# Detection of Vocal Fold Paralysis in Dynamic Translaryngeal Ultrasound using a Deep-Learning Approach

Trung Kien Bui, Muriel Lefort, Agnès Rouxel, Juliette Dindart, Christophe Nioche, Hervé Guillemet, Christophe Trésallet, Frédérique Frouin

<figure><img src="image_1.png" /></figure>

## To cite this version:

Trung Kien Bui, Muriel Lefort, Agnès Rouxel, Juliette Dindart, Christophe Nioche, et al.. Detection of Vocal Fold Paralysis in Dynamic Translaryngeal Ultrasound using a Deep-Learning Approach. 2025 IEEE International Ultrasonics Symposium (IEEE IUS 2025), Sep 2025, Utrecht, Netherlands. hal-05279599

HAL Id: hal-05279599

https://hal.science/hal-05279599v1

Submitted on 23 Sep 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
# Detection of Vocal Fold Paralysis in Dynamic Translaryngeal Ultrasound
Using a Deep Learning Approach

Trung-Kien Bui

LITO Laboratory

Inserm, Institut Curie, Paris-Saclay University Sorbonne University, INSERM, CNRS

Orsay, France

https://orcid.org/0000-0002-1788-4960

Muriel Lefort

LIB Laboratory

Paris, France

muriel.lefort@inserm.fr

Agnès Rouxel

Department of Nuclear Medicine

Avicenne Hospital, AP-HP

Bobigny, France

https://orcid.org/0009-0003-2390-8732

Juliette Dindart

LITO Laboratory

Inserm, Institut Curie, Paris-Saclay University

Orsay, France

https://orcid.org/0000-0002-3111-1358

Christophe Nioche

LITO Laboratory

Inserm, Institut Curie

Orsay, France

https://orcid.org/0000-0002-6247-2025

Hervé Guillemet

Apteryx

Apteryx sarl

Gif-sur-Yvette, France

hg@apteryx.fr

Christophe Trésallet

Department of Digestive, Bariatric and Endocrine Surgery

Avicenne Hospital, AP-HP, University Sorbonne Paris-Nord

Bobigny, France

https://orcid.org/0000-0001-5406-8981

Frédérique Frouin

LITO Laboratory

Inserm, Institut Curie

Orsay, France

https://orcid.org/0000-0002-7288-1543

**Abstract**—This study introduces a new quantitative approach to assess vocal fold motion with dynamic Translaryngeal Ultrasound (dTU). Laryngeal cartilages have been annotated in dTU images during abduction or adduction phases, to train a YOLOv7-tiny model for detecting these cartilages. Motion features were extracted from object detection results and then used as inputs for a Support Vector Machine (SVM) classifier to diagnose vocal fold paralysis. A retrospective database was constructed consisting of 227 dTU scans, each with qualitative labels for normal and paralysed conditions determined by expert consensus. Experimental results showed that the YOLO model achieved a precision of 0.843 (resp. 0.644) and a recall of 0.827 (resp. 0.630) for the prominence of the thyroid (resp. arytenoid) cartilages on the test set of 46 scans. An univariate analysis of the motion features aligned with the expected physiological characteristics of healthy and paralysed vocal folds. Sensitivity and specificity values of SVM classifier with the YOLO-based method showed good results for the detection of true paralysis cases (sensitivity of 79%). The incorporation of additional labelled images is planned to further enhance classification accuracy.

**Index Terms**—vocal fold paralysis, cartilage detection, movement tracking, deep learning, dynamic translaryngeal ultrasound

## I. INTRODUCTION

Vocal fold paralysis (VFP) is a dysfunction of the vocal folds (VF), which are unable to completely close (adduction) or open (abduction) due to a lesion of the recurrent nerve, most often following a complex neck surgery [1,2]. Current guidelines recommend flexible laryngoscopy be performed

before and after neck surgery to assess VF mobility [3]. However, this minimally invasive procedure is not often proposed due to patient discomfort and practical concerns, resulting in delayed or missed diagnosis of VFP. To improve patient care, some research groups have recently introduced dynamic Translaryngeal Ultrasound (dTU) for evaluating VF movement and detecting paralysis [4–6]. This imaging technique is easy to perform at the patient’s bedside, capturing dynamic 2D images during free breathing. Although the true vocal cords are not always clearly visible on an ultrasound video, the prominence of the thyroid cartilage and the left and right arytenoid cartilages, which are attached to the vocal cords, are generally well-defined structures, as illustrated in Fig.1. Previously, these cartilages have been proposed as reliable

<figure><img src="image_3.png" /><figcaption>Fig. 1. Axial views of the laryngeal tract. Vocal folds (VF) are barely visible in the left image and completely absent in the right one. The prominence of the thyroid cartilage (TC), at the top of the an inverted V-shape marked by the asterisks, as well as the left and right arytenoid cartilages (AC) and false vocal cords (FC) are highlighted in both images.</figcaption></figure>

This work was funded by the French National Research Agency (ANR) with grant number ANR-22-CE19-0035 (VOCALISE).
anatomical landmarks to assess VF movement using dTU and promising results have been obtained for diagnosing VFP [7–9]. Quantifying VF movement involves measuring the displacement of anatomical landmarks between the maximum closed and open positions in a breathing cycle. To make these measurements less tedious, a deep learning model has been tested to automatically detect laryngeal structures in ultrasound images, with the ultimate goal of more accurately identifying VFP cases. In the field of computer vision, the YOLO (You Only Look Once) model [10] is a popular object detection algorithm. It relies on a single deep convolutional neural network to simultaneously predict the class and position of targets. This single-step detection model has outperformed many other object detectors in terms of speed and accuracy. Its promising performance makes it particularly well-suited for medical imaging applications.

To the best of our knowledge, this study is one of the first applications of a deep learning model to ultrasound images for VF tracking. In order to develop an automated workflow for VFP diagnosis, we first trained a YOLOv7-tiny model [11] for detecting laryngeal cartilages in each video frame. Next, motion features were derived from their locations and used as input data for a Support Vector Machine (SVM) classifier to identify VFP. A comparison was made between results based on manual annotation by an expert and those from automated detection, demonstrating the potential advantages of the YOLO-based approach.

## II. MATERIALS AND METHODS

### A. YOLOv7-tiny framework

Among the various models in the YOLOv7 series, YOLOv7-tiny is a lightweight version suitable for resource-limited environments, offering reliable detection accuracy [11]. This model offers an appropriate quantity of parameters for the size of this study database and the limited number of object classes to be detected. Fig.2 illustrate the YOLOv7-tiny architecture mainly consisting of three connected parts: the backbone network, the neck network and the detection head. The backbone extracts multi-scale features from the input image through convolutional and pooling layers. The neck network integrates these features to improve richness and spatial contexts. The detection head predicts object position and class using a bounding box and a confidence score, thanks to the non-maximum suppression (NMS) technique. The NMS is used to filter out repeated predictions for the same object.

### B. Experimental dataset

1) *Data acquisition and labelling:* A retrospective database was constructed consisting of 227 dTU videos from 190 subjects in three French medical centres. Videos were acquired post-surgically during follow-up examinations using various ultrasound devices (UltraSonix, SonoScape SSI, Toshiba). Each video, lasting between 10 and 30 seconds, was recorded while the subjects were freely breathing. These videos were qualitatively labelled as normal or VFP based on a consensus between two independent experts. In VFP cases, the VF

affected side (left or right) was also noted. This process resulted in 141 videos as normal and 86 videos as VFP.

2) *Annotation of structures of interest:* A trained operator selected one or more video segments representing either an abduction or an adduction phase. For each frame of the video segment, this expert carefully marked the central points of the thyroid cartilage prominence (TC) and both the left and right arytenoid cartilages (AC), using a dedicated annotation tool (VocaliseAnnotator, Apteryx). Next, a fixed-size bounding box (65 pixels wide and 50 pixels high) was defined around each landmark. This procedure resulted in a total of 18,401 annotated images from 453 selected video segments.

3) *Data partitioning:* An 8:2 ratio was used to split the data into two sets: a training set (153 subjects with 181 videos and 14,787 annotated images) and a test set (37 subjects with 46 videos and 3,614 annotated images), while also maintaining the same VFP ratios in both sets. All images of the same subject were assigned exclusively to either the training set or the test set, in order to avoid any overlap of subject-specific data.

### C. Training YOLO model and performance evaluation metrics

The object detection model was trained from scratch using a YOLOv7-tiny configuration. Training was repeated for 300 epochs on the training set with default hyperparameters. The detection performance of the model was evaluated in terms of precision and recall for TC and AC on the test set. The evaluation was performed on all 3,614 annotated images (global-level) and using a video-centric assessment (video-level), for which performance metrics were calculated for all annotated images in each video. These metrics were computed based on two elements: Intersection over Union (IoU) and object confidence score. IoU measures the overlap between predicted and ground-truth boxes, with higher values (close to 1) indicating better spatial accuracy. The object confidence score represents the model certainty of object presence and class probability in a detected box. By setting a threshold for IoU at = 0.45 and a confidence score at = 0.25, predictions were defined as true positives (correct predictions), false positives (incorrect predictions), or false negatives (missed predictions), which allows the performance of the model to be evaluated.

<figure><img src="image_4.png" /><figcaption>Fig. 2. YOLOv7-tiny overall structure with three main components: backbone, neck and detection head.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Vocal fold in « closed » (a) and « open » (b) positions</figcaption></figure>

<caption>Fig. 3. Illustration of areas formed by three cartilages in "closed" (a) and "open" (b) positions of the glottis. The median line (white dashes) define the areas of each hemilarynx.</caption>

### D. Computation of motion features

Based on the TC and AC locations obtained at two extreme glottic positions of a breathing phase, as illustrated in Fig.3, two types of motion indices were calculated: Symmetry Index (SI) and Mobility Fraction (MF), defined by (1) and (2):

$$ SI = \frac{\min(area_{right}, area_{left})}{\max(area_{right}, area_{left})} \quad (1) $$

$$ MF = \frac{area^{open} - area^{closed}}{area^{open}} \quad (2) $$

The SI measures the symmetry between the left and right halves of the larynx. The MF reflects the relative change in area between the maximum closed and open positions. Four quantitative features were then derived for subsequent analysis: SI in closed position (SI<sup>closed</sup>), SI in open position (SI<sup>open</sup>), MF of the left hemilarynx (MF<sub>left</sub>) and MF of the right hemilarynx (MF<sub>right</sub>). Two motion datasets were defined for training the classifier: one generated from manual labelling by the operator (DS<sub>op</sub>) and the other from YOLO predictions (DS<sub>YOLO</sub>). The distribution of the indices was analysed using the Mann-Whitney U test.

### E. Classification models

Two SVM classifiers were built to distinguish between normal and VFP cases, with one model trained on DS<sub>op</sub> and the other on DS<sub>YOLO</sub>. To handle the class imbalance of VFP cases in the database, the Synthetic Minority Oversampling Technique (SMOTE) was applied on the training set. A 5-fold cross-validation technique was employed to evaluate the performance of each SVM model during training. The mean and standard deviation of accuracy, balanced accuracy, sensitivity, and specificity were calculated across the folds. Each SVM model was then evaluated on the test set to determine its performance on unseen data.

## III. RESULTS

### A. Performance scores for object detection

The precision and recall of the YOLOv7-tiny model were calculated for detecting TC and AC classes on the test set (see

<table><caption>TABLE I
PERFORMANCE SCORES OF THE YOLOv7-tiny MODEL</caption><thead><tr><th rowspan="2">Class</th><th colspan="2">Global-level</th><th colspan="2">Video-level (n=46)</th></tr><tr><th>Precision</th><th>Recall</th><th>Precision (mean±std)</th><th>Recall (mean±std)</th></tr></thead><tbody><tr><td>TC</td><td>0.820</td><td>0.807</td><td>0.843±0.290</td><td>0.827±0.289</td></tr><tr><td>AC</td><td>0.618</td><td>0.594</td><td>0.644±0.315</td><td>0.630±0.318</td></tr></tbody></table>

<table><caption>TABLE II
PERFORMANCE OF SVM<sub>OP</sub> AND SVM<sub>Y</sub> CLASSIFIERS</caption><thead><tr><th colspan="5">Cross-validation performance</th></tr><tr><th>Model</th><th>Accuracy (%)</th><th>Balanced Accuracy (%)</th><th>Sensitivity (%)</th><th>Specificity (%)</th></tr></thead><tbody><tr><td>SVM<sub>op</sub></td><td>69.3±8.5</td><td>68.0±8.8</td><td>61.2±14.8</td><td>74.8±9.5</td></tr><tr><td>SVM<sub>Y</sub></td><td>68.2±5.8</td><td>68.5±7.4</td><td>71.1±18.3</td><td>65.9±10.7</td></tr></tbody></table><table><thead><tr><th colspan="5">Performance on the test set</th></tr><tr><th>Model</th><th>Accuracy (%)</th><th>Balanced Accuracy (%)</th><th>Sensitivity (%)</th><th>Specificity (%)</th></tr></thead><tbody><tr><td>SVM<sub>op</sub></td><td>68.6</td><td>66.4</td><td>55.9</td><td>76.9</td></tr><tr><td>SVM<sub>Y</sub></td><td>60.0</td><td>63.4</td><td>78.8</td><td>48.1</td></tr></tbody></table>

Table I). In both global- and video-level, the model achieved good results for TC detection, with precision and recall of over 80%, and encouraging results for AC detection, with both scores of around 60%. The high standard deviation values of precision and recall highlight a high variability of cartilage detection across videos.

### B. Motion features analysis

Fig.4 illustrates the four motion features (SI<sup>closed</sup>, SI<sup>open</sup>, MF<sub>left</sub> and MF<sub>right</sub>) for normal, left VFP, and right VFP groups that are computed from DS<sub>op</sub> and DS<sub>YOLO</sub>. For SI<sup>open</sup> and MF features, DS<sub>YOLO</sub> values are statistically lower than DS<sub>op</sub> values. For SI<sup>closed</sup> and SI<sup>open</sup> features, the normal category has median values that are statistically higher than the median values of pathological VFP categories. In addition, MF<sub>right</sub> (resp. MF<sub>left</sub>) median values are the lowest in the VFP<sub>right</sub> (resp. VFP<sub>left</sub>) categories.

### C. Classification performance of VFP

Table II summarises the classification performance of the model based on operator measurements (SVM<sub>op</sub>) and the model based on YOLO measurements (SVM<sub>Y</sub>), for identifying cases of paralysis. In cross-validation, SVM<sub>op</sub> and SVM<sub>Y</sub> show quite similar scores of accuracy (69.3% versus 68.2%), and of balanced accuracy (68.0% versus 68.5%). The YOLO-based classifier outperformed the operator-based one in sensitivity (71.1% versus 61.2%) but had lower specificity (65.9% versus 74.8%). On the test dataset, SVM<sub>op</sub> showed higher values of accuracy, balanced accuracy, and specificity, while SVM<sub>Y</sub> demonstrated higher sensitivity (78.8% versus 55.9%).

## IV. DISCUSSION

In this study, a YOLOv7-tiny model was trained on a custom dataset of dTU images to detect the prominence of thyroid car-
<figure><img src="image_6.png" /><figcaption>Fig. 4. The four motion features were calculated for normal and vocal fold paralysis groups from operator-based (left sides of the graphs) and YOLO-based (right sides of the graphs) datasets. Statistical comparisons were performed using the Mann-Whitney U test (* for p-value < 0.05; ** for p-value < 0.01).</figcaption></figure>

tilage and arytenoid cartilages. The motion features calculated from these automatic detections were used to predict cases of VFP using a SVM classifier. Comparison of this automated method with a classifier trained on features calculated from manual annotations showed its potential effectiveness for the diagnosis of VFP.

The performance of the detection model was evaluated globally and at the video level. In general, the TC class was detected more accurately than the AC class. It can be explained by the fact that the thyroid cartilage is a large, rigid structure that remains relatively static during breathing. This stability makes it easier to identify. In contrast, the arytenoid cartilages are small and highly mobile, and present similarities with other laryngeal structures, make them more challenging to detect. Moreover, video-level performance showed high standard deviations of performance scores. This variability may be due to the quality of the dTU acquisitions.

Four features based on symmetry and mobility were studied to distinguish between the healthy and the paralysed VF. Healthy VF show higher values of movement amplitude and symmetry. In VFP cases, consistently lower mobility values on the affected side confirmed the interest of these features. Regarding classification performance, the YOLO-based model had higher sensitivity on both trained and unseen data, which is crucial for diagnostic screening as it reduces the risk of false negatives and missed cases of paralysis. However, its low specificity remains a concern, leading to a high rate of false positives and the misclassification of healthy individuals.

The variability in the performance of the YOLO-based model highlights the need of a more robust object detection. Future steps include fine-tuning of the detection model and the use of multi-frame input to incorporate temporal context, as suggested in [12]. Last but not least, the addition of new dTU studies should improve the robustness of the model. In conclusion, this study offers new insights into the study of vocal fold dysfunction using ultrasound imaging.

REFERENCES

[1] M. Heikkinen, K. Makinen, E Penttila, M. Qvarnstrom, T. Kemppainen, et al., "Incidence, risk factors, and natural outcome of vocal fold paresis in 920 thyroid operations with routine pre- and postoperative laryngoscopic evaluation", World J Surg, vol. 43, pp. 2228-2234, 2019.

[2] H. W. Wang, C. C. Lu, P. Z. Chao, and F. P. Lee, "Causes of vocal fold paralysis", Ear, Nose & Throat Journal, vol. 101, pp. NP294-NP298, 2020.

[3] M. Steurer, C. Passler, D. M. Denk, B. Schneider, B. Niederle, et al., "Advantages of recurrent laryngeal nerve identification in thyroidectomy and parathyroidectomy and the importance of preoperative and postoperative laryngoscopic examination in more than 1000 nerves at risk", The Laryngoscope, vol. 112, pp. 124-133, 2002.

[4] M. Dedecjus, Z. Adamczewski, J. Brzeziński, and A. Lewiński, "Real-time, high-resolution ultrasonography of the vocal folds—a prospective pilot study in patients before and after thyroidectomy", Langenbecks Arch Surg, vol. 395, pp. 859-864, 2010.

[5] D. Carneiro-Pla, B. S. Miller, S. M. Wilhelm, M. Milas, P. G. Gauger, et al., "Feasibility of surgeon-performed transcutaneous vocal cord ultrasonography in identifying vocal cord mobility: A multi-institutional experience," Surgery, vol. 156, pp. 1597-1604, 2014.

[6] P. Knyazeva, V. Makarin, B. Seeliger, R. Chernikov, I. Sleptsov, et al., "Transcutaneous laryngeal ultrasonography for laryngeal immobility diagnosis in patients with voice visorders after thyroid/parathyroid surgery", World J Surg, vol. 42, pp. 2102-2108, 2018.

[7] D. S. Lazard, H. Bergeret-Cassagne, M. Lefort, L. Leenhardt, G. Russ, et al., "Transcutaneous laryngeal ultrasonography for laryngeal immobility diagnosis in patients with voice visorders after thyroid/parathyroid surgery", World J Surg, vol. 42, pp. 2102-2108, 2018.

[8] M. E. Cohen, M. Lefort, H. Bergeret-Cassagne, S. Hachi, A. Li, et al., "Detection of recurrent nerve paralysis: development of a computer aided diagnosis system", IRBM, vol. 36, pp. 367-374, 2015.

[9] H. Bergeret-Cassagne, D. S. Lazard, M. Lefort, S. Hachi, L. Leenhardt, et al., "Sonographic Dynamic Description of the Laryngeal Tract: Definition of Quantitative Measures to Characterize Vocal Fold Motion and Estimation of Their Normal Values: Sonographic Dynamic Description of the Laryngeal Tract", J Ultrasound Med, vol. 36, pp. 1037-1044, 2017.

[10] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: unified, real-time object detection", In 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, 2016.

[11] C. Y. Wang, A. Bochkovskiy, and H. Y. M. Liao, "YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors", In 2023 IEEE Conference on Computer Vision and Pattern Recognition, pp. 7464-7475, 2023.

[12] C. W. Corsel, M. van Lier, L. Kampmeijer, N. Boehler, and E. M. Bakker, "Exploiting Temporal Context for Tiny Object Detection", In 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, pp. 79-89, 2023.