Artificial Intelligence and Benign Lesions of Vocal Folds Recognition

NCT05754606 | Unknown

• 1 other identifier: 4519
• Study Type: observational
• Target: 300
• Locations: 1 country
• Sites: 1

Brief Summary

The development of Artificial Intelligence (AI), the evolution of voice technology, progresses in audio signal analysis, and natural language processing/understanding methods have opened the way to numerous potential applications of voice, such as the identification of vocal biomarkers for diagnosis, classification or to enhance clinical practice. More recently, researches focused on the role of the audio signal of the voice as a signature of the pathogenic process. Dysphonia indicates that some negative changes have occurred in the voice production. The overall prevalence of dysphonia is approximately 1% even if the actual rates may be higher depending on the population studied and the definition of the specific voice disorder. Voice health may be assessed by several acoustic parameters. The relationship between voice pathology and acoustic voice features has been clinically established and confirmed both quantitatively and subjectively by speech experts. The automatic systems are designed to determine whether the sample belongs to a healthy subject or a non-healthy subject. The exactness of acoustic parameters is linked to the features used to estimate them for speech noise identification. Current voice searches are mostly restricted to basic questions even if with broad perspectives. The literature on vocal biomarkers of specific vocal fold diseases is anecdotal and related to functional vocal fold disorders or rare movement disorders of the larynx . The most common causes of dysphonia are the Benign Lesions of the Vocal Fold (BLVF). Currently, videolaryngostroboscopy, although invasive, is the gold standard for the diagnosis of BLVF. However, it is invasive and expensive procedure. The novel ML algorithms have recently improved the classification accuracy of selected features in target variables when compared to more conventional procedures thanks to the ability to combine and analyze large data-sets of voice features. Even if the majority of studies focus on the diagnosis of a disorder where they differentiate between healthy and non-healthy subjects, the investigators believe that the more important task is frequently differential diagnosis between two or more diseases. Even though this is a challenging task, it is of crucial importance to move decision support to this level. The main aim of this research would be the study, development, and validation of ML algorithms to recognize the different BVLVFL from digital voice recordings.

Conditions

Dysphonia

Outcome Measures

Primary Outcomes (1)

• validation of ML algorithms to recognize the different BVFL

  The statistical measures computed on the extracted features are the following: mean, standard deviation, skewness, kurtosis, 25th, 50th, and 75th percentiles. In addition, jitter, shimmer, and tilt of the power spectrum will be obtained from the whole unsegmented signal.

  five years

Interventions

Audio recordings DIAGNOSTIC_TEST

Automatica analysis of audio recordings

Eligibility Criteria

• Age: 18 Years - 65 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Patients affected by benign lesions of vocal fold

You may qualify if:

• Reinke's edema
• cyst of the vocal fold
• nodule of the vocal fold
• polyp of the vocal fold

You may not qualify if:

• previous laryngeal or thyroid surgery
• previous speech therapy
• current pulmonary diseases
• current gastroesophageal reflux
• laryngeal movement disorder or recurrent laryngeal nerve paralysis
• Non-native Italian speakers

Sponsors & Collaborators

• Fondazione Policlinico Universitario Agostino Gemelli IRCCS: lead

Study Sites (1)

Maria Raffaella Marchese

Roma, 00198, Italy

RECRUITING

MeSH Terms

Conditions

Dysphonia

Condition Hierarchy (Ancestors)

Voice Disorders; Laryngeal Diseases; Respiratory Tract Diseases; Otorhinolaryngologic Diseases; Neurologic Manifestations; Nervous System Diseases; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Maria Raffaella Marchese

CONTACT

3391144556; raffaellamarchese@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Medical Doctor, PhD

Study Record Dates

• First Submitted: February 1, 2023
• First Posted: March 6, 2023
• Study Start: November 1, 2021
• Primary Completion: November 1, 2025
• Study Completion: November 1, 2025
• Last Updated: March 6, 2023

Record last verified: 2023-02

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)