Machine learning: principles and applications for thoracic surgery

OBJECTIVES: Machine learning (ML) has experienced a revolutionary decade with advances across many disciplines. We seek to understand how recent advances in ML are going to specifically influence the practice of surgery in the future with a particular focus on thoracic surgery. METHODS: Review of relevant literature in both technical and clinical domains. RESULTS: ML is a revolutionary technology that promises to change the way that surgery is practiced in the near future. Spurred by an advance in computing power and the volume of data produced in healthcare, ML has shown remarkable ability to master tasks that had once been reserved for physicians. Supervised learning, unsupervised learning and reinforcement learning are all important techniques that can be leveraged to improve care. Five key applications of ML to cardiac surgery include diagnostics, surgical skill assessment, postoperative prognostication, augmenting intraoperative performance and accelerating translational research. Some key limitations of ML include lack of interpretability, low quality and volumes of relevant clinical data, ethical limitations and difficulties with clinical implementation. CONCLUSIONS: In the future, the practice of cardiac surgery will be greatly augmented by ML technologies, ultimately leading to improved surgical performance and better patient outcomes.

Automated summary

Machine learning has experienced significant advancements in the past decade and is poised to have a major impact on the future of surgery, particularly in thoracic surgery. Techniques such as supervised learning, unsupervised learning, and reinforcement learning can improve care, but there are also limitations such as lack of interpretability and difficulties with clinical implementation. Overall, ML technologies hold great promise to enhance cardiac surgical practices.