Su Hsing-Hao, Lu Chuan-Pin
Department of Otorhinolaryngology-Head and Neck Surgery, Kaohsiung Veterans General Hospital, Kaohsiung 81362, Taiwan.
Department of Physical Therapy, Shu-Zen Junior College of Medicine and Management, Kaohsiung 82144, Taiwan.
Sensors (Basel). 2023 Sep 5;23(18):7669. doi: 10.3390/s23187669.
Surgeons determine the treatment method for patients with epiglottis obstruction based on its severity, often by estimating the obstruction severity (using three obstruction degrees) from the examination of drug-induced sleep endoscopy images. However, the use of obstruction degrees is inadequate and fails to correspond to changes in respiratory airflow. Current artificial intelligence image technologies can effectively address this issue. To enhance the accuracy of epiglottis obstruction assessment and replace obstruction degrees with obstruction ratios, this study developed a computer vision system with a deep learning-based method for calculating epiglottis obstruction ratios. The system employs a convolutional neural network, the YOLOv4 model, for epiglottis cartilage localization, a color quantization method to transform pixels into regions, and a region puzzle algorithm to calculate the range of a patient's epiglottis airway. This information is then utilized to compute the obstruction ratio of the patient's epiglottis site. Additionally, this system integrates web-based and PC-based programming technologies to realize its functionalities. Through experimental validation, this system was found to autonomously calculate obstruction ratios with a precision of 0.1% (ranging from 0% to 100%). It presents epiglottis obstruction levels as continuous data, providing crucial diagnostic insight for surgeons to assess the severity of epiglottis obstruction in patients.
外科医生根据会厌梗阻的严重程度为患者确定治疗方法，通常是通过药物诱导睡眠内镜图像检查估计梗阻严重程度（采用三个梗阻等级）。然而，梗阻等级的使用并不充分，且与呼吸气流的变化不相符。当前的人工智能图像技术能够有效解决这一问题。为提高会厌梗阻评估的准确性，并用梗阻比率取代梗阻等级，本研究开发了一种基于深度学习方法计算会厌梗阻比率的计算机视觉系统。该系统采用卷积神经网络YOLOv4模型进行会厌软骨定位，采用颜色量化方法将像素转换为区域，并采用区域拼图算法计算患者会厌气道的范围。然后利用这些信息计算患者会厌部位的梗阻比率。此外，该系统集成了基于网络和基于PC的编程技术以实现其功能。通过实验验证，发现该系统能够自主计算梗阻比率，精度为0.1%（范围为0%至100%）。它将会厌梗阻水平呈现为连续数据，为外科医生评估患者会厌梗阻的严重程度提供了关键的诊断依据。
