Critical element prediction of tracheal intubation difficulty: Automatic Mallampati classification by jointly using handcrafted and attention-based deep features

Zhang, Fan; Xu, Yi; Zhou, Zhaoyun; Zhang, Han; Yang, Ke

doi:10.1016/j.compbiomed.2022.106182

Cited by 5 publications

(8 citation statements)

References 24 publications

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“…Furthermore, a TI time-sequence analysis model was developed for the identification of key targets in the airway by using VLS images and an automatic AI-based object detection model. 17,20 Strong Pearson correlations were noted between the 4 phase durations and TIT calculated by the AI model and those calculated based on the expert cut points (r > 0.97; P < .001); the results This model could be used to compare TIT across studies conducted using video data, possibly preventing heterogeneity associated with the inconsistencies of TIT time cut points and thus facilitating cross-study interactive comparisons. YOLO is an 1-stage lightweight, less-dependent, and highly efficient algorithm; this makes our AI model potentially usable on personal computers, allowing for immediate post-TI assessment of TIT.…”

Section: Discussionmentioning

confidence: 77%

“…15 Artificial intelligence (AI)-based approaches represent an advance in airway management. [15][16][17] However, most of these approaches have been developed for the analysis of targets, such as the vocal cord. In this study, we trained the AI object detection system YOLO (You Only Look Once) [18][19][20] to recognize key targets within the airway and to identify different cutoff points through comparison with expert-selected cut points (ground truth).…”

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confidence: 99%

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Development of an Artificial Intelligence–Based Image Recognition System for Time-Sequence Analysis of Tracheal Intubation

Wu,

Huang,

Tseng

2024

Anesthesia &Amp; Analgesia

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BACKGROUND: Total intubation time (TIT) is an objective indicator of tracheal intubation (TI) difficulties. However, large variations in TIT because of diverse initial and end targets make it difficult to compare studies. A video laryngoscope (VLS) can capture images during the TI process. By using artificial intelligence (AI) to detect airway structures, the start and end points can be freely selected, thus eliminating the inconsistencies. Further deconstructing the process and establishing time-sequence analysis may aid in gaining further understanding of the TI process. METHODS: We developed a time-sequencing system for analyzing TI performed using a #3 Macintosh VLS. This system was established and validated on 30 easy TIs performed by specialists and validated using TI videos performed by a postgraduate-year (PGY) physician. Thirty easy intubation videos were selected from a cohort approved by our institutional review board (B-ER-107-088), and 6 targets were labeled: the lip, epiglottis, laryngopharynx, glottic opening, tube tip, and a black line on the endotracheal tube. We used 887 captured images to develop an AI model trained using You Only Look Once, Version 3 (YOLOv3). Seven cut points were selected for phase division. Seven experts selected the cut points. The expert cut points were used to validate the AI-identified cut points and time-sequence data. After the removal of the tube tip and laryngopharynx images, the durations between 5 identical cut points and sequentially identified the durations of 4 intubation phases, as well as TIT. RESULTS: The average and total losses approached 0 within 150 cycles of model training for target identification. The identification rate for all cut points was 92.4% (194 of 210), which increased to 99.4% (179 of 180) after the removal of the tube tip target. The 4 phase durations and TIT calculated by the AI model and those from the expert exhibited strong Pearson correlation (phase I, r = 0.914; phase II, r = 0.868; phase III, r = 0.964; and phase IV, r = 0.949; TIT, r = 0.99; all P < .001). Similar findings were obtained for the PGY’s observations (r > 0.95; P < .01). CONCLUSIONS: YOLOv3 is a powerful tool for analyzing images recorded by VLS. By using AI to detect the airway structures, the start and end points can be freely selected, resolving the heterogeneity resulting from the inconsistencies in the TIT cut points across studies. Time-sequence analysis involving the deconstruction of VLS-recorded TI images into several phases should be conducted in further TI research.

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Section: Discussionmentioning

confidence: 77%

mentioning

confidence: 99%

Development of an Artificial Intelligence–Based Image Recognition System for Time-Sequence Analysis of Tracheal Intubation

Wu,

Huang,

Tseng

2024

Anesthesia &Amp; Analgesia

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“…Attention plays a vital role in human perception. The attention mechanism is widely used in classification tasks in natural language processing [ 31 , 32 ] and computer vision [ 33 , 34 ]. In this study, we present a spatial attention-based method to further improve model performance.…”

Section: Methodsmentioning

confidence: 99%

iEnhancer-DCSA: identifying enhancers via dual-scale convolution and spatial attention

Wang

2023

BMC Genomics

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Background Due to the dynamic nature of enhancers, identifying enhancers and their strength are major bioinformatics challenges. With the development of deep learning, several models have facilitated enhancers detection in recent years. However, existing studies either neglect different length motifs information or treat the features at all spatial locations equally. How to effectively use multi-scale motifs information while ignoring irrelevant information is a question worthy of serious consideration. In this paper, we propose an accurate and stable predictor iEnhancer-DCSA, mainly composed of dual-scale fusion and spatial attention, automatically extracting features of different length motifs and selectively focusing on the important features. Results Our experimental results demonstrate that iEnhancer-DCSA is remarkably superior to existing state-of-the-art methods on the test dataset. Especially, the accuracy and MCC of enhancer identification are improved by 3.45% and 9.41%, respectively. Meanwhile, the accuracy and MCC of enhancer classification are improved by 7.65% and 18.1%, respectively. Furthermore, we conduct ablation studies to demonstrate the effectiveness of dual-scale fusion and spatial attention. Conclusions iEnhancer-DCSA will be a valuable computational tool in identifying and classifying enhancers, especially for those not included in the training dataset.

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“…Zhang et al [29] created a deep convolutional neural network (DCNN) model, which automatically performed Mallampati score classification in an accurate and objective manner. Hayasaka et al [30] developed an AI model that assesses intubation difficulty.…”

Section: Remote Physical Examination Airway Assessmentmentioning

confidence: 99%

Remote, automatic, digital preanesthetic evaluation – are we there yet?

Pasternak,

Szczeklik,

Białka

et al. 2024

ait

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In recent years there have been multiple advancements in information technology, specifically e-health and telemedicine. Many of these advancements stemmed from the need to ensure patient and doctor safety during the coronavirus disease 2019 (COVID-19) pandemic. Patient evaluation without face-to-face contact to minimise the risk of viral transmission has become a new safety standard for doctors in many specialties.In this review, we examine the current state of the preanaesthetic evaluation and analyse its components and their potential for remote execution using current and potential future telemedical facilities and technologies. We review the literature regarding the beneficiaries of remote preanaesthetic evaluation. Finally, we discuss which aspects of the preanaesthetic assessment can be performed automatically. RATIONALE FOR REMOTE PREANESTHETIC EVALUATION Social distancing during the COVID-19 pandemicZhang et al. [1] reviewed the effectiveness of virtual perioperative assessment for surgical patients

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Critical element prediction of tracheal intubation difficulty: Automatic Mallampati classification by jointly using handcrafted and attention-based deep features

Cited by 5 publications

References 24 publications

Development of an Artificial Intelligence–Based Image Recognition System for Time-Sequence Analysis of Tracheal Intubation

Development of an Artificial Intelligence–Based Image Recognition System for Time-Sequence Analysis of Tracheal Intubation

iEnhancer-DCSA: identifying enhancers via dual-scale convolution and spatial attention

Remote, automatic, digital preanesthetic evaluation – are we there yet?

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