LARNet-STC: Spatio-temporal orthogonal region selection network for laryngeal closure detection in endoscopy videos

Wang, Yang Yang; Hamad, Ali; Palaniappan, Kannappan; Lever, Teresa E.; Bunyak, Filiz

doi:10.1016/j.compbiomed.2022.105339

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…Others use various techniques to analyze glottic dynamics, including VF tracking and glottic segmentation 22,24,27,31,33,37,38,41 . Automated segmentation is used in low‐speed videoendoscopy to analyze glottic movement in transnasal endoscopy videos and to detect laryngeal adductor reflex events 29,40,43,44 . Estimation of glottic midline can facilitate assessment of oscillatory symmetry.…”

Section: Discussionmentioning

confidence: 99%

“…22,24,27,31,33,37,38,41 Automated segmentation is used in low-speed videoendoscopy to analyze glottic movement in transnasal endoscopy videos and to detect laryngeal adductor reflex events. 29,40,43,44 Estimation of glottic midline can facilitate assessment of oscillatory symmetry. Although traditional computer vision algorithms can predict such a midline, they are outperformed by deep neural networks.…”

Section: Discussionmentioning

confidence: 99%

“…Automated techniques have been developed for humans and rodents which increase accuracy and efficiency of VF movement analysis and facilitate quantification of subtle variations 13,17–44 . Anatomical differences between humans and rats can impair adaptation of VF tracking tools developed for humans 45,46 .…”

Section: Introductionmentioning

confidence: 99%

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Development and Application of Automated Vocal Fold Tracking Software in a Rat Surgical Model

Pennington‐FitzGerald,

Joshi,

Honzel

et al. 2023

The Laryngoscope

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ObjectiveThe rat is a widely used model for studying vocal fold (VF) function after recurrent laryngeal nerve injury, but common techniques for evaluating rat VF motion remain subjective and imprecise. To address this, we developed a software package, called RatVocalTracker1.0 (RVT1.0), to quantify VF motion and tested it on rats with iatrogenic unilateral vocal fold paralysis (VFP).MethodsA deep neural network was trained to identify the positions of the VFs and arytenoid cartilages (ACs) in transoral laryngoscope videos of the rat glottis. Software was developed to estimate glottic midline, VF displacement, VF velocity, and AC angle. The software was applied to laryngoscope videos of adult rats before and after right recurrent and superior laryngeal nerve transection (N = 15; 6M, 9F). All software calculated metrics were compared before and after injury and validated against manually calculated metrics.ResultsRVT1.0 accurately tracked and quantified VF displacement, VF velocity, and AC angle. Significant differences were found before and after surgery for all RVT1.0 calculated metrics. There was strong agreement between programmatically and manually calculated measures. Automated analysis was also more efficient than nearly all manual methods.ConclusionThis approach provides fast, accurate assessment of VF motion in rats with minimal labor and allows for quantitative comparison of lateral differences in movement. Through this novel analysis method, we can differentiate healthy movement from unilateral VFP. RVT1.0 is open‐source and will be a valuable tool for researchers using the rat model for laryngology research.Level of EvidenceNA Laryngoscope, 2023

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Development and Application of Automated Vocal Fold Tracking Software in a Rat Surgical Model

Pennington‐FitzGerald,

Joshi,

Honzel

et al. 2023

The Laryngoscope

View full text Add to dashboard Cite

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“…This “black box” nature of DL models, apart from hampering the model structure optimization, creates open ethical issues regarding their reliability. One of the most frequent attempts to explore the interpretability of these DL models is to implement a gradient‐weighted class activation map which can visualize the contribution of each pixel to the algorithm prediction 5,6,8‐10,17‐19,27 . This graphical representation can give an insight into how these models work, but we are still far from fully understanding their learning mechanisms.…”

Section: Implications For Practicementioning

confidence: 99%

Artificial Intelligence for Upper Aerodigestive Tract Endoscopy and Laryngoscopy: A Guide for Physicians and State‐of‐the‐Art Review

Sampieri

Baldini

Azam

et al. 2023

Otolaryngol.--head neck surg.

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ObjectiveThe endoscopic and laryngoscopic examination is paramount for laryngeal, oropharyngeal, nasopharyngeal, nasal, and oral cavity benign lesions and cancer evaluation. Nevertheless, upper aerodigestive tract (UADT) endoscopy is intrinsically operator‐dependent and lacks objective quality standards. At present, there has been an increased interest in artificial intelligence (AI) applications in this area to support physicians during the examination, thus enhancing diagnostic performances. The relative novelty of this research field poses a challenge both for the reviewers and readers as clinicians often lack a specific technical background.Data SourcesFour bibliographic databases were searched: PubMed, EMBASE, Cochrane, and Google Scholar.Review MethodsA structured review of the current literature (up to September 2022) was performed. Search terms related to topics of AI, machine learning (ML), and deep learning (DL) in UADT endoscopy and laryngoscopy were identified and queried by 3 independent reviewers. Citations of selected studies were also evaluated to ensure comprehensiveness.ConclusionsForty‐one studies were included in the review. AI and computer vision techniques were used to achieve 3 fundamental tasks in this field: classification, detection, and segmentation. All papers were summarized and reviewed.Implications for PracticeThis article comprehensively reviews the latest developments in the application of ML and DL in UADT endoscopy and laryngoscopy, as well as their future clinical implications. The technical basis of AI is also explained, providing guidance for nonexpert readers to allow critical appraisal of the evaluation metrics and the most relevant quality requirements.

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Computer Vision Foundation Models in Endoscopy: Proof of Concept in Oropharyngeal Cancer

Paderno,

Rau,

Bedi

et al. 2024

The Laryngoscope

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ObjectivesTo evaluate the performance of vision transformer‐derived image embeddings for distinguishing between normal and neoplastic tissues in the oropharynx and to investigate the potential of computer vision (CV) foundation models in medical imaging.MethodsComputational study using endoscopic frames with a focus on the application of a self‐supervised vision transformer model (DINOv2) for tissue classification. High‐definition endoscopic images were used to extract image patches that were then normalized and processed using the DINOv2 model to obtain embeddings. These embeddings served as input for a standard support vector machine (SVM) to classify the tissues as neoplastic or normal. The model's discriminative performance was validated using an 80–20 train‐validation split.ResultsFrom 38 endoscopic NBI videos, 327 image patches were analyzed. The classification results in the validation cohort demonstrated high accuracy (92%) and precision (89%), with a perfect recall (100%) and an F1‐score of 94%. The receiver operating characteristic (ROC) curve yielded an area under the curve (AUC) of 0.96.ConclusionThe use of large vision model‐derived embeddings effectively differentiated between neoplastic and normal oropharyngeal tissues. This study supports the feasibility of employing CV foundation models like DINOv2 in the endoscopic evaluation of mucosal lesions, potentially augmenting diagnostic precision in Otorhinolaryngology.Level of Evidence4 Laryngoscope, 2024

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LARNet-STC: Spatio-temporal orthogonal region selection network for laryngeal closure detection in endoscopy videos

Cited by 5 publications

References 40 publications

Development and Application of Automated Vocal Fold Tracking Software in a Rat Surgical Model

Development and Application of Automated Vocal Fold Tracking Software in a Rat Surgical Model

Artificial Intelligence for Upper Aerodigestive Tract Endoscopy and Laryngoscopy: A Guide for Physicians and State‐of‐the‐Art Review

Computer Vision Foundation Models in Endoscopy: Proof of Concept in Oropharyngeal Cancer

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