Autonomous Cricothyroid Membrane Detection Using Neural Networks for First-Aid Surgical Airway Management

2020 ◽  
Author(s):  
Xiaoxue Han ◽  
Hailin Ren ◽  
Pinhas Ben-Tzvi
Keyword(s):  
Airway Management ◽  
Spatial Information ◽  
Region Of Interest ◽  
First Aid ◽  
Cricothyroid Membrane ◽  
Global Features ◽  
Severe Injuries ◽  
Anatomy And Physiology ◽  
Surgical Airway ◽  
Improved Performance

Abstract Airway management is one of the most important priorities when dealing with patients with severe injuries, but knowledge of the important anatomy and physiology is needed for providers to perform a successful surgery. This paper provides a solution for the precise cricothyroid membrane detection problem for real-time surgical airway management applications. With a commercial compact and portable cricothyrotomy kit, the proposed method will enable providers with general knowledge to perform successful first-aid airway management. In this paper, we propose a Hybrid Neural Network (HNNet), consisting of two parallel computing ensembles. The first ensemble takes as an input a low-resolution global image and outputs the Region-of-Interest (ROI) from the predefined grids. The high-resolution image is then cropped according to the ROI, and fed into the second ensemble to achieve precise keypoint detection. Global features and their spatial information from the first ensemble are also fed into the second ensemble to improve the precision. A dataset that consists of over 16,000 images from 13 subjects is built, and the location of the cricothyroid membrane in each image is precisely labeled by medical experts. The training results are presented to show both the efficiency and improved performance of our proposed method compared to existing ones.

Emergency Surgical Airway Algorithm for Difficult Airway Management During the COVID-19 Pandemic

2021 ◽  
pp. 019459982098656
Author(s):  
Soham Roy ◽  
John D. Cramer ◽  
Carol Bier-Laning ◽  
Patrick A. Palmieri ◽  
Christopher H. Rassekh ◽  
...  
Keyword(s):  
Airway Management ◽  
Difficult Airway ◽  
Difficult Airway Management ◽  
Surgical Airway

scholarly journals Current Trends in Surgical Airway Management of Neonates with Robin Sequence

2018 ◽  
Vol 6 (11) ◽  
pp. e1973 ◽  
Author(s):  
Kenneth L. Fan ◽  
Max Mandelbaum ◽  
Justin Buro ◽  
Alex Rokni ◽  
Gary F. Rogers ◽  
...  
Keyword(s):  
Airway Management ◽  
Current Trends ◽  
Robin Sequence ◽  
Surgical Airway

scholarly journals Unique spatial integration in mouse primary visual cortex and higher visual areas

2019 ◽  
Author(s):  
Kevin A. Murgas ◽  
Ashley M. Wilson ◽  
Valerie Michael ◽  
Lindsey L. Glickfeld
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Primary Visual Cortex ◽  
Spatial Information ◽  
Spatial Scales ◽  
Spatial Integration ◽  
Global Features ◽  
Wide Range ◽  
Visual Areas ◽  
Higher Visual Areas

AbstractNeurons in the visual system integrate over a wide range of spatial scales. This diversity is thought to enable both local and global computations. To understand how spatial information is encoded across the mouse visual system, we use two-photon imaging to measure receptive fields in primary visual cortex (V1) and three downstream higher visual areas (HVAs): LM (lateromedial), AL (anterolateral) and PM (posteromedial). We find significantly larger receptive field sizes and less surround suppression in PM than in V1 or the other HVAs. Unlike other visual features studied in this system, specialization of spatial integration in PM cannot be explained by specific projections from V1 to the HVAs. Instead, our data suggests that distinct connectivity within PM may support the area’s unique ability to encode global features of the visual scene, whereas V1, LM and AL may be more specialized for processing local features.

scholarly journals 3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention

2019 ◽  
Author(s):  
Zhizhong Han ◽  
Xiyang Wang ◽  
Chi Man Vong ◽  
Yu-Shen Liu ◽  
Matthias Zwicker ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Large Scale ◽  
Spatial Information ◽  
Dimensional Space ◽  
Spatial Relationship ◽  
Global Features ◽  
Pattern Correlation ◽  
3D Shape Analysis ◽  
3D Shapes ◽  
Learned Features

Learning global features by aggregating information over multiple views has been shown to be effective for 3D shape analysis. For view aggregation in deep learning models, pooling has been applied extensively. However, pooling leads to a loss of the content within views, and the spatial relationship among views, which limits the discriminability of learned features. We propose 3DViewGraph to resolve this issue, which learns 3D global features by more effectively aggregating unordered views with attention. Specifically, unordered views taken around a shape are regarded as view nodes on a view graph. 3DViewGraph first learns a novel latent semantic mapping to project low-level view features into meaningful latent semantic embeddings in a lower dimensional space, which is spanned by latent semantic patterns. Then, the content and spatial information of each pair of view nodes are encoded by a novel spatial pattern correlation, where the correlation is computed among latent semantic patterns. Finally, all spatial pattern correlations are integrated with attention weights learned by a novel attention mechanism. This further increases the discriminability of learned features by highlighting the unordered view nodes with distinctive characteristics and depressing the ones with appearance ambiguity. We show that 3DViewGraph outperforms state-of-the-art methods under three large-scale benchmarks.

scholarly journals Robust Traffic Light and Arrow Detection Using Digital Map with Spatial Prior Information for Automated Driving

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1181
Author(s):  
Keisuke Yoneda ◽  
Akisuke Kuramoto ◽  
Naoki Suganuma ◽  
Toru Asaka ◽  
Mohammad Aldibaja ◽  
...  
Keyword(s):  
Urban Areas ◽  
Spatial Information ◽  
Recognition Performance ◽  
Computational Cost ◽  
Region Of Interest ◽  
Traffic Light ◽  
Automated Driving ◽  
Digital Map ◽  
Traffic Lights

Traffic light recognition is an indispensable elemental technology for automated driving in urban areas. In this study, we propose an algorithm that recognizes traffic lights and arrow lights by image processing using the digital map and precise vehicle pose which is estimated by a localization module. The use of a digital map allows the determination of a region-of-interest in an image to reduce the computational cost and false detection. In addition, this study develops an algorithm to recognize arrow lights using relative positions of traffic lights, and the arrow light is used as prior spatial information. This allows for the recognition of distant arrow lights that are difficult for humans to see clearly. Experiments were conducted to evaluate the recognition performance of the proposed method and to verify if it matches the performance required for automated driving. Quantitative evaluations indicate that the proposed method achieved 91.8% and 56.7% of the average f-value for traffic lights and arrow lights, respectively. It was confirmed that the arrow-light detection could recognize small arrow objects even if their size was smaller than 10 pixels. The verification experiments indicate that the performance of the proposed method meets the necessary requirements for smooth acceleration or deceleration at intersections in automated driving.

scholarly journals Repeated Anesthetic Management for a Patient With Klippel-Feil Syndrome

2014 ◽  
Vol 61 (3) ◽  
pp. 103-106 ◽  
Author(s):  
Yuri Hase ◽  
Nobuhito Kamekura ◽  
Toshiaki Fujisawa ◽  
Kazuaki Fukushima
Keyword(s):  
Airway Management ◽  
Cervical Vertebrae ◽  
Anesthetic Management ◽  
Preoperative Evaluation ◽  
Fatal Outcome ◽  
Laryngeal Edema ◽  
Airway Control ◽  
First Choice ◽  
Surgical Airway ◽  
Preoperative Examination

Abstract Klippel-Feil syndrome (KFS) is a rare disease characterized by a classic triad comprising a short neck, a low posterior hairline, and restricted motion of the neck due to fused cervical vertebrae. We report repeated anesthetic management for orthognathic surgeries for a KFS patient with micrognathia. Because KFS can be associated with a number of other anomalies, we therefore performed a careful preoperative evaluation to exclude them. The patient had an extremely small mandible, significant retrognathia, and severe limitation of cervical mobility due to cervical vertebral fusion. As difficult intubation was predicted, awake nasal endotracheal intubation with a fiberoptic bronchoscope was our first choice for gaining control of the patient's airway. Moreover, the possibility of respiratory distress due to postoperative laryngeal edema was considered because of the surgeries on the mandible. In the operating room, tracheotomy equipment was always kept ready if a perioperative surgical airway control was required. Three orthognathic surgeries and their associated anesthetics were completed without a fatal outcome, although once the patient was transferred to the intensive care unit for precautionary postoperative airway management and observation. Careful preoperative examination and preparation for difficult airway management are important for KFS patients with micrognathia.

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