Automatic Recognition of Concealed Fish Bones under Laryngoscopy: A Practical AI Model Based on YOLO-V5

doi:10.1002/lary.31175

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	Automatic Recognition of Concealed Fish Bones under Laryngoscopy: A Practical AI Model Based on YOLO-V5
	Tao, Xiaoyao 1; Zhao, Xu 2; Liu, Hairui 3,4; Wang, Jinqiao 2; Tian, Chunhui 5,6; Liu, Longsheng 7,8; Ding, Yujie 9; Chen, Xue 10; Liu, Yehai 1,11
刊名	LARYNGOSCOPE
	2023-11-20
页码	8
关键词	deep learning YOLO fish bones laryngoscopy computer vision
ISSN号	0023-852X
DOI	10.1002/lary.31175
通讯作者	Liu, Yehai(liuyehai@ahmu.edu.cn)
英文摘要	Background: Fish bone impaction is one of the most common problems encountered in otolaryngology emergencies. Due to their small and transparent nature, as well as the complexity of pharyngeal anatomy, identifying fish bones efficiently under laryngoscopy requires substantial clinical experience. This study aims to create an AI model to assist clinicians in detecting pharyngeal fish bones more efficiently under laryngoscopy.Methods: Totally 3133 laryngoscopic images related to fish bones were collected for model training and validation. The images in the training dataset were trained using the YOLO-V5 algorithm model. After training, the model was validated and its performance was evaluated using a test dataset. The model's predictions were compared to those of human experts. Seven laryngoscopic videos related to fish bone were used to validate real-time target detection by the model.Results: The model trained in YOLO-V5 demonstrated good generalization and performance, with an average precision of 0.857 when the intersection over union (IOU) threshold was set to 0.5. The precision, recall rate, and F1 scores of the model are 0.909, 0.818, and 0.87, respectively. The overall accuracy of the model in the validation set was 0.821, comparable to that of ENT specialists. The model processed each image in 0.012 s, significantly faster than human processing (p < 0.001). Furthermore, the model exhibited outstanding performance in video recognition.Conclusion: Our AI model based on YOLO-V5 effectively identifies and localizes fish bone foreign bodies in static laryngoscopic images and dynamic videos. It shows great potential for clinical application.
资助项目	The authors of this article would like to express their gratitude to colleagues from the First Affiliated Hospital of Anhui Medical University for their active participation and technical support in this study.
WOS研究方向	Research & Experimental Medicine ; Otorhinolaryngology
语种	英语
出版者	WILEY
WOS记录号	WOS:001107371200001
资助机构	The authors of this article would like to express their gratitude to colleagues from the First Affiliated Hospital of Anhui Medical University for their active participation and technical support in this study.
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/55192]
专题	紫东太初大模型研究中心
通讯作者	Liu, Yehai
作者单位	1.Anhui Med Univ, Affiliated Hosp 1, Otorhinolaryngol Head & Neck Surg Dept, Hefei, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 3.Chinese Acad Sci, Beijing, Peoples R China 4.China Univ Geosci, Sch Informat Engn, Beijing, Peoples R China 5.China Univ Geosci, Beijing, Peoples R China 6.Anhui Med Univ, Suzhou Hosp, Otolaryngol Head & Neck Surg Dept, Suzhou, Peoples R China 7.Anhui Med Univ, Suzhou Hosp, Suzhou, Peoples R China 8.Anhui Med Univ, Chaohu Hosp, Otolaryngol Head & Neck Surg Dept, Hefei, Peoples R China 9.Feixi Cty Peoples Hosp, Otolaryngol Head & Neck Surg Dept, Hefei, Peoples R China 10.Feidong Cty Peoples Hosp, Otolaryngol Head & Neck Surg Dept, Hefei, Peoples R China
推荐引用方式 GB/T 7714	Tao, Xiaoyao,Zhao, Xu,Liu, Hairui,et al. Automatic Recognition of Concealed Fish Bones under Laryngoscopy: A Practical AI Model Based on YOLO-V5[J]. LARYNGOSCOPE,2023:8.
APA	Tao, Xiaoyao.,Zhao, Xu.,Liu, Hairui.,Wang, Jinqiao.,Tian, Chunhui.,...&Liu, Yehai.(2023).Automatic Recognition of Concealed Fish Bones under Laryngoscopy: A Practical AI Model Based on YOLO-V5.LARYNGOSCOPE,8.
MLA	Tao, Xiaoyao,et al."Automatic Recognition of Concealed Fish Bones under Laryngoscopy: A Practical AI Model Based on YOLO-V5".LARYNGOSCOPE (2023):8.