Development of three-dimensional laryngostroboscopy for office-based laryngeal diagnostics and phonosurgical therapy

Evaluation of Vertical Level Differences Between Left and Right Vocal Folds Using Artificial Intelligence System in Excised Canine Larynx

OBJECTIVES

This study aimed to establish an artificial intelligence (AI) system to classify vertical level differences between vocal folds during vocalization and to evaluate the accuracy of the classification.

METHODS

We designed models with different depths between the right and left vocal folds using an excised canine larynx. Video files for the data set were obtained using a high-speed camera system and a color complementary metal oxide semiconductor camera with global shutter. The data sets were divided into training, validation, and testing. We used 20,000 images for building the model and 8000 images for testing. To perform deep learning multiclass classification and to estimate the vertical level difference, we introduced DenseNet121-ConvLSTM.

RESULTS

The model was trained several times using different numbers of epochs. We achieved the most optimal results at 100 epochs, and the batch size used during training was 16. The proposed DenseNet121-ConvLSTM model achieved classification accuracies of 99.5% and 88.0% for training and testing, respectively. After verification using an external data set, the overall accuracy, precision, recall, and f1-score were 90.8%, 91.6%, 90.9%, and 91.2%, respectively.

CONCLUSIONS

The newly developed AI system may be an easy and accurate method for classifying superior and inferior vertical level differences between vocal folds. Thus, this AI system can be applied and may help in the assessment of vertical level differences in patients with unilateral vocal fold paralysis.

Harnessing Machine Learning in Vocal Arts Medicine: A Random Forest Application for "Fach" Classification in Opera

Vocal arts medicine provides care and prevention strategies for professional voice disorders in performing artists. The issue of correct "Fach" determination depending on the presence of a lyric or dramatic voice structure is of crucial importance for opera singers, as chronic overuse often leads to vocal fold damage. To avoid phonomicrosurgery or prevent a premature career end, our aim is to offer singers an improved, objective fach counseling using digital sound analyses and machine learning procedures. For this purpose, a large database of 2004 sound samples from professional opera singers was compiled. Building on this dataset, we employed a classic ensemble learning method, namely the Random Forest algorithm, to construct an efficient fach classifier. This model was trained to learn from features embedded within the sound samples, subsequently enabling voice classification as either lyric or dramatic. As a result, the developed system can decide with an accuracy of about 80% in most examined voice types whether a sound sample has a lyric or dramatic character. To advance diagnostic tools and health in vocal arts medicine and singing voice pedagogy, further machine learning methods will be applied to find the best and most efficient classification method based on artificial intelligence approaches.

A systematic review on efficacy, safety and cost-effectiveness of office-based laryngeal biopsy

OBJECTIVE

Laryngeal cancer is the second most prevalent head and neck malignancy in the USA. With recent advances in technology, this procedure is increasingly performed under local anaesthesia. This study aimed to identify the efficacy, safety and cost-effectiveness of laryngeal biopsy in out-patients by conducting a systematic review.

METHOD

A literature search was conducted using PubMed, Medline, Google Scholar and Embase over a 20-year period. Inclusion criteria were: studies performed on out-patient diagnostic biopsy procedures of the larynx. Exclusion criteria included all therapeutic procedures. The outcome measures were sensitivity and specificity, complication rate and cost-savings.

RESULTS

Thirty-five studies were included in the analysis. The sensitivity and specificity varied from 60 to 100 per cent with a low complication rate and cost savings.

CONCLUSION

Office-based laryngeal biopsies are increasingly used in the diagnosis of laryngeal cancers, resulting in earlier diagnosis and commencement of treatment. The barrier to undertaking this procedure is low sensitivity.

Three dimensional printed models of the airway for preoperative planning of open Laryngotracheal surgery in children: Surgeon's perception of utility

BACKGROUND

Preoperative planning of open laryngotracheal surgery is important for achieving good results. This study examines the surgeon's perception of the importance of using life size 3D printed models of the pediatric airway on surgical decision making.

METHODS

Life-size three-dimensional models of the upper airway were created based on CT images of children scheduled for laryngotracheal-reconstruction and cricotracheal resection with anastomosis. Five pediatric airway surgeons evaluated the three-dimensional models for determining the surgical approach, incision location and length, graft length, and need for single or double-stage surgery of seven children (median age 4.4 years, M:F ratio 4:3). They rated the importance of the three-dimensional model findings compared to the direct laryngoscopy videos and CT findings for each domain on a validated Likert scale of 1-5.

RESULTS

The mean rating for all domains was 3.6 ± 0.63 ("moderately important" to "very important"), and the median rating was 4 ("very important"). There was full agreement between raters for length of incision and length of graft. The between-rater agreement was 0.608 ("good") for surgical approach, 0.585 ("moderate") for incision location, and 0.429 ("moderate") for need for single- or two-stage surgery.

CONCLUSION

Patient-specific three-dimensional printed models of children's upper airways were scored by pediatric airway surgeons as being moderately to very important for preoperative planning of open laryngotracheal surgery. Large-scale, objective outcome studies are warranted to establish the reliability and efficiency of these models.

T1a Glottic Cancer: Advances in Vocal Outcome Assessment after Transoral CO2-Laser Microsurgery Using the VEM

Patients with unilateral vocal fold cancer (T1a) have a favorable prognosis. In addition to the oncological results of CO₂ transoral laser microsurgery (TOLMS), voice function is among the outcome measures. Previous early glottic cancer studies have reported voice function in patients grouped into combined T stages (Tis, T1, T2) and merged cordectomy types (lesser- vs. larger-extent cordectomies). Some authors have questioned the value of objective vocal parameters. Therefore, the purpose of this exploratory prospective study was to investigate TOLMS-associated oncological and vocal outcomes in 60 T1a patients, applying the ELS protocols for cordectomy classification and voice assessment. Pre- and postoperative voice function analysis included: Vocal Extent Measure (VEM), Dysphonia Severity Index (DSI), auditory-perceptual assessment (GRB), and 9-item Voice Handicap Index (VHI-9i). Altogether, 51 subjects (43 male, eight female, mean age 65 years) completed the study. The 5-year recurrence-free, overall, and disease-specific survival rates (Kaplan–Meier method) were 71.4%, 94.4%, and 100.0%. Voice function was preserved; the objective parameter VEM (64 ± 33 vs. 83 ± 31; mean ± SD) and subjective vocal measures (G: 1.9 ± 0.7 vs. 1.3 ± 0.7; VHI-9i: 18 ± 8 vs. 9 ± 9) even improved significantly (p < 0.001). The VEM best reflected self-perceived voice impairment. It represents a sensitive measure of voice function for quantification of vocal performance.