Applications of Artificial Intelligence to Office Laryngoscopy: A Scoping Review

Yao, Peter; Usman, Moon; Chen, Yu H.; German, Alexander; Andreadis, Katerina; Mages, Keith; Rameau, Anaïs

doi:10.1002/lary.29886

Cited by 26 publications

(23 citation statements)

References 25 publications

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“…17 Emerging work seeks to apply similar analytic techniques to laryngoscopy video. 18 However, efforts in video laryngoscopy have been slower, perhaps as a result of limited data sets as well as the anatomic complexity and dynamic nature of the glottis. Studies (Table 1) used keyframes (informative images) extracted from laryngoscopy videos as data inputs to train ML models.…”

Section: Part 1: Data Modalitymentioning

confidence: 99%

Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review

Bensoussan

Vanstrum

Johns

et al. 2022

Otolaryngol.--head neck surg.

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Objective This state of the art review aims to examine contemporary advances in applications of artificial intelligence (AI) to the screening, detection, management, and prognostication of laryngeal cancer (LC). Data Sources Four bibliographic databases were searched: PubMed, EMBASE, Cochrane, and IEEE. Review Methods A structured review of the current literature (up to January 2022) was performed. Search terms related to topics of AI in LC were identified and queried by 2 independent reviewers. Citations of selected studies and review articles were also evaluated to ensure comprehensiveness. Conclusions AI applications in LC have encompassed a variety of data modalities, including radiomics, genomics, acoustics, clinical data, and videomics, to support screening, diagnosis, therapeutic decision making, and prognosis. However, most studies remain at the proof-of-concept level, as AI algorithms are trained on single-institution databases with limited data sets and a single data modality. Implications for Practice AI algorithms in LC will need to be trained on large multi-institutional data sets and integrate multimodal data for optimal performance and clinical utility from screening to prognosis. Out of the data types reviewed, genomics has the most potential to provide generalizable models thanks to available large multi-institutional open access genomic data sets. Voice acoustic data represent an inexpensive and accurate biomarker, which is easy and noninvasive to capture, offering a unique opportunity for screening and monitoring of LA, especially in low-resource settings.

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Section: Part 1: Data Modalitymentioning

confidence: 99%

Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review

Bensoussan

Vanstrum

Johns

et al. 2022

Otolaryngol.--head neck surg.

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“…However, we examined several recent studies that employed artificial neural networks for the computer-aided diagnosis of laryngeal cancer [ 7 , 20 , 21 , 22 , 23 ] and found that although the datasets were labelled and well-structured, the number of images still ranged from 3000 to 25,000. Such samplings applied to clinical applications are still a considerable workload for science, technology, engineering, and math (STEM) researchers willing to cooperate with physicians [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…Interestingly, the public datasets related to informative frame selection are rare, only 3% (3/97) compared to vibration analysis, lesion recognition, etc. [ 24 ]. Our fundamental concern is that existing algorithms are insufficient for extracting specific patterns of high-quality data in enormous data with noise.…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework of Manifold Learning Cascade-Clustering for the Informative Frame Selection

Zhang

Wei

et al. 2023

Diagnostics

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Narrow band imaging is an established non-invasive tool used for the early detection of laryngeal cancer in surveillance examinations. Most images produced from the examination are useless, such as blurred, specular reflection, and underexposed. Removing the uninformative frames is vital to improve detection accuracy and speed up computer-aided diagnosis. It often takes a lot of time for the physician to manually inspect the informative frames. This issue is commonly addressed by a classifier with task-specific categories of the uninformative frames. However, the definition of the uninformative categories is ambiguous, and tedious labeling still cannot be avoided. Here, we show that a novel unsupervised scheme is comparable to the current benchmarks on the dataset of NBI-InfFrames. We extract feature embedding using a vanilla neural network (VGG16) and introduce a new dimensionality reduction method called UMAP that distinguishes the feature embedding in the lower-dimensional space. Along with the proposed automatic cluster labeling algorithm and cost function in Bayesian optimization, the proposed method coupled with UMAP achieves state-of-the-art performance. It outperforms the baseline by 12% absolute. The overall median recall of the proposed method is currently the highest, 96%. Our results demonstrate the effectiveness of the proposed scheme and the robustness of detecting the informative frames. It also suggests the patterns embedded in the data help develop flexible algorithms that do not require manual labeling.

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“…Parallelly, the use of AI in videoendoscopy, especially in the gastrointestinal field, has already become relevant in the literature and even on the market ( 18 ). When moving to the specific field of UADT, however, only a few studies have been published in the current literature, with most focusing on laryngeal endoscopy ( 19 ). Among all AI-powered methods, deep learning (DL) techniques based on convolutional neural networks (CNNs) are increasingly used in UADT videoendoscopy analysis for automatic disease detection ( 20 – 22 ), classification ( 23 , 24 ), and segmentation ( 25 ).…”

Section: Introductionmentioning

confidence: 99%

Videomics of the Upper Aero-Digestive Tract Cancer: Deep Learning Applied to White Light and Narrow Band Imaging for Automatic Segmentation of Endoscopic Images

et al. 2022

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IntroductionNarrow Band Imaging (NBI) is an endoscopic visualization technique useful for upper aero-digestive tract (UADT) cancer detection and margins evaluation. However, NBI analysis is strongly operator-dependent and requires high expertise, thus limiting its wider implementation. Recently, artificial intelligence (AI) has demonstrated potential for applications in UADT videoendoscopy. Among AI methods, deep learning algorithms, and especially convolutional neural networks (CNNs), are particularly suitable for delineating cancers on videoendoscopy. This study is aimed to develop a CNN for automatic semantic segmentation of UADT cancer on endoscopic images.Materials and MethodsA dataset of white light and NBI videoframes of laryngeal squamous cell carcinoma (LSCC) was collected and manually annotated. A novel DL segmentation model (SegMENT) was designed. SegMENT relies on DeepLabV3+ CNN architecture, modified using Xception as a backbone and incorporating ensemble features from other CNNs. The performance of SegMENT was compared to state-of-the-art CNNs (UNet, ResUNet, and DeepLabv3). SegMENT was then validated on two external datasets of NBI images of oropharyngeal (OPSCC) and oral cavity SCC (OSCC) obtained from a previously published study. The impact of in-domain transfer learning through an ensemble technique was evaluated on the external datasets.Results219 LSCC patients were retrospectively included in the study. A total of 683 videoframes composed the LSCC dataset, while the external validation cohorts of OPSCC and OCSCC contained 116 and 102 images. On the LSCC dataset, SegMENT outperformed the other DL models, obtaining the following median values: 0.68 intersection over union (IoU), 0.81 dice similarity coefficient (DSC), 0.95 recall, 0.78 precision, 0.97 accuracy. For the OCSCC and OPSCC datasets, results were superior compared to previously published data: the median performance metrics were, respectively, improved as follows: DSC=10.3% and 11.9%, recall=15.0% and 5.1%, precision=17.0% and 14.7%, accuracy=4.1% and 10.3%.ConclusionSegMENT achieved promising performances, showing that automatic tumor segmentation in endoscopic images is feasible even within the highly heterogeneous and complex UADT environment. SegMENT outperformed the previously published results on the external validation cohorts. The model demonstrated potential for improved detection of early tumors, more precise biopsies, and better selection of resection margins.

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Applications of Artificial Intelligence to Office Laryngoscopy: A Scoping Review

Cited by 26 publications

References 25 publications

Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review

Artificial Intelligence and Laryngeal Cancer: From Screening to Prognosis: A State of the Art Review

A Novel Framework of Manifold Learning Cascade-Clustering for the Informative Frame Selection

Videomics of the Upper Aero-Digestive Tract Cancer: Deep Learning Applied to White Light and Narrow Band Imaging for Automatic Segmentation of Endoscopic Images

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