A Multi-Reader Multi-Case (MRMC) Study for Assessing the Impact of Legit.Health Plus on the Clinical Assessment of Generalized Pustular Psoriasis and Other Skin Conditions by Healthcare Professionals.
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 15
- 试验地点
- 1
- 主要终点
- Diagnostic Accuracy for Generalized Pustular Psoriasis (GPP) with and without Artificial Intelligence Support.
研究概览
简要总结
This study aims to determine if an artificial intelligence (AI) medical device can help healthcare professionals more accurately diagnose rare and complex skin conditions. Dermatological issues are common in primary care, but there is often a gap in diagnostic accuracy between general practitioners and specialists, which can lead to treatment delays for serious conditions like Generalized Pustular Psoriasis (GPP) and Hidradenitis Suppurativa (HS).
The researchers hypothesized that the AI device would enhance the diagnostic accuracy of healthcare professionals for GPP and other dermatological conditions. To test this, the study followed a prospective observational design involving 15 practitioners, including both general practitioners and dermatologists.
During the study, participants were asked to evaluate 100 clinical images. For each case, they first provided a diagnosis based on the image and patient history alone. They were then shown the AI's analysis-which included the top five suggested diagnoses and confidence levels-and asked if they would like to adjust their initial assessment.
The primary question the study sought to answer was whether the information provided by the AI device could significantly increase the number of correct diagnoses made by these professionals, particularly for rare diseases that are often difficult to identify in a standard clinical setting
详细描述
This investigation is structured as a multi-reader multi-case (MRMC) study. A cohort of 15 healthcare professionals, including 11 primary care physicians and 4 dermatologists, acted as the "readers". These readers evaluated a "case" set of 100 clinical images to assess diagnostic performance both with and without the assistance of the AI device.
Study Design and Technical Methodology The research was conducted as a prospective observational and cross-sectional study. It utilized a "physician-as-their-own-control" design to measure the impact of Artificial Intelligence (AI) on diagnostic performance.
- Intervention Workflow: Participants accessed a dedicated web platform where they were presented with 100 clinical cases.
- Evaluation Steps: For each case, practitioners first evaluated a clinical image alongside anamnesis data (e.g., allergies, systemic symptoms) to provide an initial diagnosis.
- AI Support: Subsequently, they were presented with the AI's top 5 suggested diagnoses and associated confidence levels before making a final assessment.
- Image Sourcing: Cases consisted of high-quality images of Generalized Pustular Psoriasis (GPP), Hidradenitis Suppurativa (HS), and various differential "look-alike" conditions such as subcorneal pustular dermatosis and palmoplantar pustulosis.
- Data Sources: These images were curated from public dermatology atlases and internal research databases from the sponsor.
Quality Assurance and Data Management
To ensure the scientific integrity and reliability of the findings, several quality control measures were implemented:
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Prospective
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
- •Board-certified general practitioners and dermatologists, regardless of their professional experience.
- •Good quality images of patients with GPP.
- •Good quality images of patients with HS.
- •Good quality images of patients with pathologies that can be confused with GPP or HS, leading to a wrong diagnosis.
排除标准
- •Images of patients with pathologies different from GPP or HS that can be easily identified.
研究组 & 干预措施
Healthcare Professionals (Primary Care Physicians and Dermatologists)
This group is composed of board-certified healthcare professionals (HCPs) who serve as the "readers" in this multi-reader multi-case (MRMC) study. The cohort is uniquely characterized by its internal comparison: each participant acts as their own control.
- Dual Professional Roles: The group includes 11 primary care physicians (PCPs) and 4 dermatologists, allowing for a comparison between generalist and specialist diagnostic baseline performance.
- Interventional Exposure: All participants are evaluated under two distinct conditions: first, providing a diagnosis based solely on clinical images and patient history; second, providing a diagnosis assisted by the AI-based medical device's top 5 suggestions and confidence levels.
- Clinical Expertise: Every member of the cohort has a minimum of 5 years of clinical experience in their respective field.
干预措施: AI-based medical device for aided diagnosis in Dermatology (Device)
结局指标
主要结局
Diagnostic Accuracy for Generalized Pustular Psoriasis (GPP) with and without Artificial Intelligence Support.
时间窗: Day 1
This measure evaluates the Top-1 diagnostic accuracy of healthcare professionals (HCPs) when identifying GPP. Accuracy is calculated by comparing the clinician's diagnosis (both with and without the device's top 5 suggestions) against the confirmed reference diagnosis for each of the clinical cases.
Diagnostic Accuracy for different skin conditions with and without Artificial Intelligence Support
时间窗: Day 1
This measure evaluates the Top-1 diagnostic accuracy of healthcare professionals (HCPs) when identifying the corresponding skin condition. Accuracy is calculated by comparing the clinician's diagnosis (both with and without the device's top 5 suggestions) against the confirmed reference diagnosis for each of the clinical cases.
次要结局
- Diagnostic Accuracy for Rare Dermatological Conditions with and without Artificial Intelligence Support.(Day 1)