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Deep Learning Model for Predicting Difficult Laryngoscopy in Thyroid Surgery Patients Based on a Cervical Spine Lateral X-Ray Image : 경추 측면 X선 영상을 기반으로 한 갑상선 수술 환자에서 어려운 후두경 예측 딥러닝 모델
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 정철우 | - |
dc.contributor.author | 조혜연 | - |
dc.date.accessioned | 2022-12-29T08:54:05Z | - |
dc.date.available | 2022-12-29T08:54:05Z | - |
dc.date.issued | 2022 | - |
dc.identifier.other | 000000171756 | - |
dc.identifier.uri | https://hdl.handle.net/10371/188349 | - |
dc.identifier.uri | https://dcollection.snu.ac.kr/common/orgView/000000171756 | ko_KR |
dc.description | 학위논문(석사) -- 서울대학교대학원 : 의과대학 의학과, 2022. 8. 정철우. | - |
dc.description.abstract | 예상하지 못한 어려운 후두경은 심각한 기도관련 합병증과 연관되어 있다. 본 연구는 후향적으로 수집된 갑상선 수술을 받은 총 14,135명 환자의 경추 측면 X선을 통해 어려운 후두경 (Cormack-Lehane 등급 3-4)를 예측하는 딥러닝 모델을 개발 및 검증하였다. 개발 모델의 성능은 기존의 6개의 딥러닝 모델과 비교하였다. 개발 모델에서 어려운 후두경 예측의 민감도는 95.6%, 특이도 91.2%를 나타냈다. Area Under ROC curve의 경우 개발 모델에서 0.972(0.955~0.988), 기존 모델의 경우 각각 VGG-Net: 0.842, ResNet: 0.841, Xception: 0.863, ResNext: 0.825, DenseNet: 0.889, SENet: 0.875를 나타냈다. 어려운 후두경과 관련된 해부학적 특징을 설명하기 위해 클래스 활성화 맵(Class Activation Map)을 사용하였다. 클래스 활성화 맵에서 설골, 인두 및 경추 주변이 강조되었다. 본 연구를 통해 개발된 딥러닝 모델은 경추 측면 X선 영상을 이용한 어려운 후두경 예측에 높은 성능을 보였다. | - |
dc.description.abstract | An unanticipated difficult laryngoscopy is associated with serious airway-related complications. We here developed and validated a deep learning-based model that predicts a difficult laryngoscopy (Cormack–Lehane grade 3–4) from a cervical spine lateral X-ray using data from 14,135 patients undergoing thyroid surgery. The performance of our model was compared with six representative deep learning architectures. A class activation map was created to elucidate the anatomical features associated with difficult laryngoscopy. Our model showed 95.6% sensitivity and 91.2% specificity for predicting difficult laryngoscopy. The area under the receiver operating characteristic curve of our model was 0.972 (0.955‒0.988), which was higher than that of other models (VGG-Net: 0.842, ResNet: 0.841, Xception: 0.863, ResNext: 0.825, DenseNet: 0.889, and SENet: 0.875, all P < 0.001). The class activation map demonstrated clear differences around the hyoid bone, pharynx, and cervical spine. The model showed excellent performance for predicting difficult laryngoscopy using a cervical spine lateral X-ray image. | - |
dc.description.tableofcontents | 1. Introduction 1
2. Materials and Methods 2 2.1 Inclusion and Exclusion Criteria 2 2.2 Anesthesia Management 2 2.3 Data Collection and Preprocessing 2 2.4 Model Building 3 2.5 Model Validation 4 2.6 Sensitivity Analysis 4 2.7 Statistical Analysis 4 3. Results 6 3.1 Dataset Construction 6 3.2 Performance of the Models 6 3.3 Sensitivity Analysis 6 4. Discussion 8 5. Conclusions 11 References 23 Abstract 26 Tables 12 [Table 1] 12 [Table 2] 13 [Table 3] 14 Figures 15 [Figure 1] 15 [Figure 2] 16 [Figure 3] 17 [Figure 4] 18 [Figure 5] 19 Supplementary Materials 20 [Supplementary Materials] 20 | - |
dc.format.extent | iii, 32 | - |
dc.language.iso | eng | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | 기관내삽관 | - |
dc.subject | 기도평가 | - |
dc.subject | 딥러닝 | - |
dc.subject | 어려운후두경 | - |
dc.subject | 인공지능 | - |
dc.subject.ddc | 610 | - |
dc.title | Deep Learning Model for Predicting Difficult Laryngoscopy in Thyroid Surgery Patients Based on a Cervical Spine Lateral X-Ray Image | - |
dc.title.alternative | 경추 측면 X선 영상을 기반으로 한 갑상선 수술 환자에서 어려운 후두경 예측 딥러닝 모델 | - |
dc.type | Thesis | - |
dc.type | Dissertation | - |
dc.contributor.AlternativeAuthor | Hye-Yeon Cho, Kyungsu Lee, Hyoun-Joong Kong, Hyun-Lim Yang, Chul-Woo Jung, Hee-Pyoung Park, Jae Youn Hwang, Hyung-Chul Lee | - |
dc.contributor.department | 의과대학 의학과 | - |
dc.description.degree | 석사 | - |
dc.date.awarded | 2022-08 | - |
dc.contributor.major | 마취통증의학과 | - |
dc.identifier.uci | I804:11032-000000171756 | - |
dc.identifier.holdings | 000000000048▲000000000055▲000000171756▲ | - |
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