UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000044108 |
|---|---|
| Receipt number | R000050258 |
| Scientific Title | Creating an AI model for hoarseness classification using speech analysis in the perioperative period |
| Date of disclosure of the study information | 2021/05/24 |
| Last modified on | 2021/12/02 13:09:49 |
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Basic information
Public title
Creating an AI model for hoarseness classification using speech analysis in the perioperative period
Acronym
Creating an AI model for hoarseness classification using speech analysis in the perioperative period
Scientific Title
Creating an AI model for hoarseness classification using speech analysis in the perioperative period
Scientific Title:Acronym
Creating an AI model for hoarseness classification using speech analysis in the perioperative period
Region
| Japan |
Condition
Condition
Thyroid Surgery
Esophageal Cancer Surgery
Dissociative Aortic Aneurysm Surgery
Classification by specialty
| Surgery in general | Vascular surgery | Oto-rhino-laryngology |
| Anesthesiology | Intensive care medicine |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
With the recent development of artificial intelligence (AI) technology, speech analysis systems and machine learning have become an integral part of our lives. We hypothesized that by using speech analysis systems and machine learning, it would be possible to predict the diagnosis of antegrade nerve palsy in the perioperative period using the patient's voice. In this study, we aim to create a hoarseness classification AI model using a speech analysis system. If we can identify antegrade nerve palsy (hoarseness) by voice analysis, we can easily predict the diagnosis of antegrade nerve palsy without causing patient distress, and reduce complications in the perioperative period.
Basic objectives2
Safety,Efficacy
Basic objectives -Others
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
The purpose of this study is to create an AI model for hoarseness classification using a speech analysis system.
Key secondary outcomes
Base
Study type
Observational
Study design
Basic design
Randomization
Randomization unit
Blinding
Control
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
No. of arms
Purpose of intervention
Type of intervention
Interventions/Control_1
Interventions/Control_2
Interventions/Control_3
Interventions/Control_4
Interventions/Control_5
Interventions/Control_6
Interventions/Control_7
Interventions/Control_8
Interventions/Control_9
Interventions/Control_10
Eligibility
Age-lower limit
| 20 | years-old | <= |
Age-upper limit
| Not applicable |
Gender
Male and Female
Key inclusion criteria
Patients scheduled for esophageal cancer surgery, dissecting aortic aneurysm surgery, or thyroid surgery at Yamagata University Hospital.
Key exclusion criteria
Patients who were not able to cooperate in the study.
Target sample size
200
Research contact person
Name of lead principal investigator
| 1st name | Tatsuya |
| Middle name | |
| Last name | Hayasaka |
Organization
Yamagata University Hospital
Division name
Department of Anesthesia
Zip code
9909585
Address
2-2-2, Iida-Nishi, Yamagata City
TEL
023-628-5400
hayasakatatsuya1101@gmail.com
Public contact
Name of contact person
| 1st name | Tatsuya |
| Middle name | |
| Last name | Hayasaka |
Organization
Yamagata University Hospital
Division name
Department of Anesthesia
Zip code
9909585
Address
2-2-2, Iida-Nishi, Yamagata City
TEL
023-628-5400
Homepage URL
hayasakatatsuya1101@gmail.com
Sponsor or person
Institute
Department of Anesthesiology, Yamagata University School of Medicine
Institute
Department
Personal name
Funding Source
Organization
Department of Anesthesiology, Yamagata University School of Medicine
Organization
Division
Category of Funding Organization
Self funding
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Yamagata University Medical Ministry Council
Address
2-2-2 Iida-Nishi, Yamagata City, Yamagata Prefecture
Tel
0236285015
ikekenkyu@jm.kj.yamagata-u.ac.jp
Secondary IDs
Secondary IDs
NO
Study ID_1
Org. issuing International ID_1
Study ID_2
Org. issuing International ID_2
IND to MHLW
Institutions
Institutions
Other administrative information
Date of disclosure of the study information
| 2021 | Year | 05 | Month | 24 | Day |
Related information
URL releasing protocol
Publication of results
Unpublished
Result
URL related to results and publications
Number of participants that the trial has enrolled
Results
Results date posted
Results Delayed
Results Delay Reason
Date of the first journal publication of results
Baseline Characteristics
Participant flow
Adverse events
Outcome measures
Plan to share IPD
IPD sharing Plan description
Progress
Recruitment status
Open public recruiting
Date of protocol fixation
| 2021 | Year | 05 | Month | 24 | Day |
Date of IRB
| 2021 | Year | 05 | Month | 01 | Day |
Anticipated trial start date
| 2021 | Year | 05 | Month | 24 | Day |
Last follow-up date
| 2023 | Year | 05 | Month | 30 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
Patients who will undergo thyroid surgery, esophageal cancer surgery, or aortic aneurysm resection at Yamagata University Hospital between June 2021 and June 2023 will be included in the study. Before the surgery (from admission to the day before the surgery), the voice of the target patients (according to previous studies, "A-I-U-E-O", "the word 'Jack and the Beanstalk'", and a section of ATR503 sentence (about 2-3 minutes)) will be collected. After completion of the surgery, vocal fold movements will be recorded by laryngeal fiber, which is performed in normal practice (normal vocal fold movement and presence of antegrade nerve palsy will be the correct labels). After the next day of surgery, collect the voice as before the surgery. The voices of patients with a difference in voice are classified as positive, and those with no difference in voice are classified as negative. Using 80% of the total data as train data, an AI model is created based on the positive/negative data and laryngeal fiber findings. We used 20% of the total data as test data to draw ROC curve and calculate AUC.
As a secondary evaluation, we will use preoperative patient data (age, gender, height, weight, body temperature, heart rate, blood pressure, oxygenation capacity, etc.) and intraoperative findings such as surgical site to examine the correlation with recurrent nerve palsy by deep learning.
Management information
Registered date
| 2021 | Year | 05 | Month | 05 | Day |
Last modified on
| 2021 | Year | 12 | Month | 02 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000050258
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