwal NK, El Gamel A, McCormack DJ, Meikle F (2021) Cardiac surgery simulation: a low-cost feasible option in an Australasian setting. ANZ J Surg 91:2042–2046. [CrossRef]
  10. Brown M, Krishnananthan N, Paul V (2022) Right heart catherisation – a virtual reality. Eur Heart J 43:2787. [CrossRef]
  11. Hermsen JL, Yang R, Burke TM, Dardas T, Jacobs LM, Verrier ED, Mokadam NA (2018) Development of a 3-D printing-based cardiac surgical simulation curriculum to teach septal myectomy. J Thorac Cardiovasc Surg 156:1139-1148.e3. [CrossRef]
  12. Kenny L, Booth K, Freystaetter K, Wood G, Reynolds G, Rathinam S, Moorjani N (2018) Training cardiothoracic surgeons of the future: The UK experience. J Thorac Cardiovasc Surg 155:2526-2538.e2. [CrossRef]
  13. Bettati P, Dormer JD, Young J, Shahedi M, Fei B (2021) Virtual reality assisted cardiac catheterization. 82. [CrossRef]
  14. Spooner AJ, Faulkner CM, Novick RJ, Kent WDT (2019) Optimizing Surgical Skills in Cardiac Surgery Residents with Cardiac Transplant in the High-Fidelity Porcine Model. Innov Technol Tech Cardiothorac Vasc Surg 14:37–42. [CrossRef]
  15. Joyce DL, Lahr BD, Maltais S, Said SM, Stulak JM, Nuttall GA, Joyce LD (2018) Integration of simulation components enhances team training in cardiac surgery. J Thorac Cardiovasc Surg 155:2518-2524.e5. [CrossRef]
  16. Hermsen JL, Mohamadipanah H, Yang S, Wise B, Fiedler A, DiMusto P, Pugh C (2021) Multimodal Cardiopulmonary Bypass Skills Assessment Within a High-Fidelity Simulation Environment. Ann Thorac Surg 112:652–660. [CrossRef]
  17. Hicks GL, Gangemi J, Angona RE, Ramphal PS, Feins RH, Fann JI (2011) Cardiopulmonary bypass simulation at the Boot Camp. J Thorac Cardiovasc Surg 141:284–292. [CrossRef]
  18. Fouilloux V, Gsell T, Lebel S, Kreitmann B, Berdah S (2014) Assessment of team training in management of adverse acute events occurring during cardiopulmonary bypass procedure: a pilot study based on an animal simulation model (Fouilloux, Team training in cardiac surgery). Perfusion 29:44–52. [CrossRef]
  19. Premyodhin N, Mandair D, Ferng AS, Leach TS, Palsma RP, Albanna MZ, Khalpey ZI (2018) 3D printed mitral valve models: affordable simulation for robotic mitral valve repair. Interact Cardiovasc Thorac Surg 26:71–76. [CrossRef]
  20. Smelt J, Corredor C, Edsell M, Fletcher N, Jahangiri M, Sharma V (2015) Simulation-based learning of transesophageal echocardiography in cardiothoracic surgical trainees: A prospective, randomized study. J Thorac Cardiovasc Surg 150:22–25. [CrossRef]
  21. Valdis M, Chu MWA, Schlachta C, Kiaii B (2016) Evaluation of robotic cardiac surgery simulation training: A randomized controlled trial. J Thorac Cardiovasc Surg 151:1498-1505.e2. [CrossRef]
  22. Valdis M, Chu MWA, Schlachta CM, Kiaii B (2015) Validation of a Novel Virtual Reality Training Curriculum for Robotic Cardiac Surgery a Randomized Trial. Innov Technol Tech Cardiothorac Vasc Surg 10:383–388. [CrossRef]
  23. Sardari Nia P, Heuts S, Daemen J, Luyten P, Vainer J, Hoorntje J, Cheriex E, Maessen J (2016) Preoperative planning with three-dimensional reconstruction of patient’s anatomy, rapid prototyping and simulation for endoscopic mitral valve repair. Interact Cardiovasc Thorac Surg ivw308. [CrossRef]
  24. Duffy MC, Ibrahim M, Lachapelle K (2019) Development of a saphenous vein harvest model for simulation-based assessment. J Thorac Cardiovasc Surg 157:1082–1089. [CrossRef]
  25. Sardari Nia P, Daemen JHT, Maessen JG (2019) Development of a high-fidelity minimally invasive mitral valve surgery simulator. J Thorac Cardiovasc Surg 157:1567–1574. [CrossRef]
  26. Russo M, Koenigshofer M, Stoiber M, Werner P, Gross C, Kocher A, Laufer G, Moscato F, Andreas M (2020) Advanced three-dimensionally engineered simulation model for aortic valve and proximal aorta procedures. Interact Cardiovasc Thorac Surg 30:887–895. [CrossRef]
  27. Yasuda S, Van den Eynde J, Vandendriessche K, Masuda M, Meyns B, Oosterlinck W (2021) Implementation of a beating heart system for training in off-pump and minimally invasive coronary artery bypass. BMC Surg 21:26. [CrossRef]
  28. Jebran A-F, Saha S, Waezi N, Al-Ahmad A, Niehaus H, Danner BC, Baraki H, Kutschka I (2019) Design and training effects of a physical reality simulator for minimally invasive mitral valve surgery. Interact Cardiovasc Thorac Surg 29:409–415. [CrossRef]
  29. Feins RH, Burkhart HM, Conte J V., Coore DN, Fann JI, Hicks GL, Nesbitt JC, Ramphal PS, Schiro SE, Shen KR, Sridhar A, Stewart PW, Walker JD, Mokadam NA (2017) Simulation-Based Training in Cardiac Surgery. Ann Thorac Surg 103:312–321. [CrossRef]
  30. Nesbitt JC, St Julien J, Absi TS, Ahmad RM, Grogan EL, Balaguer JM, Lambright ES, Deppen SA, Wu H, Putnam JB (2013) Tissue-based coronary surgery simulation: Medical student deliberate practice can achieve equivalency to senior surgery residents. J Thorac Cardiovasc Surg 145:1453–1459. [CrossRef]
  31. Joyce DL, Dhillon TS, Caffarelli AD, Joyce DD, Tsirigotis DN, Burdon TA, Fann JI (2011) Simulation and skills training in mitral valve surgery. J Thorac Cardiovasc Surg 141:107–112. [CrossRef]
  32. Tavlasoglu M, Durukan AB, Arslan Z, Kurkluoglu M, Amrahov A, Jahollari A (2013) Evaluation of Skill-Acquisition Process in Mitral Valve Repair Techniques: A Simulation-Based Study. J Surg Educ 70:318–325. [CrossRef]
  33. Zhang L-F, Feng H-B, Yu Z-G, Jing S, Wan F (2018) Surgical Training Improves Performance in Minimally Invasive Left Ventricular Assist Device Implantation Without Cardiopulmonary Bypass. J Surg Educ 75:195–199. [CrossRef]
  34. Arango S, Gorbaty B, Tomhave N, Shervheim D, Buyck D, Porter ST, Iaizzo PA, Perry TE (2023) A High-Resolution Virtual Reality-Based Simulator to Enhance Perioperative Echocardiography Training. J Cardiothorac Vasc Anesth 37:299–305. [CrossRef]
  35. Luo X, Luo F, Li B, Li B, Tang Y, Sun H (2020) A tissue-based simulation model for cardiopulmonary bypass cannulation/decannulation training. Perfusion 35:680–686. [CrossRef]
  36. Kiraly L (2018) Three-dimensional modelling and three-dimensional printing in pediatric and congenital cardiac surgery. Transl Pediatr 7:129–138. [CrossRef]
  37. Yoo SJ, Hussein N, Peel B, Coles J, Arsdell GS va., Honjo O, Haller C, Lam CZ, Seed M, Barron D (2021) 3D Modeling and Printing in Congenital Heart Surgery: Entering the Stage of Maturation. Front Pediatr 9:1–11. [CrossRef]
  38. Batteux C, Haidar MA, Bonnet D (2019) 3D-printed models for surgical planning in complex congenital heart diseases: A systematic review. Front Pediatr 7:1–8. [CrossRef]
  39. Bakhuis W, Max SA, Maat APWM, Bogers AJJC, Mahtab EAF, Sadeghi AH (2023) Preparing for the future of cardiothoracic surgery with virtual reality simulation and surgical planning: a narrative review. Shanghai Chest 7:0–3. [CrossRef]
  40. Nanchahal S, Arjomandi Rad A, Naruka V, Chacko J, Liu G, Afoke J, Miller G, Malawana J, Punjabi P (2024) Mitral valve surgery assisted by virtual and augmented reality: Cardiac surgery at the front of innovation. Perfusion 39:244–255. [CrossRef]
  41. Pushparajah K, Chu KYK, Deng S, Wheeler G, Gomez A, Kabir S, Schnabel JA, Simpson JM (2021) Virtual reality three-dimensional echocardiographic imaging for planning surgical atrioventricular valve repair. JTCVS Tech 7:269–277. [CrossRef]
  42. Pelizzo G, Costanzo S, Roveri M, Lanfranchi G, Vertemati M, Milani P, Zuccotti G, Cassin S, Panfili S, Rizzetto F, Campari A, Camporesi A, Calcaterra V (2022) Developing Virtual Reality Head Mounted Display (HMD) Set-Up for Thoracoscopic Surgery of Complex Congenital Lung MalFormations in Children. Children 9:50. [CrossRef]
  43. Ong CS, Krishnan A, Huang CY, Spevak P, Vricella L, Hibino N, Garcia JR, Gaur L (2018) Role of virtual reality in congenital heart disease. Congenit Heart Dis 13:357–361. [CrossRef]
  44. Mendez A, Hussain T, Hosseinpour A-R, Valverde I (2019) Virtual reality for preoperative planning in large ventricular septal defects. Eur Heart J 40:1092–1092. [CrossRef]
  45. Ghosh RM, Mascio CE, Rome JJ, Jolley MA, Whitehead KK (2021) Use of Virtual Reality for Hybrid Closure of Multiple Ventricular Septal Defects. JACC Case Reports 3:1579–1583. [CrossRef]
  46. Ramaswamy R, Marimuthu S, Ramarathnam K, Vijayasekharan S, Rao KS, Balakrishnan K (2021) Virtual reality-guided left ventricular assist device implantation in pediatric patient: Valuable presurgical tool. Ann Pediatr Cardiol 14:388. [CrossRef]
  47. Sadeghi AH, Taverne YJHJ, Bogers AJJC, Mahtab EAF (2020) Immersive virtual reality surgical planning of minimally invasive coronary artery bypass for Kawasaki disease. Eur Heart J 41:3279–3279. [CrossRef]
  48. Ayerbe VMC, Morales MLV, Rojas CJL, Cortés MLA (2020) Visualization of 3D Models Through Virtual Reality in the Planning of Congenital Cardiothoracic Anomalies Correction: An Initial Experience. World J Pediatr Congenit Hear Surg 11:627–629. [CrossRef]
  49. van de Woestijne PC, Bakhuis W, Sadeghi AH, Peek JJ, Taverne YJHJ, Bogers AJJC (2021) 3D Virtual Reality Imaging of Major Aortopulmonary Collateral Arteries: A Novel Diagnostic Modality. World J Pediatr Congenit Hear Surg 12:765–772. [CrossRef]
  50. Abjigitova D, Sadeghi AH, Peek JJ, Bekkers JA, Bogers AJJC, Mahtab EAF (2022) Virtual Reality in the Preoperative Planning of Adult Aortic Surgery: A Feasibility Study. J Cardiovasc Dev Dis 9:31. [CrossRef]
  51. Bertelli F, Raimondi F, Godard C, Bergonzoni E, Cattapan C, Gastino E, Galliotto F, Boddaert N, El Beheiry M, Masson J-B, Guariento A, Vida VL (2023) Fast-track virtual reality software to facilitate 3-dimensional reconstruction in congenital heart disease. Interdiscip Cardiovasc Thorac Surg 36:. [CrossRef]
  52. Sulague RM, Beloy FJ, Medina JR, Mortalla ED, Cartojano TD, Macapagal S, Kpodonu J (2024) Artificial intelligence in cardiac surgery: A systematic review. World J Surg 48:2073–2089. [CrossRef]
  53. Agasthi P, Ashraf H, Pujari SH, Girardo ME, Tseng A, Mookadam F, Venepally NR, Buras M, Khetarpal BK, Allam M, Eleid MF, Greason KL, Beohar N, Siegel RJ, Sweeney J, Fortuin FD, Holmes DR, Arsanjani R (2021) Artificial Intelligence Trumps TAVI2-SCORE and CoreValve Score in Predicting 1-Year Mortality Post-Transcatheter Aortic Valve Replacement. Cardiovasc Revascularization Med 24:33–41. [CrossRef]
  54. Lo Muzio FP, Rozzi G, Rossi S, Luciani GB, Foresti R, Cabassi A, Fassina L, Miragoli M (2021) Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects. J Clin Med 10:5330. [CrossRef]
  55. Hernandez-Suarez DF, Kim Y, Villablanca P, Gupta T, Wiley J, Nieves-Rodriguez BG, Rodriguez-Maldonado J, Feliu Maldonado R, da Luz Sant’Ana I, Sanina C, Cox-Alomar P, Ramakrishna H, Lopez-Candales A, O’Neill WW, Pinto DS, Latib A, Roche-Lima A (2019) Machine Learning Prediction Models for In-Hospital Mortality After Transcatheter Aortic Valve Replacement. JACC Cardiovasc Interv 12:1328–1338. [CrossRef]
  56. Aranda-Michel E, Sultan I, Kilic A, Bianco V, Brown JA, Serna-Gallegos D (2022) A machine learning approach to model for end-stage liver disease score in cardiac surgery. J Card Surg 37:29–38. [CrossRef]
  57. Ayers B, Sandholm T, Gosev I, Prasad S, Kilic A (2021) Using machine learning to improve survival prediction after heart transplantation. J Card Surg 36:4113–4120. [CrossRef]
  58. Fan Y, Dong J, Wu Y, Shen M, Zhu S, He X, Jiang S, Shao J, Song C (2022) Development of machine learning models for mortality risk prediction after cardiac surgery. Cardiovasc Diagn Ther 12:12–23. [CrossRef]
  59. Hu L-H, Betancur J, Sharir T, Einstein AJ, Bokhari S, Fish MB, Ruddy TD, Kaufmann PA, Sinusas AJ, Miller EJ, Bateman TM, Dorbala S, Di Carli M, Germano G, Commandeur F, Liang JX, Otaki Y, Tamarappoo BK, Dey D, Berman DS, Slomka PJ (2020) Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry. Eur Hear J - Cardiovasc Imaging 21:549–559. [CrossRef]
  60. Luo L, Huang SQ, Liu C, Liu Q, Dong S, Yue Y, Liu KZ, Huang L, Wang SJ, Li HY, Zheng S, Wu ZK (2022) Machine Learning–Based Risk Model for Predicting Early Mortality After Surgery for Infective Endocarditis. J Am Heart Assoc 11:. [CrossRef]
  61. Molina RS, Molina-Rodríguez MA, Rincón FM, Maldonado JD (2022) Cardiac Operative Risk in Latin America: A Comparison of Machine Learning Models vs EuroSCORE-II. Ann Thorac Surg 113:92–99. [CrossRef]
  62. Kampaktsis PN, Siouras A, Doulamis IP, Moustakidis S, Emfietzoglou M, Van den Eynde J, Avgerinos D V., Giannakoulas G, Alvarez P, Briasoulis A (2023) Machine learning-based prediction of mortality after heart transplantation in adults with congenital heart disease: A UNOS database analysis. Clin Transplant 37:. [CrossRef]
  63. Kampaktsis PN, Tzani A, Doulamis IP, Moustakidis S, Drosou A, Diakos N, Drakos SG, Briasoulis A (2021) State-of-the-art machine learning algorithms for the prediction of outcomes after contemporary heart transplantation: Results from the UNOS database. Clin Transplant 35:. [CrossRef]
  64. Agasthi P, Buras MR, Smith SD, Golafshar MA, Mookadam F, Anand S, Rosenthal JL, Hardaway BW, DeValeria P, Arsanjani R (2020) Machine learning helps predict long-term mortality and graft failure in patients undergoing heart transplant. Gen Thorac Cardiovasc Surg 68:1369–1376. [CrossRef]
  65. Hasimbegovic E, Papp L, Grahovac M, Krajnc D, Poschner T, Hasan W, Andreas M, Gross C, Strouhal A, Delle-Karth G, Grabenwöger M, Adlbrecht C, Mach M (2021) A Sneak-Peek into the Physician’s Brain: A Retrospective Machine Learning-Driven Investigation of Decision-Making in TAVR versus SAVR for Young High-Risk Patients with Severe Symptomatic Aortic Stenosis. J Pers Med 11:1062. [CrossRef]
  66. Zhou Y, Chen S, Rao Z, Yang D, Liu X, Dong N, Li F (2021) Prediction of 1-year mortality after heart transplantation using machine learning approaches: A single-center study from China. Int J Cardiol 339:21–27. [CrossRef]
  67. Shou BL, Chatterjee D, Russel JW, Zhou AL, Florissi IS, Lewis T, Verma A, Benharash P, Choi CW (2022) Pre-operative Machine Learning for Heart Transplant Patients Bridged with Temporary Mechanical Circulatory Support. J Cardiovasc Dev Dis 9:311. [CrossRef]
  68. Li Y, Xu J, Wang Y, Zhang Y, Jiang W, Shen B, Ding X (2020) A novel machine learning algorithm, Bayesian networks model, to predict the high-risk patients with cardiac surgery-associated acute kidney injury. Clin Cardiol 43:752–761. [CrossRef]
  69. Karri R, Kawai A, Thong YJ, Ramson DM, Perry LA, Segal R, Smith JA, Penny-Dimri JC (2021) Machine Learning Outperforms Existing Clinical Scoring Tools in the Prediction of Postoperative Atrial Fibrillation During Intensive Care Unit Admission After Cardiac Surgery. Hear Lung Circ 30:1929–1937. [CrossRef]
  70. Park J, Bonde PN (2022) Machine Learning in Cardiac Surgery: Predicting Mortality and Readmission. ASAIO J 68:1490–1500. [CrossRef]
  71. Li T, Yang Y, Huang J, Chen R, Wu Y, Li Z, Lin G, Liu H, Wu M (2022) Machine learning to predict post-operative acute kidney injury stage 3 after heart transplantation. BMC Cardiovasc Disord 22:1–9. [CrossRef]
  72. Kim RB, Alge OP, Liu G, Biesterveld BE, Wakam G, Williams AM, Mathis MR, Najarian K, Gryak J (2022) Prediction of postoperative cardiac events in multiple surgical cohorts using a multimodal and integrative decision support system. Sci Rep 12:11347. [CrossRef]
  73. Kilic A, Goyal A, Miller JK, Gleason TG, Dubrawksi A (2021) Performance of a Machine Learning Algorithm in Predicting Outcomes of Aortic Valve Replacement. Ann Thorac Surg 111:503–510. [CrossRef]
  74. Fernandes MPB, Armengol de la Hoz M, Rangasamy V, Subramaniam B (2021) Machine Learning Models with Preoperative Risk Factors and Intraoperative Hypotension Parameters Predict Mortality After Cardiac Surgery. J Cardiothorac Vasc Anesth 35:857–865. [CrossRef]
  75. Truong VT, Beyerbach D, Mazur W, Wigle M, Bateman E, Pallerla A, Ngo TNM, Shreenivas S, Tretter JT, Palmer C, Kereiakes DJ, Chung ES (2021) Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement. Pacing Clin Electrophysiol 44:334–340. [CrossRef]
  76. Thalappillil R, Datta P, Datta S, Zhan Y, Wells S, Mahmood F, Cobey FC (2020) Artificial Intelligence for the Measurement of the Aortic Valve Annulus. J Cardiothorac Vasc Anesth 34:65–71. [CrossRef]
  77. Xue X, Chen W, Chen X (2022) A Novel Radiomics-Based Machine Learning Framework for Prediction of Acute Kidney Injury-Related Delirium in Patients Who Underwent Cardiovascular Surgery. Comput Math Methods Med 2022:1–16. [CrossRef]
  78. Chang Junior J, Binuesa F, Caneo LF, Turquetto ALR, Arita ECTC, Barbosa AC, Fernandes AM da S, Trindade EM, Jatene FB, Dossou P-E, Jatene MB (2020) Improving preoperative risk-of-death prediction in surgery congenital heart defects using artificial intelligence model: A pilot study. PLoS One 15:e0238199. [CrossRef]
  79. Zea-Vera R, Ryan CT, Havelka J, Corr SJ, Nguyen TC, Chatterjee S, Wall MJ, Coselli JS, Rosengart TK, Ghanta RK (2022) Machine Learning to Predict Outcomes and Cost by Phase of Care After Coronary Artery Bypass Grafting. Ann Thorac Surg 114:711–719. [CrossRef]
  80. Allyn J, Allou N, Augustin P, Philip I, Martinet O, Belghiti M, Provenchere S, Montravers P, Ferdynus C (2017) A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis. PLoS One 12:e0169772. [CrossRef]
  81. Bodenhofer U, Haslinger-Eisterer B, Minichmayer A, Hermanutz G, Meier J (2021) Machine learning-based risk profile classification of patients undergoing elective heart valve surgery. Eur J Cardio-Thoracic Surg 60:1378–1385. [CrossRef]
  82. Gao Y, Liu X, Wang L, Wang S, Yu Y, Ding Y, Wang J, Ao H (2022) Machine learning algorithms to predict major bleeding after isolated coronary artery bypass grafting. Front Cardiovasc Med 9:. [CrossRef]
  83. Jiang H, Liu L, Wang Y, Ji H, Ma X, Wu J, Huang Y, Wang X, Gui R, Zhao Q, Chen B (2021) Machine Learning for the Prediction of Complications in Patients After Mitral Valve Surgery. Front Cardiovasc Med 8:. [CrossRef]
  84. Evertz R, Lange T, Backhaus SJ, Schulz A, Beuthner BE, Topci R, Toischer K, Puls M, Kowallick JT, Hasenfuß G, Schuster A (2022) Artificial Intelligence Enabled Fully Automated CMR Function Quantification for Optimized Risk Stratification in Patients Undergoing Transcatheter Aortic Valve Replacement. J Interv Cardiol 2022:1–9. [CrossRef]
  85. He K, Liang W, Liu S, Bian L, Xu Y, Luo C, Li Y, Yue H, Yang C, Wu Z (2022) Long-term single-lead electrocardiogram monitoring to detect new-onset postoperative atrial fibrillation in patients after cardiac surgery. Front Cardiovasc Med 9:. [CrossRef]
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Figure 1. Percentages of scopes of VR-based cardiac surgery training. Abbreviations: TEE: transesophageal training.
Figure 1. Percentages of scopes of VR-based cardiac surgery training. Abbreviations: TEE: transesophageal training.
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Figure 2. Percentages of distribution of immersive VR assisted cardiac surgeries. Abbreviations: AV: atrioventricular, DORV: double outlet right ventricle, KD: Kawasaki disease, LVAD: Left ventricular assisted device, MAPCAs: Main aortopulmonary connecting arteries, VSD: Ventricular septal defects.
Figure 2. Percentages of distribution of immersive VR assisted cardiac surgeries. Abbreviations: AV: atrioventricular, DORV: double outlet right ventricle, KD: Kawasaki disease, LVAD: Left ventricular assisted device, MAPCAs: Main aortopulmonary connecting arteries, VSD: Ventricular septal defects.
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Figure 3. Percentages of distribution of imaging modalities used for VR reconstruction. Abbreviations: CMR: Cardiac magnetic resonance, CT: computed tomography.
Figure 3. Percentages of distribution of imaging modalities used for VR reconstruction. Abbreviations: CMR: Cardiac magnetic resonance, CT: computed tomography.
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Figure 4. Reconstructions of case 2 generated by the participants. The diagnosis was partial anomalous pulmonary venous [51].
Figure 4. Reconstructions of case 2 generated by the participants. The diagnosis was partial anomalous pulmonary venous [51].
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Figure 5. Number of studies employing machine-learning and artificial intelligence in decision making in cardiac surgery, categorized by scope. Abbreviations: DM: decision-making, MV: mitral valve, TAVI: Transcatheter aortic valve implantation. Tx: Transplantation.
Figure 5. Number of studies employing machine-learning and artificial intelligence in decision making in cardiac surgery, categorized by scope. Abbreviations: DM: decision-making, MV: mitral valve, TAVI: Transcatheter aortic valve implantation. Tx: Transplantation.
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Figure 6. Flow chart of the studies selection process.
Figure 6. Flow chart of the studies selection process.
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Figure 7. Future uses of AI in cardiac care, a promising journey.
Figure 7. Future uses of AI in cardiac care, a promising journey.
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