AI to enhance interactive simulation-based training in resuscitation medicine

When patients become acutely unwell, the ability of frontline healthcare professionals to act quickly and effectively can mean the difference between life and death. High-fidelity simulation is the gold standard by which medics acquire and maintain key resuscitation skills, but the resourceintensive nature of current, face-to-face training limits access to training and allows “skills fade” to creep in. We propose that human computer interaction-based simulations augmented by artificial intelligence could provide a cost-effective alternative to traditional training and allow clinicians much greater access to training. This paper is mostly an in-depth discussion; however, we also present a 3D simulator for resuscitation skills training which we developed using the Unity games physics engine.

[1]  D. Kolb Experiential Learning: Experience as the Source of Learning and Development , 1983 .

[2]  M. Hewson,et al.  Giving feedback in medical education: verification of recommended techniques. , 1998, Journal of general internal medicine.

[3]  R. Satava,et al.  Virtual Reality Training Improves Operating Room Performance: Results of a Randomized, Double-Blinded Study , 2002, Annals of surgery.

[4]  Rinaldo Bellomo,et al.  Prospective controlled trial of effect of medical emergency team on postoperative morbidity and mortality rates* , 2004, Critical care medicine.

[5]  R. Satava,et al.  Virtual Reality Simulation for the Operating Room: Proficiency-Based Training as a Paradigm Shift in Surgical Skills Training , 2005, Annals of surgery.

[6]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[7]  Jane Fox Safer care for acutely ill patients. , 2007, British journal of nursing.

[8]  G. B. Young,et al.  Clinical practice. Neurologic prognosis after cardiac arrest. , 2009, The New England journal of medicine.

[9]  Antonio Torralba,et al.  Evaluation of image features using a photorealistic virtual world , 2011, 2011 International Conference on Computer Vision.

[10]  Bernt Schiele,et al.  Learning people detection models from few training samples , 2011, CVPR 2011.

[11]  Michael Schneider,et al.  Relations among conceptual knowledge, procedural knowledge, and procedural flexibility in two samples differing in prior knowledge. , 2011, Developmental psychology.

[12]  Vinay Nadkarni,et al.  Low-Dose, High-Frequency CPR Training Improves Skill Retention of In-Hospital Pediatric Providers , 2011, Pediatrics.

[13]  Ronald M. Summers,et al.  Machine learning and radiology , 2012, Medical Image Anal..

[14]  Charles Vincent,et al.  Preventable deaths due to problems in care in English acute hospitals: a retrospective case record review study , 2012, BMJ quality & safety.

[15]  Mario Fritz,et al.  Recognizing Materials from Virtual Examples , 2012, ECCV.

[16]  Nigel Stallard,et al.  Improving the Efficiency of Advanced Life Support Training , 2012, Annals of Internal Medicine.

[17]  Alex Graves,et al.  Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.

[18]  David A Harrison,et al.  Incidence and outcome of in-hospital cardiac arrest in the United Kingdom National Cardiac Arrest Audit. , 2014, Resuscitation.

[19]  Kazuo Okada,et al.  Part 4: Advanced life support: 2015 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science with Treatment Recommendations. , 2015, Resuscitation.

[20]  Robert A. Berg,et al.  International Consensus on Cardiopulmonary Resuscitation and mergency Cardiovascular Care Science with Treatment ecommendations , 2015 .

[21]  Alex Graves,et al.  Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.

[22]  Fábio Ferreira Amorim,et al.  Automated early warning system for septic shock: the new way to achieve intensive care unit quality improvement? , 2017, Annals of translational medicine.

[23]  Part 4 : Advanced life support International Liaison Committee on Resuscitation , 2022 .