DysphoniaNCT05754606
Artificial Intelligence and Benign Lesions of Vocal Folds Recognition
An observational study of Dysphonia, run by Fondazione Policlinico Universitario Agostino Gemelli IRCCS at 1 site in Italy.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 to 65 years
- Conditions the study is for
- Dysphonia
- What is being tested?
- Audio recordings
- How many people
- 300 participants
- Where
- 1 siteItaly
- Study dates
- 1 Nov 2021to 1 Nov 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 65 years
- All sexes
- Adult, Older adult
Study population: Patients affected by benign lesions of vocal fold
Who may be able to join
- Reinke's edema
- cyst of the vocal fold
- nodule of the vocal fold
- polyp of the vocal fold
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
Italy1 site, 1 recruiting
- Recruiting
Maria Raffaella Marchese
Roma, 00198
Maria Raffaella Marchese, 3391144556, raffaellamarchese@gmail.com
About this study
Official title: Artificial Intelligence for the Recognition of Benign Lesions of Vocal Folds From Audio Recordings
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.
Read the detailed description
The investigators will collect the audio recordings of dysphonic participants affected by BLVF. All voice samples will be divided into the following groups based on the endoscopic diagnosis: vocal fold cysts, Reinke's edema, nodules and polyps. The audio tracks will be obtained by asking to pronounce with usual voice intensity, pitch and quality the word /aiuole/ three times in a row. Voices will be acquired using a Shure model SM48 microphone (Evanston IL) positioned at an angle of 45° at a distance of 20 cm from the patient's mouth. The microphone saturation input will be fixed at 6/9 of CH1 and the environmental noise was <30 dB sound pressure level (SPL). The signals will be recorded in ".nvi" format with a high-definition audio-recorder Computerized Speech Lab, model 4300B, from Kay Elemetrics (Lincoln Park, NJ, USA) with a sampling rate of 50 kHz frequency and converted to ".wav" format. Each audio file will be anonymously labelled with gender and type of BLVF.
Analysis pipeline All the following analyses will be performed using MatLab R2019b, the MathWorks, Natick MA, USA. The analysis pipeline included signal pre-processing, features extraction, screening of the features, and model implementation.
Features extraction On the segmented signal, 66 different features in the time, frequency, and cepstral domain will be extracted. Then, seven statistical measures will be computed on the extracted features, namely: 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.
Features screening Features screening will be applied using biostatistical analyses on the whole dataset, to reduce the extended number of features to give as input to the classifier. Two statistical tests will be used to screen relevant features for the classification task: the one-way analysis of variance (ANOVA), when all the groups were normally distributed, and the Kruskal-Wallis test, otherwise. The groups' normality will be verified through the Kolmogorov-Smirnov test. For all the tests, a p-value <0.05 will be considered statistically significant.
- Model implementation A non-linear Support Vector Machine (SVM) with a Gaussian kernel is the algorithm chosen for this research. The classification performance will be measured through the accuracy and the average F1-score. Both metrics will be provided for the description of the overall classification performances and those obtained on gender sub-groups.
- Conditions
- Dysphonia
- Being tested
- Audio recordings
- Medical subject headings
- Dysphonia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Audio recordingsDiagnostic test
Automatica analysis of audio recordings
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: five years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Mar 2023
- Study start1 Nov 2021
- Primary completion1 Nov 2025 (estimated)
- Study completion1 Nov 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for DysphoniaStudies of the same condition, open and under way.
- More trials run by Fondazione Policlinico Universitario Agostino Gemelli IRCCSThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Marchese Maria Raffaella, Fondazione Policlinico Universitario Agostino Gemelli IRCCS
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05754606
- Sponsor's protocol ID
- 4519
- Status last verified
- Feb 2023
- First submitted
- 1 Feb 2023
- Last updated
- 6 Mar 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 6 Mar 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence for the Recognition of Benign Lesions of Vocal Folds From Audio Recordings
- ClinicalTrials.gov ID
- NCT05754606
- Acronym / sponsor ID
- 4519
- Conditions
- Dysphonia
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Fondazione Policlinico Universitario Agostino Gemelli IRCCSUniversity, hospital or other
- Study start
- 1 Nov 2021 (actual)
- Primary completion
- 1 Nov 2025 (estimated)
- Study completion
- 1 Nov 2025 (estimated)
- First posted
- 6 Mar 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
The investigators will collect the audio recordings of dysphonic participants affected by BLVF. All voice samples will be divided into the following groups based on the endoscopic diagnosis: vocal fold cysts, Reinke's edema, nodules and polyps. The audio tracks will be obtained by asking to pronounce with usual voice intensity, pitch and quality the word /aiuole/ three times in a row. Voices will be acquired using a Shure model SM48 microphone (Evanston IL) positioned at an angle of 45° at a distance of 20 cm from the patient's mouth. The microphone saturation input will be fixed at 6/9 of CH1 and the environmental noise was <30 dB sound pressure level (SPL). The signals will be recorded in ".nvi" format with a high-definition audio-recorder Computerized Speech Lab, model 4300B, from Kay Elemetrics (Lincoln Park, NJ, USA) with a sampling rate of 50 kHz frequency and converted to ".wav" format. Each audio file will be anonymously labelled with gender and type of BLVF.
Analysis pipeline All the following analyses will be performed using MatLab R2019b, the MathWorks, Natick MA, USA. The analysis pipeline included signal pre-processing, features extraction, screening of the features, and model implementation.
Features extraction On the segmented signal, 66 different features in the time, frequency, and cepstral domain will be extracted. Then, seven statistical measures will be computed on the extracted features, namely: 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.
Features screening Features screening will be applied using biostatistical analyses on the whole dataset, to reduce the extended number of features to give as input to the classifier. Two statistical tests will be used to screen relevant features for the classification task: the one-way analysis of variance (ANOVA), when all the groups were normally distributed, and the Kruskal-Wallis test, otherwise. The groups' normality will be verified through the Kolmogorov-Smirnov test. For all the tests, a p-value <0.05 will be considered statistically significant.
- Model implementation A non-linear Support Vector Machine (SVM) with a Gaussian kernel is the algorithm chosen for this research. The classification performance will be measured through the accuracy and the average F1-score. Both metrics will be provided for the description of the overall classification performances and those obtained on gender sub-groups.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Patients affected by benign lesions of vocal fold
Key inclusion criteria (4 listed)
- Reinke's edema
- cyst of the vocal fold
- nodule of the vocal fold
- polyp of the vocal fold
Key exclusion criteria (6 listed)
- previous laryngeal or thyroid surgery
- previous speech therapy
- current pulmonary diseases
- current gastroesophageal reflux
- laryngeal movement disorder or recurrent laryngeal nerve paralysis
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Audio recordingsAutomatica analysis of audio recordings | Diagnostic test | – |
Primary outcome
validation of ML algorithms to recognize the different BVFL
Time frame: five years
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Mar 2023.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Dysphonia, run by Fondazione Policlinico Universitario Agostino Gemelli IRCCS at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 6 Mar 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Maria Raffaella Marchese (3391144556, raffaellamarchese@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
validation of ML algorithms to recognize the different BVFL (time frame: five years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Mar 2023.