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

Effect of Using Deep Neural Networks on the Accuracy of Skin Disease Diagnosis in Non-Dermatologist Physician

Status

Terminated

Phases

Study type

Interventional

Source

ClinicalTrials.gov

Registry ID

NCT04636164

Enrollment

Registered

2020-11-19

Start date

2020-11-27

Completion date

2021-12-27

Last updated

2022-10-27

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

Conditions

Skin Diseases

Keywords

skin diseases, deep neural networks

Brief 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.

Detailed description

In the DNN group and control group, these steps are the same process. 1. Routine exam and capture photographs of skin lesions for all eligible consecutive series patient. 2. Make a clinical diagnosis (BEFORE-DX) 3. Make a clinical diagnosis (AFTER-DX) 4. consult to dermatologist In the DNN group, after making the BEFORE-DX, physicians use deep neural networks and make an AFTER-DX considering the results of the deep neural networks (Model Dermatology, build 2020). In the control group, after making the BEFORE-DX, physicians make an AFTER-DX after reviewing the pictures of skin lesions once more. Ground truth will be based on the biopsy if available, or the consensus diagnosis of the dermatologists. The investigators will compare the accuracy between the DNN group and control group after 6 consecutive months study.

Interventions

DIAGNOSTIC_TESTModel Dermatology (deep neural networks; Build 2020)

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

Study design

Allocation

RANDOMIZED

Intervention model

PARALLEL

Primary purpose

DIAGNOSTIC

Masking

NONE

Eligibility

Sex/Gender

ALL

Healthy volunteers

Inclusion criteria

* non-dermatologist physician (residents) who agree to participate in this study

Exclusion criteria

* dermatology residents * non-dermatology residents who use other deep neural networks for skin lesion diagnosis

Design outcomes

Primary

Measure	Time frame	Description
Top-1 diagnostic accuracy	6 consecutive months	frequency of correct Top-1 prediction

Secondary

Measure	Time frame	Description
Malignancy sensitivity	6 consecutive months	Positive rate of malignancy diagnosis
Top-2 and 3 diagnostic accuracy	6 consecutive months	frequency of correct Top-2 and 3 prediction
Infection sensitivity	6 consecutive months	positive rate of infection diagnosis

Countries

South Korea

Outcome results

None listed

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