A Skin Image Reference Tool to Aid Healthcare Providers' Diagnosis

A Skin Image Reference Tool to Aid Healthcare Providers' Diagnosis of Commonly Encountered Dermatologic Diseases

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07033169

Enrollment

400

Registered

2025-06-24

Start date

2025-09-15

Completion date

2027-12-31

Last updated

2025-10-24

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

Conditions

Dermatologic Disease

Keywords

artificial intelligence, skin diseases, dermatology

Brief summary

Consented patients will have three images taken of their dermatologic conditions within the Belle.ai software. These images will be uploaded and saved within the Belle software system where a single AI-generated differential list will be generated based on the three photos. All photos uploaded will be de-identified. The software will not have any unique identifiers of participants saved in the system. The photos will be named based on participant enrollment numbers or unique code numbers and no unique identifiers will be attached to the photos. There will be no data collection form necessary for this study

Detailed description

Belle.ai provides a differential diagnosis from more than 2,000 different skin conditions leveraging a database trained on over 500,000 images. The image referencing technology deploys deep learning to analyze an uploaded clinical image and then matches its geometric pattern characteristics to Belle.ai's database of images to provide reference differentials. The purpose is to determine the validity of the Belle.ai software in diagnosing common dermatologic diseases across a range of skin tones. Consented patients will have three images taken of their dermatologic disease within the Belle.ai software. These images will be uploaded and saved within the Belle system where a single AI-generated differential list will be generated based on the three photos. The study coordinator will review uploaded patient cases and assign the cases for review and adjudication to designated Dermatologic Review Committee (DRC) members within the Belle web portal. Successful validation will require \>80% concordance between Belle.ai's primary working diagnosis (#1 on the differential) and our dermatology experts. A team of dermatology experts will then secondarily assess the concordance among the remaining diagnoses.

Interventions

None listed

Study design

Observational model

OTHER

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

10 Years to No maximum

Inclusion criteria

* Patient must present to an Advocate Health dermatology clinic * Patient must have the ability and willingness to provide informed consent and comply with study procedures and visits * Participant dermatologists must have access to the required technology (e.g., smartphone with internet access) and be capable of using it for the required image capture

Exclusion criteria

* Patients who are unable to comply with study procedures due to physical or mental health limitations (as assessed by study coordinator) * Pediatric, adolescent, and teen patients who present with dermatological conditions on their genitalia will not be included in the study (in support of patient privacy concerns).

Design outcomes

Primary

Measure	Time frame	Description
concordance of Belle.ai diagnoses with physician diagnoses.	Day 1	The study coordinator will review uploaded patient cases and assign them for review and adjudication to designated Dermatologic Review Committee (DRC) members within the Belle web portal. The DRC will be comprised of 1-2 Advocate Health board-certified dermatologists from each of the Winston, Charlotte, and Midwest dermatology practices.

Countries

United States

Contacts

Primary ContactIrma M Richardson, MHA

irichard@wakehealth.edu336-716-2903

Outcome results