Advancing lung transplantation through machine learning and artificial intelligence
Lielle Ronen1,2,3, Shaf Keshavjee1,2,3,4, Andrew T Sage1,2,3,4,5
1Latner Thoracic Research Laboratories, Toronto General Hospital Research Institute, University Health Network.
Purpose Of Review:
To explore the current applications of artificial intelligence and machine learning in lung transplantation, including outcome prediction, drug dosing, and the potential future uses and risks as the technology continues to evolve.
Recent Findings:
While the use of artificial intelligence (AI) and machine learning (ML) in lung transplantation is relatively new, several groups have developed models to predict short-term outcomes, such as primary graft dysfunction and time-to-extubation, as well as long-term outcomes related to survival and chronic lung allograft dysfunction. Additionally, drug dosing models for Tacrolimus levels have been designed, demonstrating proof of concept for modelling treatment as a time-series problem.
Summary:
The integration of ML models with clinical decision-making has shown promise in improving post-transplant survival and optimizing donor lung utilization. As technology advances, the field will continue to evolve, with enhanced datasets supporting more sophisticated ML models, particularly through real-time monitoring of biological, biochemical, and physiological data.
