دورية أكاديمية
Mask R-CNN based multiclass segmentation model for endotracheal intubation using video laryngoscope
| العنوان: | Mask R-CNN based multiclass segmentation model for endotracheal intubation using video laryngoscope |
|---|---|
| المؤلفون: | Seung Jae Choi, Dae Kon Kim, Byeong Soo Kim, Minwoo Cho, Joo Jeong, You Hwan Jo, Kyoung Jun Song, Yu Jin Kim, Sungwan Kim |
| المصدر: | Digital Health, Vol 9 (2023) |
| بيانات النشر: | SAGE Publishing, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics |
| مصطلحات موضوعية: | Computer applications to medicine. Medical informatics, R858-859.7 |
| الوصف: | Objective Endotracheal intubation (ETI) is critical to secure the airway in emergent situations. Although artificial intelligence algorithms are frequently used to analyze medical images, their application to evaluating intraoral structures based on images captured during emergent ETI remains limited. The aim of this study is to develop an artificial intelligence model for segmenting structures in the oral cavity using video laryngoscope (VL) images. Methods From 54 VL videos, clinicians manually labeled images that include motion blur, foggy vision, blood, mucus, and vomitus. Anatomical structures of interest included the tongue, epiglottis, vocal cord, and corniculate cartilage. EfficientNet-B5 with DeepLabv3+, EffecientNet-B5 with U-Net, and Configured Mask R-Convolution Neural Network (CNN) were used; EffecientNet-B5 was pretrained on ImageNet. Dice similarity coefficient (DSC) was used to measure the segmentation performance of the model. Accuracy, recall, specificity, and F1 score were used to evaluate the model's performance in targeting the structure from the value of the intersection over union between the ground truth and prediction mask. Results The DSC of tongue, epiglottis, vocal cord, and corniculate cartilage obtained from the EfficientNet-B5 with DeepLabv3+, EfficientNet-B5 with U-Net, and Configured Mask R-CNN model were 0.3351/0.7675/0.766/0.6539, 0.0/0.7581/0.7395/0.6906, and 0.1167/0.7677/0.7207/0.57, respectively. Furthermore, the processing speeds (frames per second) of the three models stood at 3, 24, and 32, respectively. Conclusions The algorithm developed in this study can assist medical providers performing ETI in emergent situations. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2055-2076 20552076 |
| Relation: | https://doaj.org/toc/2055-2076 |
| DOI: | 10.1177/20552076231211547 |
| URL الوصول: | https://doaj.org/article/a60f8bc587294176a6dec9aa156ae5ae |
| رقم الأكسشن: | edsdoj.60f8bc587294176a6dec9aa156ae5ae |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20552076 |
|---|---|
| DOI: | 10.1177/20552076231211547 |