Abrar Majeedi, Patrick J Peebles, Yin Li, Ryan M McAdams
{"title":"利用深度学习为新生儿视频喉镜插管进行声门开放检测。","authors":"Abrar Majeedi, Patrick J Peebles, Yin Li, Ryan M McAdams","doi":"10.1038/s41372-024-02171-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to develop an artificial intelligence (AI) method to augment video laryngoscopy (VL) by automating the detection of the glottic opening in neonates, as a step toward future studies on improving intubation outcomes.</p><p><strong>Study design: </strong>A deep learning model, YOLOv8, was trained on 1623 video frames from 84 neonatal intubations to detect the glottic opening and evaluated using 14-fold cross-validation on metrics like precision and recall. Additionally, it was compared with 25 medical providers of varied intubation experience to assess its relative performance.</p><p><strong>Results: </strong>The model demonstrated a precision of 80.8% and a recall of 75.3% in identifying the glottic opening, detecting it 0.31 s faster than the average medical provider. It performed comparably or better than novice and intermediate providers, and slightly slower than experts.</p><p><strong>Conclusion: </strong>AI-powered tools can aid VL by providing real-time guidance, potentially enhancing neonatal intubation safety and efficiency for less experienced users.</p>","PeriodicalId":16690,"journal":{"name":"Journal of Perinatology","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Glottic opening detection using deep learning for neonatal intubation with video laryngoscopy.\",\"authors\":\"Abrar Majeedi, Patrick J Peebles, Yin Li, Ryan M McAdams\",\"doi\":\"10.1038/s41372-024-02171-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>This study aimed to develop an artificial intelligence (AI) method to augment video laryngoscopy (VL) by automating the detection of the glottic opening in neonates, as a step toward future studies on improving intubation outcomes.</p><p><strong>Study design: </strong>A deep learning model, YOLOv8, was trained on 1623 video frames from 84 neonatal intubations to detect the glottic opening and evaluated using 14-fold cross-validation on metrics like precision and recall. Additionally, it was compared with 25 medical providers of varied intubation experience to assess its relative performance.</p><p><strong>Results: </strong>The model demonstrated a precision of 80.8% and a recall of 75.3% in identifying the glottic opening, detecting it 0.31 s faster than the average medical provider. It performed comparably or better than novice and intermediate providers, and slightly slower than experts.</p><p><strong>Conclusion: </strong>AI-powered tools can aid VL by providing real-time guidance, potentially enhancing neonatal intubation safety and efficiency for less experienced users.</p>\",\"PeriodicalId\":16690,\"journal\":{\"name\":\"Journal of Perinatology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Perinatology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1038/s41372-024-02171-3\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"OBSTETRICS & GYNECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Perinatology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1038/s41372-024-02171-3","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
Glottic opening detection using deep learning for neonatal intubation with video laryngoscopy.
Objective: This study aimed to develop an artificial intelligence (AI) method to augment video laryngoscopy (VL) by automating the detection of the glottic opening in neonates, as a step toward future studies on improving intubation outcomes.
Study design: A deep learning model, YOLOv8, was trained on 1623 video frames from 84 neonatal intubations to detect the glottic opening and evaluated using 14-fold cross-validation on metrics like precision and recall. Additionally, it was compared with 25 medical providers of varied intubation experience to assess its relative performance.
Results: The model demonstrated a precision of 80.8% and a recall of 75.3% in identifying the glottic opening, detecting it 0.31 s faster than the average medical provider. It performed comparably or better than novice and intermediate providers, and slightly slower than experts.
Conclusion: AI-powered tools can aid VL by providing real-time guidance, potentially enhancing neonatal intubation safety and efficiency for less experienced users.
期刊介绍:
The Journal of Perinatology provides members of the perinatal/neonatal healthcare team with original information pertinent to improving maternal/fetal and neonatal care. We publish peer-reviewed clinical research articles, state-of-the art reviews, comments, quality improvement reports, and letters to the editor. Articles published in the Journal of Perinatology embrace the full scope of the specialty, including clinical, professional, political, administrative and educational aspects. The Journal also explores legal and ethical issues, neonatal technology and product development.
The Journal’s audience includes all those that participate in perinatal/neonatal care, including, but not limited to neonatologists, perinatologists, perinatal epidemiologists, pediatricians and pediatric subspecialists, surgeons, neonatal and perinatal nurses, respiratory therapists, pharmacists, social workers, dieticians, speech and hearing experts, other allied health professionals, as well as subspecialists who participate in patient care including radiologists, laboratory medicine and pathologists.