Images Of Vocal Folds Research Articles

Improving Laryngoscopy Image Analysis Through Integration of Global Information and Local Features in VoFoCD Dataset.

The diagnosis and treatment of vocal fold disorders heavily rely on the use of laryngoscopy. A comprehensive vocal fold diagnosis requires accurate identification of crucial anatomical structures and potential lesions during laryngoscopy observation. However, existing approaches have yet to explore the joint optimization of the decision-making process, including object detection and image classification tasks simultaneously. In this study, we provide a new dataset, VoFoCD, with 1724 laryngology images designed explicitly for object detection and image classification in laryngoscopy images. Images in the VoFoCD dataset are categorized into four classes and comprise six glottic object types. Moreover, we propose a novel Multitask Efficient trAnsformer network for Laryngoscopy (MEAL) to classify vocal fold images and detect glottic landmarks and lesions. To further facilitate interpretability for clinicians, MEAL provides attention maps to visualize important learned regions for explainable artificial intelligence results toward supporting clinical decision-making. We also analyze our model's effectiveness in simulated clinical scenarios where shaking of the laryngoscopy process occurs. The proposed model demonstrates outstanding performance on our VoFoCD dataset. The accuracy for image classification and mean average precision at an intersection over a union threshold of 0.5 (mAP50) for object detection are 0.951 and 0.874, respectively. Our MEAL method integrates global knowledge, encompassing general laryngoscopy image classification, into local features, which refer to distinct anatomical regions of the vocal fold, particularly abnormal regions, including benign and malignant lesions. Our contribution can effectively aid laryngologists in identifying benign or malignant lesions of vocal folds and classifying images in the laryngeal endoscopy process visually.

Vision-Based Assistance for Vocal Fold Identification in Laryngoscopy with Knowledge Distillation.

Laryngoscopy images play a vital role in merging computer vision and otorhinolaryngology research. However, limited studies offer laryngeal datasets for comparative evaluation. Hence, this study introduces a novel dataset focusing on vocal fold images. Additionally, we propose a lightweight network utilizing knowledge distillation, with our student model achieving around 98.4% accuracy-comparable to the original EfficientNetB1 while reducing model weights by up to 88%. We also present an AI-assisted smartphone solution, enabling a portable and intelligent laryngoscopy system that aids laryngoscopists in efficiently targeting vocal fold areas for observation and diagnosis. To sum up, our contribution includes a laryngeal image dataset and a compressed version of the efficient model, suitable for handheld laryngoscopy devices.

Support of deep learning to classify vocal fold images in flexible laryngoscopy

PurposeTo collect a dataset with adequate laryngoscopy images and identify the appearance of vocal folds and their lesions in flexible laryngoscopy images by objective deep learning models. MethodsWe adopted a number of novel deep learning models to train and classify 4549 flexible laryngoscopy images as no vocal fold, normal vocal folds, and abnormal vocal folds. This could help these models recognize vocal folds and their lesions within these images. Ultimately, we made a comparison between the results of the state-of-the-art deep learning models, and another comparison of the results between the computer-aided classification system and ENT doctors. ResultsThis study exhibited the performance of the deep learning models by evaluating laryngoscopy images collected from 876 patients. The efficiency of the Xception model was higher and steadier than almost the rest of the models. The accuracy of no vocal fold, normal vocal folds, and vocal fold abnormalities on this model were 98.90 %, 97.36 %, and 96.26 %, respectively. Compared to our ENT doctors, the Xception model produced better results than a junior doctor and was near an expert. ConclusionOur results show that current deep learning models can classify vocal fold images well and effectively assist physicians in vocal fold identification and classification of normal or abnormal vocal folds.

Simulation en dermatologie 1 : consultation d’annonce d’un mélanome

To collect a dataset with adequate laryngoscopy images and identify the appearance of vocal folds and their lesions in flexible laryngoscopy images by objective deep learning models.We adopted a number of novel deep learning models to train and classify 4549 flexible laryngoscopy images as no vocal fold, normal vocal folds, and abnormal vocal folds. This could help these models recognize vocal folds and their lesions within these images. Ultimately, we made a comparison between the results of the state-of-the-art deep learning models, and another comparison of the results between the computer-aided classification system and ENT doctors.This study exhibited the performance of the deep learning models by evaluating laryngoscopy images collected from 876 patients. The efficiency of the Xception model was higher and steadier than almost the rest of the models. The accuracy of no vocal fold, normal vocal folds, and vocal fold abnormalities on this model were 98.90 %, 97.36 %, and 96.26 %, respectively. Compared to our ENT doctors, the Xception model produced better results than a junior doctor and was near an expert.Our results show that current deep learning models can classify vocal fold images well and effectively assist physicians in vocal fold identification and classification of normal or abnormal vocal folds.

Pre- and Postoperative Ultrasound and MRI Examinations in Assessing Vocal Folds in Patients with Goiter.

Aim of the study: To determine the value of dynamic examinations ultrasound (US) and MRI in the 1.5T field in the assessment of the mobility of vocal folds (VF) in comparison to laryngoscopy in patients with thyroid gland resection. Materials and methods: A total of 44 patients with goiter, before and after thyroidectomy, were subjected to videolaryngoscopy and dynamic examinations of the vocal folds using ultrasound and the following MRI sequences: generic gradient echo (GRE) and true fast imaging with steady-state precession (TRUFI). The qualitative and quantitative data were analyzed, i.e., the angles of deviation from the midline of the vocal folds and the area of the right and left rima glottidis compartments. Results: The analysis of qualitative data showed that the results obtained by laryngoscopy, US and MRI are independent of the diagnostic method used in the group of patients pre and post thyroidectomy. Between the pre- and postoperative examinations in the group of paralyzed vocal folds, statistically significant differences were found in the minimum and maximum values of the angles for the MRI-GRE and MRI-TRUFI sequences and the maximum value of the angles in the US examination, but also in the maximum value of the area of the glottis compartments in both MRI-GRE and MRI-TRUFI dynamic sequences and the minimum value of the area in the sequence MRI-GRE. Statistically significant differences were found in both MRI sequences during phonation, both for the value of the angles and the area of the affected vocal folds. However, no statistically significant differences were found in the values of the angles or the areas in both vocal fold imaging methods without identified mobility abnormalities. Conclusions: Ultrasound and MRI examinations using dynamic sequences have a similar diagnostic value to laryngoscopy in the assessment of vocal fold paralysis in patients with goiter. The GRE sequence seems to be the most reliable one in determining vocal fold paralysis, and the most reliable parameter is the maximum area of the rima glottidis compartment. The inclusion of dynamic short sequences widely available in 1.5T scanners in standard neck examination protocols represents a novelty of the method and a promising diagnostic perspective in the diagnosis of vocal fold paralysis.

A Qualitative Comparison of Manual and K‐means Segmentation for Whole‐slide Histology Images of Rabbit Vocal Folds

IntroductionSurgical treatment for subglottic stenosis often faces recurrence of narrowing and patients continue to face quality of life issues. Currently, biomechanical models that inform surgical treatments are difficult to create due to vocal fold intricacies and trouble distinguishing between tissue types–muscle, mucosa, and cartilage. Improved models of laryngeal microanatomy can be used to assess and predict surgical outcomes for patients who suffer from subglottic stenosis. While manual segmentation of histology is the standard, automatic segmentation can be more efficient, sensitive, and specific in tissue differentiation. Manual segmentation faces challenges like extensive operator time and inter‐rater subjectivity. The aim of this study, therefore, was to qualitatively compare manual segmentation to automatic k‐means segmentation of whole‐slide histology rabbit vocal fold images. This is the preliminary step towards enhancing segmentation and, later, achieving a high‐fidelity human larynx model.Methods134 whole‐slide histology images were manually segmented from seven rabbits using colored masks for muscle, cartilage, mucosa, and adipose tissue. Of those images, 31 were also selected for segmentation using k‐means for initial qualitative comparison. At least two images were segmented per rabbit. K‐means segmentation is optimal for our multidimensional data. Since it is deterministic, we selected the number of clusters as our parameter. After inspecting n=1‐10 clusters, n=4 was chosen as qualitatively producing the most distinct clusters without oversampling.ResultsFor our preliminary results, we compared the histological image, the manual segmentation, and the automatically segmented image. In the automatic segmentations, tissue type is distinguished by the grayscale contrast. The four classes visible are cartilage, muscle, adipose tissue, and mucosa. There is occasional loss of visualization of adipose tissue and mucosa, however, the cartilage and muscle tissues remain distinct. The mucosal area boundaries remain clear despite some visualization loss within the mucosal borders, therefore, mucosa can be distinguished from adjacent cartilage and muscle. Training on a large dataset like other machine learning algorithms is unnecessary for k‐means, which is ideal for our relatively small sample size. The speed and versatility of k‐means has qualitatively demonstrated comparable results to our manual segmentations.ConclusionFuture directions for our project include conducting k‐means segmentation of the remainder of the 134 images collected. We have also tested mean‐shift clustering and a deep learning convolutional neural network (CNN) to compare automatic segmentation methods to one another, and to the standard manual segmentation/ground truth. Future testing also includes quantitative analysis of these algorithms’ accuracy. Beyond our histology images, we have at least 14 MR images (at least two per rabbit) of the larynges as well. We intend to combine and overlay the structural detail of the histology images with the soft tissue differentiation of the MRI.

In Vivo Automatic and Quantitative Measurement of Adult Human Larynx and Vocal Fold Images

Qualitative laryngoscopy belongs to a diagnostic routine. Nevertheless, quantitative morphometric measurements of laryngeal structures remain challenging. This study aimed to introduce a special laser projection device that can facilitate computer-assisted digitalized analysis and provide important quantitative information for diagnostics and treatment planning. The laryngeal images were captured with our device, which contained two parallel laser beams in order to provide the scaling reference. The maximum length of the vocal fold during respiration and vibration (phonation), vocal width at midpoint, total fold area, maximum cross-sectional area of the glottic space, and maximum vocal fold angle were determined and calculated. These parameters were analyzed and compared on the basis of age, sex, body height, body weight and body mass index. A total of 87 subjects were enrolled in this study, comprising 39 males and 48 females. The age range for all subjects was 21 to 80 years old. The maximum value of the glottic area and vocal angle showed no significant gender difference. Both the respiration and vibration vocal fold length was significantly longer in males than in females. The vocal width revealed no gender difference, but the fold area during both respiration and phonation was significantly larger in men than in women. As for the respiration-to-vibration ratio of the vocal length, there was a trend, but without statistical significance (P=0.06), toward a higher length compression ratio in men than in women. Meanwhile, age was found to have a strong relationship with vocal width during phonation. The width of vibration vocal fold decreased with aging significantly. Our innovative module can provide reference parameters, which makes it possible to directly estimate the objective absolute values of relevant laryngeal structures. Our non-invasive approach can be used during routine laryngoscopy and the findings easily documented. In future, we can extend its clinical application to measure subtle laryngeal or hypopharyngeal changes, which are difficult to objectively quantify.

Optical Biopsy: Automated Classification of Airway Endoscopic Findings Using a Convolutional Neural Network.

Create an autonomous computational system to classify endoscopy findings. Computational analysis of vocal fold images at an academic, tertiary-care laryngology practice. A series of normal and abnormal vocal fold images were obtained from the image database of an academic tertiary care laryngology practice. The benign images included normals, nodules, papilloma, polyps, and webs. A separate set of carcinoma and leukoplakia images comprised a single malignant-premalignant class. All images were classified with their existing labels. Images were randomly withheld from each class for testing. The remaining images were used to train and validate a neural network for classifying vocal fold lesions. Two classifiers were developed. A multiclass system classified the five categories of benign lesions. A separate analysis was performed using a binary classifier trained to distinguish malignant-premalignant from benign lesions. Precision ranged from 71.7% (polyps) to 89.7% (papilloma), and recall ranged from 70.0% (papilloma) to 88.0% (nodules) for the benign classifier. Overall accuracy for the benign classifier was 80.8%. The binary classifier correctly identified 92.0% of the malignant-premalignant lesions with an overall accuracy of 93.0%. Autonomous classification of endoscopic images with artificial intelligence technology is possible. Better network implementations and larger datasets will continue to improve classifier accuracy. A clinically useful optical cancer screening system may require a multimodality approach that incorporates nonvisual spectra. NA Laryngoscope, 132:S1-S8, 2022.

In Vivo Magnetic Resonance Imaging of the Rat Vocal Folds After Systemic Dehydration and Rehydration.

Objective Consuming less water (systemic dehydration) has long been thought to dehydrate the vocal folds. An in vivo, repeated measures study tested the assumption that systemic dehydration causes vocal fold dehydration. Proton density (PD)-weighted magnetic resonance imaging (MRI) of rat vocal folds was employed to investigate (a) whether varying magnitudes of systemic dehydration would dehydrate the vocal folds and (b) whether systemic rehydration would rehydrate the vocal folds. Method Male (n = 25) and female (n = 14) Sprague Dawley rats were imaged with 7T MRI, and normalized PD-weighted signal intensities were obtained at predehydration, following dehydration, and following rehydration. Animals were dehydrated to 1 of 3 levels by water withholding to induce body weight loss: mild (< 6% body weight loss), moderate (6%-10% body weight loss), and marked (> 10% body weight loss). Results There was a significant decrease in vocal fold signal intensities after moderate and marked dehydration (p < .0167). Rehydration increased the normalized signal intensity to predehydration levels for only the moderate group (p < .0167). Normalized signal intensity did not significantly change after mild dehydration or when the mildly dehydrated animals were rehydrated. Additionally, there were no significant differences in PD-weighted MRI normalized signal intensity between male and female rats (p > .05). Conclusion This study provides evidence supporting clinical voice recommendations for rehydration by increasing water intake after an acute, moderate systemic dehydration event. However, acute systemic dehydration of mild levels did not dehydrate the vocal folds as observed by PD-weighted MRI. Future programmatic research will focus on chronic, recurring systemic dehydration.

Imaging the Vocal Folds: A Feasibility Study on Strain Imaging and Elastography of Porcine Vocal Folds

Vocal folds are an essential part of human voice production. The biomechanical properties are a good indicator for pathological changes. In particular, as an oscillation system, changes in the biomechanical properties have an impact on the vibration behavior. Subsequently, those changes could lead to voice-related disturbances. However, no existing examination combines biomechanical properties and spatial imaging. Therefore, we propose an image registration-based approach, using ultrasound in order to gain this information synchronously. We used a quasi-static load to compress the tissue and measured the displacement by image registration. The strain distribution was directly calculated from the displacement field, whereas the elastic properties were estimated by a finite element model. In order to show the feasibility and reliability of the algorithm, we tested it on gelatin phantoms. Further, by examining ex vivo porcine vocal folds, we were able to show the practicability of the approach. We displayed the strain distribution in the tissue and the elastic properties of the vocal folds. The results were superimposed on the corresponding ultrasound images. The findings are promising and show the feasibility of the suggested approach. Possible applications are in improved diagnosis of voice disorders, by measuring the biomechanical properties of the vocal folds with ultrasound. The transducer will be placed on the vocal folds of the anesthetized patient, and the elastic properties will be measured. Further, the understanding of the vocal folds’ biomechanics and the voice forming process could benefit from it.

Computational analysis of six optical coherence tomography systems for vocal fold imaging: A comparison study.

There have been many advancements in laryngeal imaging using optical coherence tomography (OCT), with varying system design and probes for use in research, office, and operating room settings. We evaluated the performance of six distinct OCT systems in imaging porcine vocal folds (cords) using computational image processing and segmentation. Porcine vocal folds were scanned using six OCT systems. Imaging system and probe performance were quantitatively assessed for signal penetration, layer differentiation, and epithelium (EP) measurement. Fitted exponential decay curves with corresponding α constant and intensity thresholding segmentation were utilized to quantify the aforementioned parameters. The smallest average α constant and deepest signal penetration was of the SS-OCT 1700 nm 90 kHz microscope system (α = -1.74), followed by the SS-OCT 1310 nm 200 kHz VCSEL microscope system (α = -1.99), and SS-OCT 1310 nm 50 kHz rigid forward viewing endoscope system (α = -2.23). The EP was not readily visualized for three out of six systems, but was detected using automated segmentation. Average EP thickness (mean ± SD) was calculated as 55.79 ± 31.86 μm which agrees favorably with previous literature. Comparisons of OCT systems are challenging, as they encompass different probe design, optical path, and lasers, depending on application. Practical evaluation of different systems using computer based quantitative image processing and segmentation revealed basic, constructive information, such as EP measurements. To further validate the comparisons of system performance with clinical usability, in vivo human laryngeal imaging will be conducted. Further development of automated image processing and segmentation can be useful in rapid analysis of information. Lasers Surg. Med. 51:412-422, 2019. © 2019 Wiley Periodicals, Inc.

Vocal Fold Vibration Following Surgical Intervention in Three Vocal Pathologies: A Preliminary Study

High-speed videoendoscopy captures the cycle-to-cycle vibratory motion of each individual vocal fold in normal and severely disordered phonation. Therefore, it provides a direct method to examine the specific vibratory changes following vocal fold surgery. The purpose of this study was to examine the vocal fold vibratory pattern changes in the surgically treated pathologic vocal fold and the contralateral vocal fold in three vocal pathologies: vocal polyp (n = 3), paresis or paralysis (n = 3), and scar (n = 3). Digital kymography was used to extract high-speed kymographic vocal fold images at the mid-membranous region of the vocal fold. Spectral analysis was subsequently applied to the digital kymography to quantify the cycle-to-cycle movements of each vocal fold, expressed as a spectrum. Surgical modification resulted in significantly improved spectral power of the treated pathologic vocal fold. Furthermore, the contralateral vocal fold also presented with improved spectral power irrespective of vocal pathology. In comparison with normal vocal fold spectrum, postsurgical vocal fold vibrations continued to demonstrate decreased vibratory amplitude in both vocal folds.

Quantification of the vocal folds’ dynamic displacements

Fast dynamic data acquisition techniques are required to investigate the motional behavior of the vocal folds (VFs) when they are subjected to a steady air-flow through the trachea. High-speed digital holographic interferometry (DHI) is a non-invasive full-field-of-view technique that has proved its usefulness to study rapid and non-repetitive object movements. Hence it is an ideal technique used here to measure VF displacements and vibration patterns at 2000 fps. Analyses from a set of 200 displacement images showed that VFs’ vibration cycles are established along their width (y) and length (x). Furthermore, the maximum deformation for the right and left VFs’ area may be quantified from these images, which in itself represents an important result in the characterization of this structure. At a controlled air pressure, VF displacements fall within the range ~100–1740 nm, with a calculated precision and accuracy that yields a variation coefficient of 1.91%. High-speed acquisition of full-field images of VFs and their displacement quantification are on their own significant data in the study of their functional and physiological behavior since voice quality and production depend on how they vibrate, i.e. their displacement amplitude and frequency. Additionally, the use of high speed DHI avoids prolonged examinations and represents a significant scientific and technological alternative contribution in advancing the knowledge and working mechanisms of these tissues.

The First Application of the Two-Dimensional Scanning Videokymography in Excised Canine Larynx Model

Evaluation of the vibratory pattern of vocal folds is of paramount importance to diagnose vocal fold disorders. Currently, laryngeal videokymography (VKG) and digital kymography from high-speed videolaryngoscopy are the available techniques for studying aperiodic vibrations of vocal folds. But VKG has the fundamental limitation that only linear portion of the vocal fold mucosa can be visualized. Digital kymography has the disadvantages of no immediate feedback during examination, considerable waiting time before kymographic visualization, recoding duration limited to seconds, and extreme demands on storage space. We developed a new system--two-dimensional (2D) scanning VKG--for evaluation of the vibratory pattern of vocal folds, and the method provided a possible alternative with its advantages and disadvantages. Thus, we aimed to evaluate the feasibility of the new device for the vocal fold vibration in excised canine larynx model. The vibrating pattern for vocal folds was evaluated using high-speed videolaryngoscopic and 2D scanning videokymographic system in the excised canine larynx model. The images of canine vocal folds were captured with high-speed videolaryngoscopic system and converted to the kymographic images using the software. The kymographic image acquired by 2D scanning VKG was comparable with multi-line digital kymography at multiple locations. The vocal fold vibration could be evaluated in the excised canine larynx model using 2D scanning VKG. And this new device is expected to be a promising tool to evaluate the vocal fold vibration for clinical practice and voice research.

Vocal Fold Vibratory Changes Following Surgical Intervention

High-speed videoendoscopy (HSV) captures direct cycle-to-cycle visualization of vocal fold movement in real time. This ultrafast recording rate is capable of visualizing the vibratory motion of the vocal folds in severely disordered phonation and provides a direct method for examining vibratory changes after vocal fold surgery. The purpose of this study was to examine the vibratory motion before and after surgical intervention. HSV was captured from two subjects with identifiable midvocal fold benign lesions and six subjects with highly aperiodic vocal fold vibration before and after phonosurgery. Digital kymography (DKG) was used to extract high-speed kymographic vocal fold images sampled at the midmembranous, anterior 1/3, and posterior 1/3 region. Spectral analysis was subsequently applied to the DKG to quantify the cycle-to-cycle movements of the left and the right vocal fold, expressed as a spectrum. Before intervention, the vibratory spectrum consisted of decreased and flat-like spectral peaks with robust power asymmetry. After intervention, increases in spectral power and decreases in power symmetry were noted. Spectral power increases were most remarkable in the midmembranous region of the vocal fold. Surgical modification resulted in improved lateral excursion of the vocal folds, vibratory function, and perceptual measures of Voice Handicap Index-10. These changes in vibratory behavior trended toward normal vocal fold vibration.

Estimation of conductivity changes in a region of interest with electrical impedance tomography

This paper proposes a novel approach to reconstruct changesin a target conductivity from electrical impedance tomography measurements.As in the conventional difference imaging,the reconstruction of the conductivity change is based on electrical potential measurementsfrom the exterior boundary of the target before and after the change.In this paper, however, images of the conductivitybefore and after the change are reconstructed simultaneously based on the two data sets.The key feature of the approach is that the conductivity after the change is parameterized as a linear combination of the initial state and the change.This allows for modeling independently the spatial characteristics of the background conductivityand the change of the conductivity - by separate regularization functionals.The approach also allows in a straightforward way the restriction of the conductivity change to a localized region of interest inside the domain.While conventional difference imaging reconstruction is based on a global linearization of the observation model, the proposed approach amounts to solving a non-linear inverse problem. The feasibility of the proposed reconstruction method is tested experimentally and with a simulation which demonstrates a potential new medical application of electrical impedance tomography:imaging of vocal folds in voice loading studies.

Estimation of inferior-superior vocal fold kinematics from high-speed stereo endoscopic data in vivo.

Despite being an indispensable tool for both researchers and clinicians, traditional endoscopic imaging of the human vocal folds is limited in that it cannot capture their inferior-superior motion. A three-dimensional reconstruction technique using high-speed video imaging of the vocal folds in stereo is explored in an effort to estimate the inferior-superior motion of the medial-most edge of the vocal folds under normal muscle activation in vivo. Traditional stereo-matching algorithms from the field of computer vision are considered and modified to suit the specific challenges of the in vivo application. Inferior-superior motion of the medial vocal fold surface of three healthy speakers is reconstructed over one glottal cycle. The inferior-superior amplitude of the mucosal wave is found to be approximately 13 mm for normal modal voice, reducing to approximately 3 mm for strained falsetto voice, with uncertainty estimated at σ ≈ 2 mm and σ ≈ 1 mm, respectively. Sources of error, and their relative effects on the estimation of the inferior-superior motion, are considered and recommendations are made to improve the technique.

Automatic Segmentation of High Speed Video Images of Vocal Folds

An automatic method for segmenting glottis in high speed endoscopic video (HSV) images of vocal folds is proposed. The method is based on image histogram modeling. Three fundamental problems in automatic histogram based processing of HSV images, which are automatic localization of vocal folds, deformation of the intensity distribution by nonuniform illumination, and ambiguous segmentation when glottal gap is small, are addressed. The problems are solved by using novel masking, illumination, and reflectance modeling methods. The overall algorithm has three stages: masking, illumination modeling, and segmentation. Firstly, a mask is determined based on total variation norm for the region of interest in HSV images. Secondly, a planar illumination model is estimated from consecutive HSV images and reflectance image is obtained. Reflectance images of the masked HSV are used to form a vertical slice image whose reflectance distribution is modeled by a Gaussian mixture model (GMM). Finally, estimated GMM is used to isolate the glottis from the background. Results show that proposed method provides about 94% improvements with respect to manually segmented data in contrast to conventional method which uses Rayleigh intensity distribution in extracting the glottal areas.

Development of laryngeal video stroboscope with laser marking module for dynamic glottis measurement

Physicians clinically use laryngeal video stroboscope as an auxiliary instrument to test glottal diseases, and read vocal fold images and voice quality for diagnosis. As the position of vocal fold varies in each person, the proportion of the vocal fold size as presented in the vocal fold image is different, making it impossible to directly estimate relevant glottis physiological parameters, such as the length, area, perimeter, and opening angle of the glottis. Hence, this study designs an innovative laser projection marking module for the laryngeal video stroboscope to provide reference parameters for image scaling conversion. This innovative laser projection marking module to be installed on the laryngeal video stroboscope using laser beams to project onto the glottis plane, in order to provide reference parameters for scaling conversion of images of laryngeal video stroboscope.

New Concepts in Vocal Fold Imaging: A Review

To review new imaging technology potentially useful in the clinical practice of laryngology. Narrow band imaging, iScan (Pentax Medical Company, Montvale, NJ), optical computed tomography, and confocal microscopy have potential value for enhancing diagnosis of laryngeal pathology. Literature review. Literature search of computer databases including MEDLINE and PubMed. A review of 50 articles suggests that new imaging technologies may enhance clinical diagnostic capabilities. The probable value of new imaging technologies suggests that further research is needed to refine these technologies and define their clinical efficacy.