Overview of AI and Machine Learning Methods for Outcome Prediction in Pediatric Congenital Heart Surgery

Abstract Purpose Congenital heart disease (CHD) constitutes the most common major congenital malformation. Despite the improvement of diagnostic technologies and the advances in pediatric cardiovascular surgery, the utilization of artificial intelligence (AI) holds a critical role in predicting post-operative outcomes in pediatric population. The primary aim of the study was to define the utilization of AI machine learning models in the prediction of post-operative outcome in children undergoing a congenital heart surgery. Methods Following the PRISMA guidelines, a systematic literature review was conducted by a comprehensive retrieval of two large databases. The inclusion and exclusion criteria were predefined. Two independent reviewers screened the articles. Results 12 articles included in the review published the last seven years. The included research papers were retrospective cohort studies with a range of size population from 71 to 24.685 pediatric patients. The majority of them examined the prediction performance of AI machine learning algorithms in mortality and other post-operative complications. Various types of congenital heart surgeries were described. The area under the curve (AUC) was used for model performance, ranged from 0.642 to 0.970. LightGBM outperformed with AUC 0.970 for the prediction of deep venous thrombosis (DVT). Conclusion AI machine learning models show greater discriminative power for the prediction of post-operative outcomes in pediatric patients undergoing a CHS surpass the traditional prediction risk tools. Further studies must be conducted to strengthen the explainable role of AI applications in clinical decision making.