UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000054472 |
|---|---|
| Receipt number | R000062003 |
| Scientific Title | Research on analysis of pharynx images using machine learning for patients with fever and respiratory symptoms |
| Date of disclosure of the study information | 2024/05/24 |
| Last modified on | 2024/05/24 07:21:15 |
* This page includes information on clinical trials registered in UMIN clinical trial registed system.
* We don't aim to advertise certain products or treatments
Basic information
Public title
Research on analysis of pharynx images using machine learning for patients with fever and respiratory symptoms
Acronym
Research on analysis of pharynx images using machine learning for patients with fever and respiratory symptoms
Scientific Title
Research on analysis of pharynx images using machine learning for patients with fever and respiratory symptoms
Scientific Title:Acronym
Research on analysis of pharynx images using machine learning for patients with fever and respiratory symptoms
Region
| Japan |
Condition
Condition
Pharyngitis, febrile diseases, Pharyngeal tumor
Classification by specialty
| Medicine in general | Clinical immunology | Infectious disease |
| Pediatrics | Oto-rhino-laryngology |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
In recent years, diagnostic technology using machine learning has been established in various fields. It has become clear that the characteristics of follicular changes in the posterior pharyngeal wall (influenza follicles) and redness of the tonsils are useful for diagnosing influenza. Currently, the characteristics of the pharynx are observed in detail using a special camera, and mechanical It has become possible to analyze and diagnose using learning. Similar to influenza, analysis of pharynx images may lead to diagnosis of other viral diseases, bacterial infections, and non-infectious inflammatory diseases, but there is no sufficient prior research. In this study, we will use a special camera to photograph the pharynx findings of patients who come to the hospital complaining of fever and respiratory tract symptoms during routine medical treatment. The characteristics of swelling, redness, and lymphoid follicles in the pharyngeal arches, tonsils, and posterior pharyngeal wall were observed in detail from the obtained image data, and the characteristics of the pharyngeal images for each disease were determined using machine learning, including scoring and grouping. The pharyngeal images and data on observation items and test items collected in this study will be made available to medical professionals as pharyngeal image collections and pharyngeal image registry data for each disease, and many medical professionals will be able to observe pharyngeal images in the future. We aim to create a platform that can perform diagnosis.
Basic objectives2
Others
Basic objectives -Others
In this study, we will use a special camera to photograph the pharynx findings of patients who come to the hospital complaining of fever and respiratory tract symptoms during routine medical treatment. The characteristics of swelling, redness, and lymphoid follicles in the pharyngeal arches, tonsils, and posterior pharyngeal wall were observed in detail from the obtained image data, and the characteristics of the pharyngeal images for each disease were determined using machine learning, including scoring and grouping. The pharyngeal images and data on observation items and test items collected in this study will be made available to medical professionals as pharyngeal image collections and pharyngeal image registry data for each disease, and many medical professionals will be able to observe pharyngeal images in the future. We aim to create a platform that can perform diagnosis.
Trial characteristics_1
Exploratory
Trial characteristics_2
Others
Developmental phase
Not applicable
Assessment
Primary outcomes
From the image data obtained, we will make detailed observations of swelling, redness, and lymphoid follicles in the pharyngeal arches, tonsils, and posterior pharyngeal wall. In addition, machine learning will be used to perform exploratory analysis of the characteristics of pharynx images for each disease, including scoring and grouping based on information obtained from patients' usual medical treatment items.
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
| Not applicable |
Age-upper limit
| Not applicable |
Gender
Male and Female
Key inclusion criteria
Patients who visit the outpatient department or are hospitalized at the principal investigator's institution or joint research institution (excluding Iris Co., Ltd.) complaining of fever and respiratory symptoms and meet all of 1,2 and 3.
1. Those who are judged by the research director or co-researcher to be able to use an endoscopic telescope.
2. Those who are 0 years of age or older at the time of obtaining consent
3. Those who have received a sufficient explanation to participate in this research, and have obtained written consent of their own free will from the research subject or his/her legal representative after fully understanding the subject.
Key exclusion criteria
Those who fall under any of the following criteria will be excluded from the program.
1.Those who requested exclusion from the analysis of this study
2.Others who are judged by the research director or co-researcher to be unsuitable as research subjects.
Target sample size
400
Research contact person
Name of lead principal investigator
| 1st name | Hirotake |
| Middle name | |
| Last name | Mori |
Organization
Juntendo University
Division name
Department of General Medicine
Zip code
113-8421
Address
2-1-1 Hongo, Bunkyo, Tokyo, Japan
TEL
81-3-3813-3111
h.mori.oa@juntendo.ac.jp
Public contact
Name of contact person
| 1st name | Hirotake |
| Middle name | |
| Last name | Mori |
Organization
Juntendo University
Division name
Department of General Medicine
Zip code
113-8421
Address
2-1-1 Hongo, Bunkyo, Tokyo, Japan
TEL
81-3-3813-3111
Homepage URL
h.mori.oa@juntendo.ac.jp
Sponsor or person
Institute
Juntendo University
Institute
Department
Personal name
Funding Source
Organization
Juntendo University
Organization
Division
Category of Funding Organization
Other
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Juntendo University
Address
2-1-1, Hongo, Bunkyo, Tokyo, Japan
Tel
03-3813-3111
gakujutu@juntendo.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
| 2024 | 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
Preinitiation
Date of protocol fixation
| 2024 | Year | 05 | Month | 24 | Day |
Date of IRB
Anticipated trial start date
| 2024 | Year | 05 | Month | 24 | Day |
Last follow-up date
| 2025 | Year | 03 | Month | 31 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
Observation items and test items performed in the course of normal medical treatment
1. Imaging findings: Imaging of the posterior wall of the pharynx using Nodoca, adverse events that occurred during pharynx imaging, and information on malfunctions of the pharynx imaging equipment
2. Patient background: gender, age at time of consent, medical history, complications, vital signs
3. Confirmation of subjective symptoms and objective findings: Refer to the questionnaire at the time of visit.
4. Blood tests: Hematological tests (hemoglobin, white blood cell count, white blood cell differential, platelet count, blood sedimentation), blood biochemical tests (albumin, AST, ALT, total protein, LDH, creatinine, BUN, Na, K, Cl) , CRP)
5. Autoimmune related tests such as anti-nuclear antibodies, throat culture test, Gram staining, sputum mycobacterial test, acid-fast bacterium culture, tuberculosis PCR, non-tuberculous mycobacterial disease PCR, streptococcal antigen test, influenza antigen test, new type Coronavirus antigen test, PCR, adenovirus antigen test, RSV antigen test, mycoplasma antigen test, PCR, Legionella urinary antigen test, pneumococcal urinary antigen test, HIV antigen and antibody test, STS, TPHA, chlamydia PCR, EB virus Antibody titer, cytomegalovirus antibody titer, herpesvirus (simple, herpes zoster)
6. Contents of treatment and progress of treatment
7. Other tests performed during normal medical treatment, including pathological tests
8. Diagnosis determined by the medical doctor
Management information
Registered date
| 2024 | Year | 05 | Month | 24 | Day |
Last modified on
| 2024 | Year | 05 | Month | 24 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000062003
