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

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.

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.

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