Using Artificial Intelligence as a Diagnostic Decision Support Tool to Help the Diagnosis of Skin Disease in Primary Healthcare in Catalonia
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 100
- 试验地点
- 2
- 主要终点
- Sensitivity of the ML model
研究概览
简要总结
Background: Dermatological conditions are a relevant health problem. Machine learning models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, specially for skin cancer detection and classification.
Objective: The objective of this study is to perform a prospective validation of an image analysis ML model, which is capable of screening 44 different skin disease types, comparing its diagnostic capacity with that of General Practitioners (GPs) and dermatologists.
Methods: In this prospective study 100 consecutive patients who visit a participant GP with a skin problem in central Catalonia will be recruited, data collection is planned to last 7 months. Skin diseases anonymized pictures will be taken and introduced in the ML model interface, which will return top 5 accuracy diagnosis. The same image will be also sent as a teledermatology consultation, following the current workflow. GP, ML model and dermatologist/s assessments will be compared to calculate the precision, sensitivity, specificity and accuracy of the ML model.
详细描述
A secure anonymous stand alone web interface that is compatible to any mobile device will be integrated with the Autoderm API. The study conducted in this project will consist in a prospective study aimed to evaluate the ML model performance, comparing its diagnostic capacity with GPs and dermatologists.
To conduct the study the following procedure will be executed until the required number of samples is reached:
- A suitable patient with skin concern is asked to participate and sign the patient's study agreement.
- GP will diagnose the skin condition.
- GP (or nurse) will take one good quality image of the skin condition.
- GP will send the photograph as a teledermatology consultation following the current workflow.
- The image is entered in the Autoderm ML interface.
- Dermatologist will diagnose the skin condition.
The study will be conducted in primary care centers managed by the Catalan Health Institute. Participant PCP will be located in rural and metropolitan areas in Central Catalonia, which includes the regions of Anoia, Bages, Moianès, Berguedà and Osona. The reference population included in the study will be about 512,050. The recruitment of prospective subjects will consist on a consecutive basis.
General practitioners will collect data from consecutive patients who meet the inclusion criteria after obtaining written informed consent. Collected data will be reported exclusively in case report form (attached at Annex V and VI).
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients who have cutaneous disease reason-for-visit.
- •Patients who provide written informed consent.
- •Patients who are 18 years of age or older.
排除标准
- •Patients with advanced dementia.
- •Patients with a cutaneous lesion which can't be photographed with a smartphone and images with poor quality.
- •Patients who have conditions associated with risk of poor protocol compliance.
结局指标
主要结局
Sensitivity of the ML model
时间窗: 1 year
True positive rate of the ML model
Specificity of the ML model
时间窗: 1 year
True negative rate of the ML model
Area under the receiver operating characteristic curve of the ML model
时间窗: 1 year
Diagnostic ability of the ML model
Accuracy of the ML model
时间窗: 1 year
Ratio of number of correct predictions to the total number of input samples
次要结局
- Rate of the eligible participants who agree to participate in the study(1 year)