Development of artificial intelligence-driven biosignal-sensitive cardiopulmonary resuscitation robot.

ABSTRACT

AIM OF THE STUDY We evaluated whether an artificial intelligence (AI)-driven robot cardiopulmonary resuscitation (CPR) could improve hemodynamic parameters and clinical outcomes. METHODS: We developed an AI-driven CPR robot which utilizes an integrated feedback system with an AI model predicting carotid blood flow (CBF). Twelve pigs were assigned to the AI robot group (n = 6) and the LUCAS 3 group (n = 6). They underwent 6 min of CPR after 7 min of ventricular fibrillation. In the AI robot group, the robot explored for the optimal compression position, depth and rate during the first 270-second period, and continued CPR with the optimal setup during the next 90-second period and beyond. The primary outcome was CBF during the last 90-second period. The secondary outcomes were coronary perfusion pressure (CPP), end-tidal carbon dioxide level (ETCO2) and return of spontaneous circulation (ROSC). RESULTS: The AI model's prediction performance was excellent (Pearson correlation coefficient = 0.98). CBF did not differ between the two groups [estimate and standard error (SE), -23.210 ± 20.193, P = 0.250]. CPP, ETCO2 level and rate of ROSC also did not show difference [estimate and SE, -0.214 ± 7.245, P = 0.976 for CPP; estimate and SE, 1.745 ± 3.199, P = 0.585 for ETCO2; 5/6 (83.3%) vs. 4/6 (66.7%), P = 1.000 for ROSC). CONCLUSION: This study provides proof of concept that an AI-driven CPR robot in porcine cardiac arrest is feasible. Compared to a LUCAS 3, an AI-driven CPR robot produced comparable hemodynamic and clinical outcomes.