Clinical Development of Deep Learning for the OtoSight

Clinical Development and Evaluation of a Deep Learning Approach to Improve Diagnostic Accuracy

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT04722770

Enrollment

188

Registered

2021-01-25

Start date

2021-01-19

Completion date

2023-08-31

Last updated

2024-01-23

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

Conditions

Otitis Media Acute

Keywords

Ear Infection, Otitis Media, Otitis Media with Effusion, Machine Learning

Brief summary

The objective of this study is to clinically develop and evaluate a machine learning approach to improve the performance and data interpretation of the PhotoniCare OtoSight Middle Ear Scope in pediatric patients presenting at the primary care office for suspected ear infections. In this observational study, results of OtoSight imaging will not affect patient standard of care.

Interventions

DEVICEPhotoniCare OtoSight Middle Ear Scope

PhotoniCare OtoSight imaging using a near-infrared laser

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

6 Months to 4 Years

Healthy volunteers

Yes

Inclusion criteria

* Pediatric patients presenting for an office visit with ear-related complaints

Exclusion criteria

* Otoscopy contra-indicated or not possible per attending clinician's decision, Down's syndrome (due to abnormalities in ear canal anatomy)

Design outcomes

Primary

Measure	Time frame	Description
PhotoniCare OtoSight imaging exam	1-5 minutes; at first and only study visit	Optical coherence tomography imaging to evaluate the middle ear.

Secondary

Measure	Time frame	Description
Adverse events	15-30 minutes; at first and only study visit	Rate of adverse events

Countries

United States

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026