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The Potential for High-Priority Care Based on Pain Through Facial Expression Detection with Patients Experiencing
Hsiang Kao1, Rita Wiryasaputra2,3, Yo-Yun Liao4
1Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung 407219, Taiwan.
Insights
A new AI system uses facial expressions to detect chest pain, improving emergency cardiac care. YOLOv4 and YOLOv6 models show high accuracy, aiding faster diagnosis and reducing heart damage.
Area of Science:
- Artificial Intelligence
- Medical Technology
- Cardiology
Background:
- Cardiovascular disease (CVD) is a leading cause of mortality, encompassing various heart and vascular disorders.
- Facial expressions can indicate emotional stress, an indirect but significant indicator of CVD.
- Prompt recognition of chest pain is critical to prevent brain cell death during the 'golden hour'.
Purpose of the Study:
- To develop an AI-assisted system for automatic chest pain detection using facial expressions.
- To enhance emergency care services for cardiovascular disease patients.
- To minimize cardiac damage by enabling rapid intervention.
Main Methods:
- Utilized deep learning, specifically the You Only Look Once (YOLO) models, for object detection and recognition.
- Applied a series of YOLO models to detect pain through facial expressions in patients.
- Evaluated YOLOv4, YOLOv6, and YOLOv7 for their efficacy in this task.
Main Results:
- YOLOv4 and YOLOv6 demonstrated superior performance in detecting chest pain via facial expressions compared to YOLOv7.
- Achieved high accuracy rates of 80-100% for YOLOv4 and YOLOv6.
- YOLOv6 exhibited a faster training time than YOLOv4 while maintaining comparable accuracy.
Conclusions:
- Physicians can leverage this AI system to prioritize treatment plans effectively.
- The system aids in reducing the extent of cardiac damage in patients experiencing chest pain.
- Improved patient care and outcomes are expected by optimizing the critical 'golden hour' treatment window.
Background And Objective:
Cardiovascular disease (CVD), one of the chronic non-communicable diseases (NCDs), is defined as a cardiac and vascular disorder that includes coronary heart disease, heart failure, peripheral arterial disease, cerebrovascular disease (stroke), congenital heart disease, rheumatic heart disease, and elevated blood pressure (hypertension). Having CVD increases the mortality rate. Emotional stress, an indirect indicator associated with CVD, can often manifest through facial expressions. Chest pain or chest discomfort is one of the symptoms of a heart attack. The golden hour of chest pain influences the occurrence of brain cell death; thus, saving people with chest discomfort during observation is a crucial and urgent issue. Moreover, a limited number of emergency care (ER) medical personnel serve unscheduled outpatients. In this study, a computer-based automatic chest pain detection assistance system is developed using facial expressions to improve patient care services and minimize heart damage.
Methods:
The You Only Look Once (YOLO) model, as a deep learning method, detects and recognizes the position of an object simultaneously. A series of YOLO models were employed for pain detection through facial expression.
Results:
The YOLOv4 and YOLOv6 performed better than YOLOv7 in facial expression detection with patients experiencing chest pain. The accuracy of YOLOv4 and YOLOv6 achieved 80-100%. Even though there are similarities in attaining the accuracy values, the training time for YOLOv6 is faster than YOLOv4.
Conclusion:
By performing this task, a physician can prioritize the best treatment plan, reduce the extent of cardiac damage in patients, and improve the effectiveness of the golden treatment time.
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