Sulcus Vocalis and Benign Vocal Cord Lesions: Is There Any Relationship?

Saraniti, Carmelo; Patti, Gaetano; Verro, Barbara

doi:10.3390/ijerph20095654

IJERPH

2023

DOI: 10.3390/ijerph20095654

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Sulcus Vocalis and Benign Vocal Cord Lesions: Is There Any Relationship?

Carmelo Saraniti

Gaetano Patti

Barbara Verro

Abstract: Background: Sulcus vocalis (SV) is a longitudinal groove in the free edge of the true vocal cord. It may impair phonation with incomplete glottic closure, phonasthenia and hoarseness. This study aims to detect a correlation between benign vocal cord lesions and the incidence of the SV. Methods: A retrospective study was carried out on patients who underwent transoral surgery due to benign vocal fold lesions and were selected according to strict criteria. Patients were divided into a group with sulcus vocalis (… Show more

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2024

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Cited by 3 publications

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Revisiting the etiology and clinical characteristics of hemorrhagic polyps of the vocal fold

Santa Maria,

Rubin,

Vemula

et al. 2024

Laryngoscope Investig Oto

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ObjectiveBenign phonotraumatic lesions of the vocal folds (BVFLs) are typically seen in younger female patients with high vocal loads. Hemorrhagic polyps (HPs) do not fit the classic paradigm of most BVFLs, as they tend to occur in an older population, have a male predominance, and report to result from a vocal accident. We present one of the largest cohorts of HPs, to reexamine their etiology and clinical features.MethodsRetrospective cohort study, inclusive of all patients with HP managed by the senior authors between the years 2016 through 2023. Demographic data, management, phonotraumatic risk factors, pre‐ and post‐treatment VHI‐10 were reviewed. We examined patient videostroboscopy, categorized the size of the lesion, and identified any concurrent mucosal abnormality.ResultsOne hundred and eleven patients had confirmed HP, 84 males (75.7%). Thirty‐five patients were size category 1; pinpoint (28.9%), 57 were category 2; less than 1/3rd the vocal fold (45.5%), and 26 were category 3; greater than 1/3rd the vocal fold (21.5%). Ten patients (9%) had bilateral HPs. Thirty‐five patients had an additional 40 mucosal lesions in addition to the HP(s). The onset of symptoms was gradual in 60% of patients. The mean pretreatment VHI‐10 was 18.0 (SD 10.7), compared to 6.0 (SD 10.5) post‐treatment, (p < .001). 57/111 patients reported high voice demand professions or recreational activities. The average self‐reported talkative scale score was 7.6/10. Patients were managed with operative microdirect laryngoscopy and microflap excision (53.1%), in‐office clinic potassium titanyl phosphate (KTP) laser (24.3%), voice therapy alone (7.2%), and KTP in the operating room (6.3%).ConclusionsIn our cohort, most patients were male, had high vocal demands, reported gradual symptom onset, and almost a third of patients had additional BVFLs.Level of evidenceLevel 3: Retrospective cohort study.

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Revisiting the etiology and clinical characteristics of hemorrhagic polyps of the vocal fold

Santa Maria,

Rubin,

Vemula

et al. 2024

Laryngoscope Investig Oto

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

Kavak,

Gündüz,

Vural

et al. 2024

Eur Arch Otorhinolaryngol

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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.

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A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method

Nobel,

Swapno,

Islam

et al. 2024

Sci Rep

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In the healthcare domain, the essential task is to understand and classify diseases affecting the vocal folds (VFs). The accurate identification of VF disease is the key issue in this domain. Integrating VF segmentation and disease classification into a single system is challenging but important for precise diagnostics. Our study addresses this challenge by combining VF illness categorization and VF segmentation into a single integrated system. We utilized two effective ensemble machine learning methods: ensemble EfficientNetV2L-LGBM and ensemble UNet-BiGRU. We utilized the EfficientNetV2L-LGBM model for classification, achieving a training accuracy of 98.88%, validation accuracy of 97.73%, and test accuracy of 97.88%. These exceptional outcomes highlight the system’s ability to classify different VF illnesses precisely. In addition, we utilized the UNet-BiGRU model for segmentation, which attained a training accuracy of 92.55%, a validation accuracy of 89.87%, and a significant test accuracy of 91.47%. In the segmentation task, we examined some methods to improve our ability to divide data into segments, resulting in a testing accuracy score of 91.99% and an Intersection over Union (IOU) of 87.46%. These measures demonstrate skill of the model in accurately defining and separating VF. Our system’s classification and segmentation results confirm its capacity to effectively identify and segment VF disorders, representing a significant advancement in enhancing diagnostic accuracy and healthcare in this specialized field. This study emphasizes the potential of machine learning to transform the medical field’s capacity to categorize VF and segment VF, providing clinicians with a vital instrument to mitigate the profound impact of the condition. Implementing this innovative approach is expected to enhance medical procedures and provide a sense of optimism to those globally affected by VF disease.

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Sulcus Vocalis and Benign Vocal Cord Lesions: Is There Any Relationship?

Cited by 3 publications

References 43 publications

Revisiting the etiology and clinical characteristics of hemorrhagic polyps of the vocal fold

Revisiting the etiology and clinical characteristics of hemorrhagic polyps of the vocal fold

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

A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method

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