Kao Hsiang, Wiryasaputra Rita, Liao Yo-Yun, Tsan Yu-Tse, Chu Wei-Min, Chen Yi-Hsuan, Lin Tzu-Chieh, Yang Chao-Tung
Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung 407219, Taiwan.
Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 407224, Taiwan.
Diagnostics (Basel). 2024 Dec 25;15(1):17. doi: 10.3390/diagnostics15010017.
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.
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.
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.
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.
心血管疾病（CVD）是慢性非传染性疾病（NCDs）之一，被定义为一种心脏和血管疾病，包括冠心病、心力衰竭、外周动脉疾病、脑血管疾病（中风）、先天性心脏病、风湿性心脏病和高血压。患有心血管疾病会增加死亡率。情绪压力是与心血管疾病相关的一个间接指标，通常可通过面部表情表现出来。胸痛或胸部不适是心脏病发作的症状之一。胸痛的黄金救治时间会影响脑细胞死亡的发生；因此，在观察期间救治胸部不适的患者是一个至关重要且紧迫的问题。此外，数量有限的急诊室（ER）医护人员为非预约门诊患者服务。在本研究中，开发了一种基于计算机的自动胸痛检测辅助系统，利用面部表情来改善患者护理服务并将心脏损伤降至最低。
作为一种深度学习方法，You Only Look Once（YOLO）模型可同时检测和识别物体的位置。一系列YOLO模型被用于通过面部表情进行疼痛检测。
在对胸痛患者的面部表情检测中，YOLOv4和YOLOv6的表现优于YOLOv7。YOLOv4和YOLOv6的准确率达到了80 - 100%。尽管在获得准确率值方面存在相似之处，但YOLOv6的训练时间比YOLOv4更快。
通过执行此任务，医生可以优先制定最佳治疗方案，减少患者心脏损伤的程度，并提高黄金治疗时间的有效性。
