Role of Artificial Intelligence in Cardiac Homograft Banking: A Comprehensive Review for the Neonatal and Paediatric Cardiac Surgical Community
Keywords:
artificial intelligence, cardiac homograft, heart valve banking, machine learning, deep learning, cryopreservation, neonatal cardiac surgery, tissue banking, natural language processing, outcome predictionAbstract
Background: Cardiac homograft banking—encompassing donor identification, tissue procurement, antibiotic processing, cryopreservation, inventory management, and post-implantation surveillance—represents a data-rich operational environment uniquely amenable to artificial intelligence (AI) augmentation. Homografts remain the conduit of choice for neonatal and paediatric cardiac surgery, particularly in Ross procedures and right ventricular outflow tract reconstruction, yet longstanding challenges in quality assurance, tissue utilisation, and equitable distribution have constrained programme capacity globally.
Objective: To conduct a comprehensive, structured review of current and emerging AI applications across the cardiac homograft banking value chain, evaluating evidence quality, practical implementation requirements, and specific implications for resource-limited programmes in developing nations.
Methods: A narrative synthesis informed by a systematic search of PubMed/MEDLINE, EMBASE, and the Cochrane Library (January 2010–March 2025) was undertaken. Search terms combined controlled vocabulary for artificial intelligence, machine learning, deep learning, and natural language processing with terms for cardiac surgery, heart valve banking, homograft, and cryopreservation. Seventy-four primary studies and policy documents met inclusion criteria. Expert consensus from the three authoring institutions—spanning management science, tissue banking administration, and cardiac surgical practice—contextualises the evidence for neonatal and paediatric surgical audiences.
Results: AI applications delivering demonstrated value encompass: automated donor eligibility screening via natural language processing of electronic health records (reducing review time by 60–80%); machine learning risk scores predicting tissue structural durability from donor and processing characteristics; intelligent allocation algorithms optimising homograft distribution across competing clinical priorities; real-time process monitoring for cryopreservation quality assurance; demand forecasting reducing expiration waste; and deep learning echocardiographic surveillance automating post-implantation outcome tracking. Emerging applications include three-dimensional cardiac simulation for preoperative sizing, intraoperative decision support via transesophageal echocardiography analysis, and federated learning consortia enabling multi-institutional predictive model development without centralising sensitive data.
Conclusions: AI offers transformative potential to improve safety, equity, and efficiency in cardiac homograft banking. Realising this potential requires deliberate attention to training data diversity, regulatory compliance, interpretability, and infrastructure-appropriate deployment strategies. Paediatric cardiac surgery programmes—including those operating under resource constraints—stand to benefit substantially from thoughtful AI adoption framed around genuine clinical needs rather than technological novelty.
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