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

Study Overview
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Brief Summary
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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
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In the DNN group and control group, these steps are the same process. Routine exam and capture photographs of skin lesions for all eligible consecutive series patient. Make a clinical diagnosis (BEFORE-DX) Make a clinical diagnosis (AFTER-DX) 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.

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

Conditions
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Skin Diseases

Intervention / Treatment
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* Diagnostic Test: Model Dermatology (deep neural networks; Build 2020)


Participation Criteria
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Eligibility Criteria
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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
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: DNN group<br>using deep neural networks for skin lesion diagnosis | Diagnostic Test: Model Dermatology (deep neural networks; Build 2020)<br>* Physicians in the DNN group take pictures of the skin lesion and use the algorithm by uploading pictures.<br>|
| No Intervention: Control group<br>conventional diagnosis | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Top-1 diagnostic accuracy | frequency of correct Top-1 prediction | 6 consecutive months | 

Secondary Outcome Measures

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

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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skin diseases, deep neural networks