Effect of Using Deep Neural Networks on the Accuracy of Skin Disease Diagnosis in Non-Dermatologist Physician
试验速览
- 阶段
- 不适用
- 状态
- 终止
- 发起方
- 入组人数
- 55
- 试验地点
- 1
- 主要终点
- Top-1 diagnostic accuracy
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •non-dermatologist physician (residents) who agree to participate in this study
排除标准
- •dermatology residents
- •non-dermatology residents who use other deep neural networks for skin lesion diagnosis
研究组 & 干预措施
DNN group
using deep neural networks for skin lesion diagnosis
干预措施: Model Dermatology (deep neural networks; Build 2020) (Diagnostic Test)
Control group
conventional diagnosis
结局指标
主要结局
Top-1 diagnostic accuracy
时间窗: 6 consecutive months
frequency of correct Top-1 prediction
次要结局
- Top-2 and 3 diagnostic accuracy(6 consecutive months)
- Infection sensitivity(6 consecutive months)
- Malignancy sensitivity(6 consecutive months)
研究者
Pyoeng Gyun Choe
Clinical Professor
Seoul National University Hospital