Computer methods and programs in biomedicine · 2026
Non-invasive classification of coronary perfusion pressure during CPR using smartphone-based skin video and deep learning.
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Kwon Soyoon, Shin Dong Ah, Kim Taegyun, Kim Kyung Su, Suh Gil Joon, Sim Jaehoon, Hur Sungmoon, Park Jaeheung, Lee Jung Chan
Аннотация
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.
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.