Cited time in webofscience Cited time in scopus

Deep-learning model associating lateral cervical radiographic features with Cormack-Lehane grade 3 or 4 glottic view

Title
Deep-learning model associating lateral cervical radiographic features with Cormack-Lehane grade 3 or 4 glottic view
Author(s)
Cho, Hye-YeonLee, KyungsuKong, Hyoun-JoongYang, Hyun-LimJung, Chul-WooPark, Hee-PyoungHwang, Jae YounLee, Hyung-Chul
Issued Date
2023-01
Citation
Anaesthesia, v.78, no.1, pp.64 - 72
Type
Article
Author Keywords
airway evaluationartificial intelligencedeep-learningdifficult laryngoscopyintratrachealintubation
Keywords
DISTANCEDIFFICULT INTUBATIONPREDICTIONCLASSIFICATIONMETAANALYSIS
ISSN
0003-2409
Abstract
Unanticipated difficult laryngoscopy is associated with serious airway-related complications. We aimed to develop and test a convolutional neural network-based deep-learning model that uses lateral cervical spine radiographs to predict Cormack-Lehane grade 3 or 4 direct laryngoscopy views of the glottis. We analysed the radiographs of 5939 thyroid surgery patients at our hospital, 253 (4%) of whom had grade 3 or 4 glottic views. We used 10 randomly sampled datasets to train a model. We compared the new model with six similar models (VGG, ResNet, Xception, ResNext, DenseNet and SENet). The Brier score (95%CI) of the new model, 0.023 (0.021-0.025), was lower ('better') than the other models: VGG, 0.034 (0.034-0.035); ResNet, 0.033 (0.033-0.035); Xception, 0.032 (0.031-0.033); ResNext, 0.033 (0.032-0.033); DenseNet, 0.030 (0.029-0.032); SENet, 0.031 (0.029-0.032), all p < 0.001. We calculated mean (95%CI) of the new model for: R-2, 0.428 (0.388-0.468); mean squared error, 0.023 (0.021-0.025); mean absolute error, 0.048 (0.046-0.049); balanced accuracy, 0.713 (0.684-0.742); and area under the receiver operating characteristic curve, 0.965 (0.962-0.969). Radiographic features around the hyoid bone, pharynx and cervical spine were associated with grade 3 and 4 glottic views. © 2022 Association of Anaesthetists
URI
http://hdl.handle.net/20.500.11750/17165
DOI
10.1111/anae.15874
Publisher
Blackwell Publishing Inc.
Related Researcher
  • 황재윤 Hwang, Jae Youn 전기전자컴퓨터공학과
  • Research Interests Multimodal Imaging; High-Frequency Ultrasound Microbeam; Ultrasound Imaging and Analysis; 스마트 헬스케어; Biomedical optical system
Files in This Item:

There are no files associated with this item.

Appears in Collections:
Department of Electrical Engineering and Computer Science MBIS(Multimodal Biomedical Imaging and System) Laboratory 1. Journal Articles

qrcode

  • twitter
  • facebook
  • mendeley

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE