Novel Techniques in Imaging Congenital Heart Disease

Sachdeva, Ritu; Armstrong, Aimee K.; Arnaout, Rima; Grosse-Wortmann, Lars; Han, B. Kelly; Mertens, Luc; Moore, Ryan A.; Olivieri, Laura J.; Parthiban, Anitha; Powell, Andrew J.

doi:10.1016/j.jacc.2023.10.025

Cited by 4 publications

(3 citation statements)

References 100 publications

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“…The integration of AI tools with imaging technology allows better visualisation of complex congenital anatomy and improves the assessment of its physiology. These aspects are increasingly important in the complexity of congenital heart surgery and catheter-based procedures [69].…”

Section: Artificial Intelligence and Machine Learning Applied To Cong...mentioning

confidence: 99%

“…Cattapan et al performed an observational study involving cardiac surgery residents to assay the role of surgical simulation on 3D cardiac models as a promising innovative tool to improve understanding of cardiac anatomy, facilitate hands-on learning and enhance proficiency in surgical techniques and confidence levels when performing cardiac surgery [76]. Towards this trajectory, AI-driven continuous learning systems encourage ongoing skill development and provide personalised feedback and insights for surgeons [69].…”

Section: Artificial Intelligence and Machine Learning Applied To Cong...mentioning

confidence: 99%

“…Some innovative methods are in their early stages of experimentation and require additional refinement before achieving widespread acceptance in routine clinical applications [69]. For example, echocardiographers often select single frames for manual segmentation to avoid foreshortening of cardiac chambers or variation from an irregular heart rhythm, which could affect the analysis.…”

Section: Exploring Boundaries: Challenges In Applying Artificial Inte...mentioning

confidence: 99%

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How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease?

Pozza,

Zanella,

Castaldi

et al. 2024

JCM

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Improvements in medical technology have significantly changed the management of congenital heart disease (CHD), offering novel tools to predict outcomes and personalize follow-up care. By using sophisticated imaging modalities, computational models and machine learning algorithms, clinicians can experiment with unprecedented insights into the complex anatomy and physiology of CHD. These tools enable early identification of high-risk patients, thus allowing timely, tailored interventions and improved outcomes. Additionally, the integration of genetic testing offers valuable prognostic information, helping in risk stratification and treatment optimisation. The birth of telemedicine platforms and remote monitoring devices facilitates customised follow-up care, enhancing patient engagement and reducing healthcare disparities. Taking into consideration challenges and ethical issues, clinicians can make the most of the full potential of artificial intelligence (AI) to further refine prognostic models, personalize care and improve long-term outcomes for patients with CHD. This narrative review aims to provide a comprehensive illustration of how AI has been implemented as a new technological method for enhancing the management of CHD.

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Section: Artificial Intelligence and Machine Learning Applied To Cong...mentioning

confidence: 99%

Section: Artificial Intelligence and Machine Learning Applied To Cong...mentioning

confidence: 99%

Section: Exploring Boundaries: Challenges In Applying Artificial Inte...mentioning

confidence: 99%

See 1 more Smart Citation

How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease?

Pozza,

Zanella,

Castaldi

et al. 2024

JCM

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Immersive 3D Visualisation (3D Printing, Virtual Reality) enhances spatial understanding of complex congenital heart defects

Kappanayil,

Gurav,

Xavier

et al. 2024

Preprint

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Background: Diagnosing, managing complex CHD demands excellent morphological understanding. Individual differences in visuospatial skills, training and experience can impact spatial interpretation of volumetric cardiac imaging. Immersive 3D visualisation may help overcome these challenges, but evidence of clinical benefit is lacking. This study explores variability in visuospatial abilities and interpretation of conventionally viewed volumetric cardiac imaging data among members of a pediatric cardiac unit, and impact of using immersive 3D formats (3D-prints, Virtual Reality) on spatial understanding of complex CHD morphology. Methods: Prospective cohort study involving 9 heterogenous members of an advanced pediatric cardiac program [3 consultant cardiologists, 2 cardiothoracic surgeons, 1 cardiac radiologist, 3 cardiology trainees]. Participants visuospatial abilities were quantified using a validated test (Revised PSVT:R). Understanding of spatial relationships was assessed using structured questionnaires for 17 unique anonymised volumetric cardiac scans (15 CT, 2 MRI) of complex CHD visualised in three formats 1). conventional DICOM (CDICOM); 2). 3D prints (3DP); 3). Virtual Reality (VR). Accuracy, time taken, perceived level of difficulty, and confidence in interpretation were assessed and compared. Results : Spatial abilities varied widely (median 8, IQR 6-30), independent of expertise/experience. Limitations in conventional reading were significantly overcome with immersive 3D. Mean accuracy score of 60.48% +/- 17.13% with CDICOM increased to 83.93% +/- 7.84% with 3DP, and 90.81% +/- 5.03% with VR (p<0.001). 3DP and VR permitted significantly faster interpretation (p<0.001), with significantly better ease and confidence. Immersive 3D significantly narrowed gap between consultants and trainees. Conclusion: Immersive 3D visualisation of volumetric cardiovascular imaging can enhance spatial understanding of complex CHD morphology, overcoming challenges in spatial ability, experience, expertise.

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The effect of 3D modeling on family quality of life, surgical success, and patient outcomes in congenital heart diseases: objectives and design of a randomized controlled trial

Akça Sümengen,

İsmailoğlu,

İsmailoğlu

et al. 2024

Turk J Pediatr

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Background. Understanding the severity of the disease from the parents’ perspective can lead to better patient outcomes, improving both the child’s health-related quality of life and the family’s quality of life. The implementation of 3-dimensional (3D) modeling technology in care is critical from a translational science perspective. Aim. The purpose of this study is to determine the effect of 3D modeling on family quality of life, surgical success, and patient outcomes in congenital heart diseases. Additionally, we aim to identify challenges and potential solutions related to this innovative technology. Methods. The study is a two-group pretest-posttest randomized controlled trial protocol. The sample size is 15 in the experimental group and 15 in the control group. The experimental group’s heart models will be made from their own computed tomography (CT) images and printed using a 3D printer. The experimental group will receive surgical simulation and preoperative parent education with their 3D heart model. The control group will receive the same parent education using the standard anatomical model. Both groups will complete the Sociodemographic Information Form, the Surgical Simulation Evaluation Form - Part I-II, and the Pediatric Quality of Life Inventory (PedsQL) Family Impacts Module. The primary outcome of the research is the average PedsQL Family Impacts Module score. Secondary outcome measurement includes surgical success and patient outcomes. Separate analyses will be conducted for each outcome and compared between the intervention and control groups. Conclusions. Anomalies that can be clearly understood by parents according to the actual size and dimensions of the child’s heart will affect the preoperative preparation of the surgical procedure and the recovery rate in the postoperative period.

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Novel Techniques in Imaging Congenital Heart Disease

Cited by 4 publications

References 100 publications

How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease?

How Will Artificial Intelligence Shape the Future of Decision-Making in Congenital Heart Disease?

Immersive 3D Visualisation (3D Printing, Virtual Reality) enhances spatial understanding of complex congenital heart defects

The effect of 3D modeling on family quality of life, surgical success, and patient outcomes in congenital heart diseases: objectives and design of a randomized controlled trial

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