Non-invasive classification of coronary perfusion pressure during CPR using smartphone-based skin video and deep learning.

BACKGROUND AND OBJECTIVE: Coronary perfusion pressure (CPP) is an important determinant of myocardial blood flow and an indicator during cardiopulmonary resuscitation (CPR). However, conventional CPP monitoring methods are invasive and unsuitable for out-of-hospital settings. This study proposes a non-invasive approach to classify CPP levels using skin video recorded with a smartphone camera and deep learning. METHODS: Video and biosignal data were collected simultaneously from 15 pigs during CPR. An integrated deep learning model that combines backbone model (CNN, EfficientNetV2-B0, ConvNeXt-Nano, FastViT-T8) with gated recurrent unit (GRU) was developed to classify whether CPP exceeded the clinically relevant threshold of 20 mmHg. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to identify regions attended to by the model, and statistical analysis conducted. RESULTS: Among four different backbone models used for training and evaluation, the EfficientNetV2-B0-GRU architecture demonstrated the best performance, achieving accuracy of 84.60 % and F1-score of 76.04 %. Statistical analysis revealed significant differences in YCrCb channel values between correctly classified groups. In addition, active regions of Grad-CAM showed greater variances than inactive regions, indicating that the model focused on regions with higher color variabilities related to perfusion. CONCLUSION: A deep learning-based approach using skin video enables the non-invasive classification of CPP during CPR. Grad-CAM and quantitative YCrCb analyses improve interpretability, while the proposed method provides proof-of-concept for cost-effective and accessible perfusion-related assessment using skin video recorded with a smartphone, with potential future utility for real-time CPR quality assessment and decision-making in emergency or out-of-hospital settings.