Applications of Artificial Intelligence to Office Laryngoscopy: A Scoping Review

Advancing laryngology through artificial intelligence: a comprehensive review of implementation frameworks and strategies

PURPOSE OF REVIEW

This review aims to explore the integration of artificial intelligence (AI) in laryngology, with specific focus on the barriers preventing translation from pilot studies into routine clinical practice and strategies for successful implementation.

RECENT FINDINGS

Laryngology has seen an increasing number of pilot and proof-of-concept studies demonstrating AI's ability to enhance diagnostics, treatment planning, and patient outcomes. Despite these advancements, few tools have been successfully adopted in clinical settings. Effective implementation requires the application of established implementation science frameworks early in the design phase. Additional factors required for the successful integration of AI applications include addressing specific clinical needs, fostering diverse and interdisciplinary teams, and ensuring scalability without compromising model performance. Governance, epistemic, and ethical considerations must also be continuously incorporated throughout the project lifecycle to ensure the safe, responsible, and equitable use of AI technologies.

SUMMARY

While AI hold significant promise for advancing laryngology, its implementation in clinical practice remains limited. Achieving meaningful integration will require a shift toward practical solutions that prioritize clinicians' and patients' needs, usability, sustainability, and alignment with clinical workflows.

The Bridge2AI-voice application: initial feasibility study of voice data acquisition through mobile health

Introduction

Bridge2AI-Voice, a collaborative multi-institutional consortium, aims to generate a large-scale, ethically sourced voice, speech, and cough database linked to health metadata in order to support AI-driven research. A novel smartphone application, the Bridge2AI-Voice app, was created to collect standardized recordings of acoustic tasks, validated patient questionnaires, and validated patient reported outcomes. Before broad data collection, a feasibility study was undertaken to assess the viability of the app in a clinical setting through task performance metrics and participant feedback.

Materials & methods

Participants were recruited from a tertiary academic voice center. Participants were instructed to complete a series of tasks through the application on an iPad. The Plan-Do-Study-Act model for quality improvement was implemented. Data collected included demographics and task metrics including time of completion, successful task/recording completion, and need for assistance. Participant feedback was measured by a qualitative interview adapted from the Mobile App Rating Scale.

Results

Forty-seven participants were enrolled (61% female, 92% reported primary language of English, mean age of 58.3 years). All owned smart devices, with 49% using mobile health apps. Overall task completion rate was 68%, with acoustic tasks successfully recorded in 41% of cases. Participants requested assistance in 41% of successfully completed tasks, with challenges mainly related to design and instruction understandability. Interview responses reflected favorable perception of voice-screening apps and their features.

Conclusion

Findings suggest that the Bridge2AI-Voice application is a promising tool for voice data acquisition in a clinical setting. However, development of improved User Interface/User Experience and broader, diverse feasibility studies are needed for a usable tool.Level of evidence: 3.

Application of artificial intelligence in laryngeal lesions: a systematic review and meta-analysis

OBJECTIVE

The objective of this systematic review and meta-analysis was to evaluate the diagnostic accuracy of AI-assisted technologies, including endoscopy, voice analysis, and histopathology, for detecting and classifying laryngeal lesions.

METHODS

A systematic search was conducted in PubMed, Embase, etc. for studies utilizing voice analysis, histopathology for laryngeal lesions, or AI-assisted endoscopy. The results of diagnostic accuracy, sensitivity and specificity were synthesized by a meta-analysis.

RESULTS

12 studies employing AI-assisted endoscopy, 2 studies for voice analysis, and 4 studies for histopathology were included in the meta-analysis. The combined sensitivity of AI-assisted endoscopy was 91% (95% CI 87-94%) for the classification of benign from malignant lesions and 91% (95% CI 90-93%) for lesion detection. The highest accuracy pooled in detecting lesions versus healthy tissue was the AI-aided endoscopy was 94% (95% CI 92-97%).

CONCLUSIONS

For laryngeal lesions, AI-assisted endoscopy shows excellent diagnosis accuracy. But more sizable prospective trials are needed to confirm the practical clinical value.

A Deep-Learning Model for Multi-class Audio Classification of Vocal Fold Pathologies in Office Stroboscopy

OBJECTIVE

To develop and validate a deep-learning classifier trained on voice data extracted from videolaryngostroboscopy recordings, differentiating between three different vocal fold (VF) states: healthy (HVF), unilateral paralysis (UVFP), and VF lesions, including benign and malignant pathologies.

METHODS

Patients with UVFP (n = 105), VF lesions (n = 63), and HVF (n = 41) were retrospectively identified. Voice samples were extracted from stroboscopic videos (Pentax Laryngeal Strobe Model 9400), including sustained /i/ phonation, pitch glide, and /i/ sniff task. Extracted audio files were converted into Mel-spectrograms. Voice samples were independently divided into training (80%), validation (10%), and test (10%) by patient. Pretrained ResNet18 models were trained to classify (1) HVF and pathological VF (lesions and UVFP), and (2) HVF, UVFP, and VF lesions. Both classifiers were further validated on an external dataset consisting of 12 UVFP, 13 VF lesions, and 15 HVF patients. Model performances were evaluated by accuracy and F1-score.

RESULTS

When evaluated on a hold-out test set, the binary classifier demonstrated stronger performance compared to the multi-class classifier (accuracy 83% vs. 40%; F1-score 0.90 vs. 0.36). When evaluated on an external dataset, the binary classifier achieved an accuracy of 63% and F1-score of 0.48, compared to 35% and 0.25 for the multi-class classifier.

CONCLUSIONS

Deep-learning classifiers differentiating HVF, UVFP, and VF lesions were developed using voice data from stroboscopic videos. Although healthy and pathological voice were differentiated with moderate accuracy, multi-class classification lowered model performance. The model performed poorly on an external dataset. Voice captured in stroboscopic videos may have limited diagnostic value, though further studies are needed.

LEVEL OF EVIDENCE

4 Laryngoscope, 2025.

Diagnostic accuracy of deep learning-based algorithms in laryngoscopy: a systematic review and meta-analysis

PURPOSE

Laryngoscopy is routinely used for suspicious vocal cord lesions with limited performance. Accumulated studies have demonstrated the bright prospect of deep learning in processing medical imaging. In this study, we perform a systematic review and meta-analysis to investigate diagnostic utility of deep learning in laryngoscopy.

METHODS

The study was performed according to the Primary Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. We comprehensively retrieved articles from the PubMed, Scopus, Embase, and Web of Science up to July 14, 2024. Eligible studies with application of deep learning algorithm in laryngoscopy were assessed and enrolled by two independent investigators. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio with 95% confidence intervals (CIs) were calculated using a random effects model.

RESULTS

We retained 9 eligible studies adding up to 106,175 endoscopic images for the meta-analysis. The pooled sensitivity and specificity to diagnose laryngeal cancer were 0.95(95% CI: 0.85-0.98) and 0.96 (95% CI: 0.91-0.98). The area under the curve of deep learning was 0.99 (95%CI: 0.97-0.99).

CONCLUSION

Deep learning demonstrated excellent diagnostic efficacy in assessing laryngeal cancer with laryngoscope images in current studies, which manifests its potential of aiding endoscopist for laryngeal cancer diagnosis and clinical decision making.

New developments in the application of artificial intelligence to laryngology

PURPOSE OF REVIEW

The purpose of this review is to summarize the existing literature on artificial intelligence technology utilization in laryngology, highlighting recent advances and current barriers to implementation.

RECENT FINDINGS

The volume of publications studying applications of artificial intelligence in laryngology has rapidly increased, demonstrating a strong interest in utilizing this technology. Vocal biomarkers for disease screening, deep learning analysis of videolaryngoscopy for lesion identification, and auto-segmentation of videofluoroscopy for detection of aspiration are a few of the new ways in which artificial intelligence is poised to transform clinical care in laryngology. Increasing collaboration is ongoing to establish guidelines and standards for the field to ensure generalizability.

SUMMARY

Artificial intelligence tools have the potential to greatly advance laryngology care by creating novel screening methods, improving how data-heavy diagnostics of laryngology are analyzed, and standardizing outcome measures. However, physician and patient trust in artificial intelligence must improve for the technology to be successfully implemented. Additionally, most existing studies lack large and diverse datasets, external validation, and consistent ground-truth references necessary to produce generalizable results. Collaborative, large-scale studies will fuel technological innovation and bring artificial intelligence to the forefront of patient care in laryngology.

Summary of the best evidence for vocal rehabilitation training in adults with unilateral vocal fold paralysis

Purpose

To search, evaluate, and summarize the relevant evidence of voice rehabilitation training in adults with unilateral vocal fold paralysis (UVFP).

Methods

Using the '6S' model, the database of UpToDate, BMJ Best Practice, domestic and foreign guidelines, and voice rehabilitation training for adults with UVFP were systematically searched. The search includes clinical decisions, guidelines, systematic reviews, randomized controlled trials, and expert consensus. Data search since the establishment of the database to 27 November 2023. The quality evaluation of the literature methodology and the extraction and integration of evidence were carried out independently by two researchers.

Results

A total of 12 literature were included, including 2 systematic reviews, 2 expert consensus, 4 guidelines, 2 clinical decisions, and 2 randomized controlled trials. Four categories of 16 pieces of evidence were summarized: evaluation of vocal cord paralysis, voice rehabilitation training, health education, joint decision-making, and multidisciplinary cooperation.

Conclusion

This study summarized the best evidence of voice rehabilitation training for adults with UVFP and provided evidence-based summary for clinical staff to carry out relevant interventions.

Artificial intelligence based diagnosis of sulcus: assesment of videostroboscopy via deep learning

PURPOSE

To develop a convolutional neural network (CNN)-based model for classifying videostroboscopic images of patients with sulcus, benign vocal fold (VF) lesions, and healthy VFs to improve clinicians' accuracy in diagnosis during videostroboscopies when evaluating sulcus.

MATERIALS AND METHODS

Videostroboscopies of 433 individuals who were diagnosed with sulcus (91), who were diagnosed with benign VF diseases (i.e., polyp, nodule, papilloma, cyst, or pseudocyst [311]), or who were healthy (33) were analyzed. After extracting 91,159 frames from videostroboscopies, a CNN-based model was created and tested. The healthy and sulcus groups underwent binary classification. In the second phase of the study, benign VF lesions were added to the training set, and multiclassification was executed across all groups. The proposed CNN-based model results were compared with five laryngology experts' assessments.

RESULTS

In the binary classification phase, the CNN-based model achieved 98% accuracy, 98% recall, 97% precision, and a 97% F1 score for classifying sulcus and healthy VFs. During the multiclassification phase, when evaluated on a subset of frames encompassing all included groups, the CNN-based model demonstrated greater accuracy when compared with that of the five laryngologists (%76 versus 72%, 68%, 72%, 63%, and 72%).

CONCLUSION

The utilization of a CNN-based model serves as a significant aid in the diagnosis of sulcus, a VF disease that presents notable challenges in the diagnostic process. Further research could be undertaken to assess the practicality of implementing this approach in real-time application in clinical practice.

AI Detection of Glottic Neoplasm Using Voice Signals, Demographics, and Structured Medical Records

OBJECTIVE

This study investigated whether artificial intelligence (AI) models combining voice signals, demographics, and structured medical records can detect glottic neoplasm from benign voice disorders.

METHODS

We used a primary dataset containing 2-3 s of vowel "ah", demographics, and 26 items of structured medical records (e.g., symptoms, comorbidity, smoking and alcohol consumption, vocal demand) from 60 patients with pathology-proved glottic neoplasm (i.e., squamous cell carcinoma, carcinoma in situ, and dysplasia) and 1940 patients with benign voice disorders. The validation dataset comprised data from 23 patients with glottic neoplasm and 1331 patients with benign disorders. The AI model combined convolutional neural networks, gated recurrent units, and attention layers. We used 10-fold cross-validation (training-validation-testing: 8-1-1) and preserved the percentage between neoplasm and benign disorders in each fold.

RESULTS

Results from the AI model using voice signals reached an area under the ROC curve (AUC) value of 0.631, and additional demographics increased this to 0.807. The highest AUC of 0.878 was achieved when combining voice, demographics, and medical records (sensitivity: 0.783, specificity: 0.816, accuracy: 0.815). External validation yielded an AUC value of 0.785 (voice plus demographics; sensitivity: 0.739, specificity: 0.745, accuracy: 0.745). Subanalysis showed that AI had higher sensitivity but lower specificity than human assessment (p < 0.01). The accuracy of AI detection with additional medical records was comparable with human assessment (82% vs. 83%, p = 0.78).

CONCLUSIONS

Voice signal alone was insufficient for AI differentiation between glottic neoplasm and benign voice disorders, but additional demographics and medical records notably improved AI performance and approximated the prediction accuracy of humans.

LEVEL OF EVIDENCE

NA Laryngoscope, 134:4585-4592, 2024.

Standardization, Collaboration, and Education in the Implementation of Artificial Intelligence in Otolaryngology: The Key to Scalable Impact

The study delves into the crucial role of standardization, collaboration, and education in the integration of artificial intelligence (AI) in otolaryngology. It emphasizes the necessity of large, diverse datasets for effective AI implementation in health care, particularly in otolaryngology, due to its reliance on medical imagery and diverse instruments. The text identifies current barriers, including siloed work in academia and sparse academic-industrial partnerships, while proposing solutions like forming interdisciplinary teams and aligning incentives. Moreover, it discusses the importance of standardizing AI projects through system reporting and advocates for AI education and literacy among otolaryngology practitioners.

NBI Classification Optimization With Morphological Characteristics in Vocal Fold Leukoplakia

Objectives: To investigate the pathological contribution of vocal fold leukoplakia (VFL) of type II in narrow-band imaging (NBI) classification and morphological characteristics to improve pathological prediction. Material and Methods: The 59 VFL patients with type II in 2019 Ni classification in NBI were included. The pathological reports were collected and divided following 2005 WHO Blue Book. Low-risk VFL contained non-, mild, moderate dysplasia, high-risk VFL included severe dysplasia. The morphological classification and laryngoscopic scoring system were employed to evaluate leukoplakia for pathological prediction. Results: The pathologies contained 1 case of leukoplakia with non-dysplasia, 12 of mild dysplasia, 15 of moderate dysplasia, 8 of severe dysplasia, and 23 of carcinoma. The 30 smooth VFL contained 1 non-dysplasia, 12 mild dysplasia, 14 moderate dysplasia, 2 severe dysplasia, and 1 carcinoma. The 29 rough cases included 1 moderate dysplasia, 6 severe dysplasia, and 22 carcinomas. Laryngoscopic scoring system revealed irregular texture, large size, and thick lesion as factors in relationship with high-risk leukoplakia in univariate (P = .002, <.001, <.001) and multivariate (P = .025, .002, .016) analysis, irregular texture was the most accurate predictor of high-risk VFL pathology. Conclusions and Significance: The pathologies of VFL with type II in NBI classification were hard to be predicted. Morphological irregular/rough texture contributed to predict high-risk pathology in leukoplakia.

Optimal Pathological Prediction of Vocal Fold Leukoplakia Based on IPCLs and Morphology

OBJECTIVES

To establish a combined classification based on intrapapillary capillary loops (IPCLs) and morphological characteristics to improve the accuracy of pathological prediction of vocal fold leukoplakia (VFL).

MATERIAL AND METHODS

A prospective research was conducted of VFL patients diagnosed by IPCLs and morphology. The VFL cases were classified as Type I-III based on IPCLs and morphological characteristics. Type I referred to VFL with dendritic vessels but not IPCLs. Type II defined VFL without any IPCLs or vessels and classified by morphology into two subtypes as non-rough Type IIa and rough Type IIb. Type III referred to VFL with IPCLs and classified into two subtypes as Type IIIa with small IPCLs and Type IIIb with large IPCLs or vascular distortion in or around lesions. Predicting pathology accuracy was analyzed.

RESULTS

182 eligible patients were recruited. The prediction accuracy rates of VFL pathology were 81.5% according to the 2019 Ni classification. The combined classification includes 4 cases of Type I, 28 Type IIa, 35 Type IIb, 56 Type IIIa, and 59 Type IIIb VFLs. The prediction accuracy rate of combined classification ranged from 95.1% to 97.3% in three observers. The average sensitivity, specificity, positive predictive value, negative predictive value was 97.8%, 86.2%, 97.4%, 88.2%, respectively. The inter-observer agreement varied from 84.1% to 94.0%, and mean area under curve of receiver-operating curve analysis was 0.954.

CONCLUSIONS AND SIGNIFICANCE

The new combined classification based on IPCLs and morphological characteristics could predict pathology of VFL accurately.

LEVEL OF EVIDENCE

4 Laryngoscope, 2024.

Laryngeal Cancer Screening During Flexible Video Laryngoscopy Using Large Computer Vision Models

OBJECTIVE

Develop an artificial intelligence assisted computer vision model to screen for laryngeal cancer during flexible laryngoscopy.

METHODS

Using laryngeal images and flexible laryngoscopy video recordings, we developed computer vision models to classify video frames for usability and cancer screening. A separate model segments any identified lesions on the frames. We used these computer vision models to construct a video stream annotation system. This system classifies findings from flexible laryngoscopy as "potentially malignant" or "probably benign" and segments any detected lesions. Additionally, the model provides a confidence level for each classification.

RESULTS

The overall accuracy of the flexible laryngoscopy cancer screening model was 92%. For cancer screening, it achieved a sensitivity of 97.7% and a specificity of 76.9%. The segmentation model attained an average precision at a 0.50 intersection-over-union of 0.595. The confidence level for positive screening results can assist clinicians in counseling patients regarding the findings.

CONCLUSION

Our model is highly sensitive and adequately specific for laryngeal cancer screening. Segmentation helps endoscopists identify and describe potential lesions. Further optimization is required to enable the model's deployment in clinical settings for real-time annotation during flexible laryngoscopy.

The Use of Deep Learning Software in the Detection of Voice Disorders: A Systematic Review

OBJECTIVE

To summarize the use of deep learning in the detection of voice disorders using acoustic and laryngoscopic input, compare specific neural networks in terms of accuracy, and assess their effectiveness compared to expert clinical visual examination.

DATA SOURCES

Embase, MEDLINE, and Cochrane Central.

REVIEW METHODS

Databases were screened through November 11, 2023 for relevant studies. The inclusion criteria required studies to utilize a specified deep learning method, use laryngoscopy or acoustic input, and measure accuracy of binary classification between healthy patients and those with voice disorders.

RESULTS

Thirty-four studies met the inclusion criteria, with 18 focusing on voice analysis, 15 on imaging analysis, and 1 both. Across the 18 acoustic studies, 21 programs were used for identification of organic and functional voice disorders. These technologies included 10 convolutional neural networks (CNNs), 6 multilayer perceptrons (MLPs), and 5 other neural networks. The binary classification systems yielded a mean accuracy of 89.0% overall, including 93.7% for MLP programs and 84.5% for CNNs. Among the 15 imaging analysis studies, a total of 23 programs were utilized, resulting in a mean accuracy of 91.3%. Specifically, the twenty CNNs achieved a mean accuracy of 92.6% compared to 83.0% for the 3 MLPs.

CONCLUSION

Deep learning models were shown to be highly accurate in the detection of voice pathology, with CNNs most effective for assessing laryngoscopy images and MLPs most effective for assessing acoustic input. While deep learning methods outperformed expert clinical exam in limited comparisons, further studies integrating external validation are necessary.

An introduction to machine learning and generative artificial intelligence for otolaryngologists-head and neck surgeons: a narrative review

PURPOSE

Despite the robust expansion of research surrounding artificial intelligence (AI) and machine learning (ML) and their applications to medicine, these methodologies often remain opaque and inaccessible to many otolaryngologists. Especially, with the increasing ubiquity of large-language models (LLMs), such as ChatGPT and their potential implementation in clinical practice, clinicians may benefit from a baseline understanding of some aspects of AI. In this narrative review, we seek to clarify underlying concepts, illustrate applications to otolaryngology, and highlight future directions and limitations of these tools.

METHODS

Recent literature regarding AI principles and otolaryngologic applications of ML and LLMs was reviewed via search in PubMed and Google Scholar.

RESULTS

Significant recent strides have been made in otolaryngology research utilizing AI and ML, across all subspecialties, including neurotology, head and neck oncology, laryngology, rhinology, and sleep surgery. Potential applications suggested by recent publications include screening and diagnosis, predictive tools, clinical decision support, and clinical workflow improvement via LLMs. Ongoing concerns regarding AI in medicine include ethical concerns around bias and data sharing, as well as the "black box" problem and limitations in explainability.

CONCLUSIONS

Potential implementations of AI in otolaryngology are rapidly expanding. While implementation in clinical practice remains theoretical for most of these tools, their potential power to influence the practice of otolaryngology is substantial.

LEVEL OF EVIDENCE: 4

Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey

INTRODUCTION

Accuracy and validity of voice AI algorithms rely on substantial quality voice data. Although commensurable amounts of voice data are captured daily in voice centers across North America, there is no standardized protocol for acoustic data management, which limits the usability of these datasets for voice artificial intelligence (AI) research.

OBJECTIVE

The aim was to capture current practices of voice data collection, storage, analysis, and perceived limitations to collaborative voice research.

METHODS

A 30-question online survey was developed with expert guidance from the voicecollab.ai members, an international collaborative of voice AI researchers. The survey was disseminated via REDCap to an estimated 200 practitioners at North American voice centers. Survey questions assessed respondents' current practices in terms of acoustic data collection, storage, and retrieval as well as limitations to collaborative voice research.

RESULTS

Seventy-two respondents completed the survey of which 81.7% were laryngologists and 18.3% were speech language pathologists (SLPs). Eighteen percent of respondents reported seeing 40%-60% and 55% reported seeing >60 patients with voice disorders weekly (conservative estimate of over 4000 patients/week). Only 28% of respondents reported utilizing standardized protocols for collection and storage of acoustic data. Although, 87% of respondents conduct voice research, only 38% of respondents report doing so on a multi-institutional level. Perceived limitations to conducting collaborative voice research include lack of standardized methodology for collection (30%) and lack of human resources to prepare and label voice data adequately (55%).

CONCLUSION

To conduct large-scale multi-institutional voice research with AI, there is a pertinent need for standardization of acoustic data management, as well as an infrastructure for secure and efficient data sharing.

LEVEL OF EVIDENCE

5 Laryngoscope, 134:1333-1339, 2024.

Vocal cord leukoplakia classification using deep learning models in white light and narrow band imaging endoscopy images

BACKGROUND

Accurate vocal cord leukoplakia classification is critical for the individualized treatment and early detection of laryngeal cancer. Numerous deep learning techniques have been proposed, but it is unclear how to select one to apply in the laryngeal tasks. This article introduces and reliably evaluates existing deep learning models for vocal cord leukoplakia classification.

METHODS

We created white light and narrow band imaging (NBI) image datasets of vocal cord leukoplakia which were classified into six classes: normal tissues (NT), inflammatory keratosis (IK), mild dysplasia (MiD), moderate dysplasia (MoD), severe dysplasia (SD), and squamous cell carcinoma (SCC). Vocal cord leukoplakia classification was performed using six classical deep learning models, AlexNet, VGG, Google Inception, ResNet, DenseNet, and Vision Transformer.

RESULTS

GoogLeNet (i.e., Google Inception V1), DenseNet-121, and ResNet-152 perform excellent classification. The highest overall accuracy of white light image classification is 0.9583, while the highest overall accuracy of NBI image classification is 0.9478. These three neural networks all provide very high sensitivity, specificity, and precision values.

CONCLUSION

GoogLeNet, ResNet, and DenseNet can provide accurate pathological classification of vocal cord leukoplakia. It facilitates early diagnosis, providing judgment on conservative treatment or surgical treatment of different degrees, and reducing the burden on endoscopists.

Interpretable Computer Vision to Detect and Classify Structural Laryngeal Lesions in Digital Flexible Laryngoscopic Images

OBJECTIVE

To localize structural laryngeal lesions within digital flexible laryngoscopic images and to classify them as benign or suspicious for malignancy using state-of-the-art computer vision detection models.

STUDY DESIGN

Cross-sectional diagnostic study SETTING: Tertiary care voice clinic METHODS: Digital stroboscopic videos, demographic and clinical data were collected from patients evaluated for a structural laryngeal lesion. Laryngoscopic images were extracted from videos and manually labeled with bounding boxes encompassing the lesion. Four detection models were employed to simultaneously localize and classify structural laryngeal lesions in laryngoscopic images. Classification accuracy, intersection over union (IoU) and mean average precision (mAP) were evaluated as measures of classification, localization, and overall performance, respectively.

RESULTS

In total, 8,172 images from 147 patients were included in the laryngeal image dataset. Classification accuracy was 88.5 for individual laryngeal images and increased to 92.0 when all images belonging to the same sequence (video) were considered. Mean average precision across all four detection models was 50.1 using an IoU threshold of 0.5 to determine successful localization.

CONCLUSION

Results of this study showed that deep neural network-based detection models trained using a labeled dataset of digital laryngeal images have the potential to classify structural laryngeal lesions as benign or suspicious for malignancy and to localize them within an image. This approach provides valuable insight into which part of the image was used by the model to determine a diagnosis, allowing clinicians to independently evaluate models' predictions.

Advances in Image-Based Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery: A Systematic Review

OBJECTIVE

To update the literature and provide a systematic review of image-based artificial intelligence (AI) applications in otolaryngology, highlight its advances, and propose future challenges.

DATA SOURCES

Web of Science, Embase, PubMed, and Cochrane Library.

REVIEW METHODS

Studies written in English, published between January 2020 and December 2022. Two independent authors screened the search results, extracted data, and assessed studies.

RESULTS

Overall, 686 studies were identified. After screening titles and abstracts, 325 full-text studies were assessed for eligibility, and 78 studies were included in this systematic review. The studies originated from 16 countries. Among these countries, the top 3 were China (n = 29), Korea (n = 8), the United States, and Japan (n = 7 each). The most common area was otology (n = 35), followed by rhinology (n = 20), pharyngology (n = 18), and head and neck surgery (n = 5). Most applications of AI in otology, rhinology, pharyngology, and head and neck surgery mainly included chronic otitis media (n = 9), nasal polyps (n = 4), laryngeal cancer (n = 12), and head and neck squamous cell carcinoma (n = 3), respectively. The overall performance of AI in accuracy, the area under the curve, sensitivity, and specificity were 88.39 ± 9.78%, 91.91 ± 6.70%, 86.93 ± 11.59%, and 88.62 ± 14.03%, respectively.

CONCLUSION

This state-of-the-art review aimed to highlight the increasing applications of image-based AI in otorhinolaryngology head and neck surgery. The following steps will entail multicentre collaboration to ensure data reliability, ongoing optimization of AI algorithms, and integration into real-world clinical practice. Future studies should consider 3-dimensional (3D)-based AI, such as 3D surgical AI.

Visual-perceptive assessment of glottic characteristics of vocal nodules by means of high-speed videoendoscopy

OBJECTIVE

Visual-perceptive assessment of glottic characteristics of vocal nodules by means of high-speed videoendoscopy.

METHODS

Descriptive observational research with convenience sampling of five laryngeal videos of women with an average age of 25 years. The diagnosis of vocal nodules was defined by two otolaryngologists, with 100% intra-rater agreement and 53.40% inter-rater agreement and five otolaryngologists as judge assessed the laryngeal videos based on an adapted protocol. The statistical analysis calculated measures of central tendency and dispersion, as well as percentage. The AC1 coefficient was used for agreement analysis.

RESULTS

In high-speed videoendoscopy imaging, vocal nodules are characterized by amplitude of the mucosal wave and muco-undulatory movement with magnitude between 50% and 60%. Non-vibrating segments of vocal folds are scarce, and the glottal cycle does not show a predominant phase, it is symmetric and periodic. Glottal closure is characterized by the presence of a mid-posterior triangular chink (double chink or isolated mid-posterior triangular chink), without movement of supraglottic laryngeal structures, with irregular contour of the free edge of vocal folds, which are vertically on-plane.

CONCLUSION

Vocal nodules present mid-posterior triangular chink and irregular free edge contour. Amplitude and mucosal wave were partially reduced.

LEVEL OF EVIDENCE

Level 4 (Case-series).

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

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.

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

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.

Convolutional neural network based anatomical site identification for laryngoscopy quality control: A multicenter study

OBJECTIVES

Video laryngoscopy is an important diagnostic tool for head and neck cancers. The artificial intelligence (AI) system has been shown to monitor blind spots during esophagogastroduodenoscopy. This study aimed to test the performance of AI-driven intelligent laryngoscopy monitoring assistant (ILMA) for landmark anatomical sites identification on laryngoscopic images and videos based on a convolutional neural network (CNN).

MATERIALS AND METHODS

The laryngoscopic images taken from January to December 2018 were retrospectively collected, and ILMA was developed using the CNN model of Inception-ResNet-v2 + Squeeze-and-Excitation Networks (SENet). A total of 16,000 laryngoscopic images were used for training. These were assigned to 20 landmark anatomical sites covering six major head and neck regions. In addition, the performance of ILMA in identifying anatomical sites was validated using 4000 laryngoscopic images and 25 videos provided by five other tertiary hospitals.

RESULTS

ILMA identified the 20 anatomical sites on the laryngoscopic images with a total accuracy of 97.60 %, and the average sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 100 %, 99.87 %, 97.65 %, and 99.87 %, respectively. In addition, multicenter clinical verification displayed that the accuracy of ILMA in identifying the 20 targeted anatomical sites in 25 laryngoscopic videos from five hospitals was ≥95 %.

CONCLUSION

The proposed CNN-based ILMA model can rapidly and accurately identify the anatomical sites on laryngoscopic images. The model can reflect the coverage of anatomical regions of the head and neck by laryngoscopy, showing application potential in improving the quality of laryngoscopy.

Nyquist Plot Parametrization for Quantitative Analysis of Vibration of the Vocal Folds

OBJECTIVES

The Nyquist plot provides a graphical representation of the glottal cycles as elliptical trajectories in a 2D plane. This study proposes a methodology to parameterize the Nyquist plot with application to support the quantitative analysis of voice disorders.

METHODS

We considered high-speed videoendoscopy recordings of 33 functional dysphonia (FD) patients and 33 normophonic controls (NC). Quantitative analysis was performed by computing four shape-based parameters from the Nyquist plot: Variability, Size (Perimeter and Area), and Consistency. Additionally, we performed automatic classification using a linear support vector machine and feature importance analysis by combining the proposed features with state-of-the-art glottal area waveform (GAW) parameters.

RESULTS

We found that the inter-cycle variability was significantly higher in FD patients compared to NC. We achieved a classification accuracy of 83% when the top 30 most important features were used. Furthermore, the proposed Nyquist plot features were ranked in the top 12 most important features.

CONCLUSIONS

The Nyquist plot provides complementary information for subjective and objective assessment of voice disorders. On the one hand, with visual inspection it is possible to observe intra- and inter-glottal cycle irregularities during sustained phonation. On the other hand, shaped-based parameters allow quantifying such irregularities and provide complementary information to state-of-the-art GAW parameters.

Usefulness, acceptation and feasibility of electronic medical history tool in reflux disease

OBJECTIVES

To investigate usefulness, feasibility, and patient satisfaction of an electronic pre-consultation medical history tool (EPMH) in laryngopharyngeal reflux (LPR) work-up.

METHODS

Seventy-five patients with LPR were invited to complete electronic medical history assessment prior to laryngology consultation. EPMH collected the following parameters: demographic and epidemiological data, medication, medical and surgical histories, diet habits, stress and symptom findings. Stress and symptoms were assessed with perceived stress scale and reflux symptom score. Duration of consultation, acceptance, and satisfaction of patients (feasibility, usefulness, effectiveness, understanding of questions) were evaluated through a 9-item patient-reported outcome questionnaire.

RESULTS

Seventy patients completed the evaluation (93% participation rate). The mean age of cohort was 51.2 ± 15.6 years old. There were 35 females and 35 males. Patients who refused to participate (N = 5) were > 65 years old. The consultation duration was significantly lower in patients who used the EPMH (11.3 ± 2.7 min) compared with a control group (18.1 ± 5.1 min; p = 0.001). Ninety percent of patients were satisfied about EPMH easiness and usefulness, while 97.1% thought that EPMH may improve the disease management. Patients would recommend similar approach for otolaryngological or other specialty consultations in 98.6% and 92.8% of cases, respectively.

CONCLUSION

The use of EPMH is associated with adequate usefulness, feasibility, and satisfaction outcomes in patients with LPR. This software is a preliminary step in the development of an AI-based diagnostic decision support tool to help laryngologists in their daily practice. Future randomized controlled studies are needed to investigate the gain of similar approaches on the traditional consultation format.

Comparison of convolutional neural networks for classification of vocal fold nodules from high-speed video images

OBJECTIVES

Deep learning is in this study used through convolutional neural networks (CNN) to the determination of vocal fold nodules. Through high-speed video (HSV) images and computer-assisted tools, a comparison of convolutional neural network models and their accuracy will be presented.

METHODS

The data have been collected by an Ear Nose Throat (ENT) specialist with a 90° rigid scope in the years from 2007 to 2019, where 15.732 high-speed videos have been collected from 7909 patients. A total of 4000 images have been carefully selected, 2000 images were of normal vocal folds and 2000 images were of vocal folds with varying degrees of vocal fold nodules. These images were then split into training-, validation-, and testing-data set, for use with a CNN model with 5 layers (CNN5) and compared to other models: VGG19, MobileNetV2, and Inception-ResNetV2. To compare the neural network models, the following evaluation metrics have been calculated: accuracy, sensitivity, specificity, precision, and negative predictive values.

RESULTS

All the trained CNN models have shown high accuracy when applied to the test set. The accuracy is 97.75%, 83.5%, 91.5%, and 89.75%, for CNN5, VGG19, MobileNetV2, and InceptionResNetV2, respectively.

CONCLUSIONS

Precision was identified as the most relevant performance metric for a study that focuses on the classification of vocal fold nodules. The highest performing model was MobilNetV2 with a precision of 97.7%. The average accuracy across all 4 neural networks was 90.63% showing that neural networks can be used for classifying vocal fold nodules in a clinical setting.

Re-Training of Convolutional Neural Networks for Glottis Segmentation in Endoscopic High-Speed Videos

Endoscopic high-speed video (HSV) systems for visualization and assessment of vocal fold dynamics in the larynx are diverse and technically advancing. To consider resulting "concepts shifts" for neural network (NN)-based image processing, re-training of already trained and used NNs is necessary to allow for sufficiently accurate image processing for new recording modalities. We propose and discuss several re-training approaches for convolutional neural networks (CNN) being used for HSV image segmentation. Our baseline CNN was trained on the BAGLS data set (58,750 images). The new BAGLS-RT data set consists of additional 21,050 images from previously unused HSV systems, light sources, and different spatial resolutions. Results showed that increasing data diversity by means of preprocessing already improves the segmentation accuracy (mIoU + 6.35%). Subsequent re-training further increases segmentation performance (mIoU + 2.81%). For re-training, finetuning with dynamic knowledge distillation showed the most promising results. Data variety for training and additional re-training is a helpful tool to boost HSV image segmentation quality. However, when performing re-training, the phenomenon of catastrophic forgetting should be kept in mind, i.e., adaption to new data while forgetting already learned knowledge.

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

Introduction

Narrow 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 Methods

A 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.

Results

219 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%.

Conclusion

SegMENT 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.

Otorhinolaryngological Advancements in Phoniatrics

The production of voice is a powerful tool not only for communication, but also for artistic performances [...]