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Research on analysis of pharynx images using machine learning for patients with fever and respiratory symptoms

Research on analysis of pharynx images using machine learning for patients with fever and respiratory symptoms - Research on analysis of pharynx images using machine learning for patients with fever and respiratory symptoms

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
JPRN
Registry ID
JPRN-UMIN000054472
Enrollment
400
Registered
2024-05-24
Start date
2024-05-24
Completion date
Unknown
Last updated
2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Pharyngitis, febrile diseases, Pharyngeal tumor

Interventions

None listed

Sponsors

Juntendo University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

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.

Exclusion criteria

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.

Design outcomes

Primary

MeasureTime frame
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.

Countries

Japan

Contacts

Public ContactHirotake Mori

Juntendo University Department of General Medicine

h.mori.oa@juntendo.ac.jp81-3-3813-3111

Outcome results

None listed

Source: JPRN (via WHO ICTRP) · Data processed: Jul 3, 2026