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Deep Neural Networks on the Accuracy of Skin Disease Diagnosis in Non-Dermatologists

NCT04636164 · Status: TERMINATED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 55

Last updated 2022-10-27

No results posted yet for this study

Summary

Background: Deep neural networks (DNN) has been applied to many kinds of skin diseases in experimental settings.

Objective: The objective of this study is to confirm the augmentation of deep neural networks for the diagnosis of skin diseases in non-dermatologist physicians in a real-world setting.

Methods: A total of 40 non-dermatologist physicians in a single tertiary care hospital will be enrolled. They will be randomized to a DNN group and control group. By comparing two groups, the investigators will estimate the effect of using deep neural networks on the diagnosis of skin disease in terms of accuracy.

Conditions

  • Skin Diseases

Interventions

DIAGNOSTIC_TEST

Model Dermatology (deep neural networks; Build 2020)

Physicians in the DNN group take pictures of the skin lesion and use the algorithm by uploading pictures.

Sponsors & Collaborators

  • Pyoeng Gyun Choe

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Model
PARALLEL

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-11-27
Primary Completion
2021-11-27
Completion
2021-12-27

Countries

  • South Korea

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04636164 on ClinicalTrials.gov