Abstract 4369164: Application of Artificial Intelligence (AI) for Predictive Modelling and Imaging in Cardiac Transplantation - A Systematic Review and Meta-Analysis

Heart transplantation remains the ultimate treatment option for patients with end stage heart disease, but donor supply remains insufficient, thereby complicating its allocation. Traditional scoring systems have demonstrated limited accuracy in predicting transplant outcomes, implying the need for more advanced approaches. Machine learning (ML) and artificial intelligence (AI) models show promising potential in predicting post-transplant prognosis, rejection risk and mortality by analyzing complex multidimensional variables beyond the capacity of conventional models. This meta analysis evaluates the application and performance of ML algorithms in image analysis and outcome prediction in patients undergoing cardiac transplantation. This meta analysis was conducted in accordance with the “Preferred Reporting Items for Systematic Reviews and Meta-analysis” (PRISMA) guidelines. An extensive search was conducted in all the major medical databases for relevant articles concerning machine learning algorithms and its application in cardiac transplantation. This was followed by an in-depth review of the included papers for relevant characteristics and outcomes. The statistical analysis was performed in R-Studio. Pooled area under the curve (AUC) was assessed using Ruttergatsonis model and the heterogeneity was assessed using the I^2 test. This review included a total of 17 papers with 512504 patients (prognostic elements) and 10 AI algorithms. Statistical analysis indicated pooled area under the curve(AUC) of 0.77[0.68;0.87,95%CI,p=0.9999]. A maximum AUC of 0.89 was observed with the RF algorithm by Miller et al and a minimum of 0.64 with the ANN algorithm by Lisboa et al and Nilsson et al. The papers were reviewed for relevant qualitativeas well as quantitative data pertaining to the performance of AI models. The capacity of AI algorithms in the domain of predicting cardiac transplant outcomes has been statistically established. The machine learning algorithms show promising clinical applications and utility in further enhancing the effectiveness of cardiac transplantation.