Cui Haipo, Wu Jinjing, Li Tianying, Zou Zui, Guo Wenhui, Liu Long, Zhang Qianwen, Huang Xiaoping
School of Health Science and Engineering, University of Shanghai for Science and Technology Shanghai 201210, China.
Department of Anesthesiology, Naval Medical University Shanghai 200433, China.
Am J Transl Res. 2025 May 15;17(5):3293-3306. doi: 10.62347/BIHI3707. eCollection 2025.
Tracheal intubation is a routine procedure in clinical surgeries and emergency situations, essential for maintaining respiration and ensuring airway patency. Due to the complexity of laryngeal structures and the need for rapid airway management in critically ill patients, real-time, accurate identification of key laryngeal structures is crucial for successful intubation. This study presents a real-time laryngeal structure recognition method based on an improved YOLOv8-seg model.
Laryngeal images from retrospective intubation procedures were used to assist clinicians in the rapid and precise identification of critical laryngeal structures, such as the epiglottis, glottis, and vocal cords. The proposed model, named SlimMSDA-YOLO, integrates a lightweight neck structure, Slimneck, into the original YOLOv8n-seg model by combining GSConv and standard convolutions. This modification effectively reduces the floating-point operations and computational resource requirements. Additionally, a multi-scale dilation attention module was incorporated between the neck and head sections to enhance the network's ability to capture features across various receptive fields, thereby improving its focus on critical regions.
The SlimMSDA-YOLO model achieved a precision of 90.4%, recall of 84.2%, and mAP50 of 90.1%. The model's Giga Floating Point Operations Per Second was 11.4, and the number of parameters was 3,139,819. These results demonstrate the effectiveness of the proposed method in enhancing both model efficiency and performance.
The SlimMSDA-YOLO model is lightweight and efficient, making it ideal for real-time laryngeal structure recognition during intubation. Comparative experiments with other lightweight segmentation networks highlight the effectiveness and superiority of the proposed approach.
气管插管是临床手术和紧急情况下的常规操作，对于维持呼吸和确保气道通畅至关重要。由于喉部结构复杂，且重症患者需要快速进行气道管理，因此实时、准确地识别关键喉部结构对于成功插管至关重要。本研究提出了一种基于改进的YOLOv8-seg模型的实时喉部结构识别方法。
回顾性插管手术中的喉部图像用于辅助临床医生快速、精确地识别关键喉部结构，如会厌、声门和声带。所提出的模型名为SlimMSDA-YOLO，通过结合GSConv和标准卷积，将轻量级颈部结构Slimneck集成到原始的YOLOv8n-seg模型中。这种修改有效地减少了浮点运算和计算资源需求。此外，在颈部和头部之间引入了多尺度扩张注意力模块，以增强网络跨不同感受野捕捉特征的能力，从而提高其对关键区域的关注。
SlimMSDA-YOLO模型的精度为90.4%，召回率为84.2%，mAP50为90.1%。该模型的每秒千兆浮点运算次数为11.4，参数数量为3,139,819。这些结果证明了所提方法在提高模型效率和性能方面的有效性。
SlimMSDA-YOLO模型轻量级且高效，非常适合插管过程中实时喉部结构识别。与其他轻量级分割网络的对比实验突出了所提方法的有效性和优越性。
