Mask R-CNN based multiclass segmentation model for endotracheal intubation using video laryngoscope Article Swipe
Seung Jae Choi
,
Dae Kon Kim
,
Byeong Soo Kim
,
Minwoo Cho
,
Joo Seong Jeong
,
You Hwan Jo
,
Kyoung Jun Song
,
Yu Jin Kim
,
Sungwan Kim
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1177/20552076231211547
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1177/20552076231211547
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/20552076231211547
- OA Status
- gold
- Cited By
- 12
- References
- 28
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388468259Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1177/20552076231211547Digital Object Identifier
- Title
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Mask R-CNN based multiclass segmentation model for endotracheal intubation using video laryngoscopeWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Seung Jae Choi, Dae Kon Kim, Byeong Soo Kim, Minwoo Cho, Joo Seong Jeong, You Hwan Jo, Kyoung Jun Song, Yu Jin Kim, Sungwan KimList of authors in order
- Landing page
-
https://doi.org/10.1177/20552076231211547Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1177/20552076231211547Direct OA link when available
- Concepts
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Endotracheal intubation, Intubation, Video laryngoscope, Medicine, Computer science, Endotracheal tube, Segmentation, Artificial intelligence, Anesthesia, Tracheal intubationTop concepts (fields/topics) attached by OpenAlex
- Cited by
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12Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 5Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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