Clinical Validation Study of a CAD System With Artificial Intelligence Algorithms for Early Noninvasive in Vivo Cutaneous Melanoma Detection
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 105
- 试验地点
- 1
- 主要终点
- Area Under the ROC Curve (AUC) for Melanoma Detection
研究概览
简要总结
The goal of this observational study is to learn if a computer-aided diagnosis (CAD) system can help identify skin cancer (cutaneous melanoma). The research focuses on adults who have skin spots that a doctor thinks might be cancerous. The main questions the study aims to answer are:
Can the artificial intelligence (AI) tool accurately identify melanoma in skin images?
How does the tool's accuracy compare to the clinical judgment of expert skin doctors (dermatologists)?
Researchers will compare the results from the AI tool to the final diagnosis made by doctors or through a skin biopsy. A biopsy is a medical test where a small piece of skin is removed and checked in a lab.
Participants will:
Have their skin spots photographed using a special camera attached to a smartphone.
Allow researchers to use their clinical data and biopsy results for the study.
The study does not change the medical care participants receive. Doctors will continue to treat participants as they normally would. By testing this tool, researchers hope to find a way to detect skin cancer earlier and more accurately
详细描述
This study is designed to clinically validate a computer-aided diagnosis (CAD) system that utilizes artificial intelligence (AI) and machine vision to assist in the detection of cutaneous melanoma in its early stages. Cutaneous melanoma is a form of skin cancer that is treatable when identified early; however, differentiating early melanoma from benign skin lesions during visual examination presents a challenge for healthcare professionals.
Study Design and Methodology The research is a prospective, observational, and cross-sectional study conducted at Hospital Universitario Cruces and Hospital Universitario Basurto in Spain. The protocol evaluates the diagnostic performance of an AI device using clinical images without interfering with routine patient care.
- Participant Selection: The study focuses on adults with skin lesions suspected of malignancy during regular clinical visits.
- Image Acquisition: Researchers capture photographs of skin lesions using a smartphone equipped with a specialized dermoscopic camera.
- Data Collection: Clinical and demographic data, such as age and sex, are collected alongside the digital images.
- Preprocessing: All images undergo a standardized preprocessing step where the lesion is cropped to minimize background noise for the algorithm.
- AI Analysis: The system processes the cropped images to generate a malignancy probability and a list of possible disease categories.
- Reference Standard: AI predictions are compared against a composite "Gold Standard." This standard is based on pathological anatomy results from a biopsy or, where a biopsy is not clinically indicated, the consensus diagnosis of expert dermatologists with extensive clinical experience.
Study Phases and Sample Size Plan
The investigation was planned in two phases to ensure a representative dataset:
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Cross Sectional
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 with skin lesions with suspected malignancy
- •Age over 18 years old
- •Patients who consent to participate in the study by signing the Informed Consent form
排除标准
- •Patients under 18 years of age
研究组 & 干预措施
Patients with suspected cutaneous malignancy
Group/Cohort Description The study group consists of adult patients (over 18 years old) who presented at the Dermatology Departments of Hospital Universitario Cruces and Hospital Universitario Basurto with skin lesions suspected of being malignant.
As this is an observational study, participants were not assigned to any new medical interventions, drugs, or treatments as part of the research protocol.
干预措施: AI-based Computer-Aided Diagnosis (CAD) Software for Skin Lesion Analysis. (Device)
结局指标
主要结局
Area Under the ROC Curve (AUC) for Melanoma Detection
时间窗: At the time of the single clinical visit (Baseline).
Measures the device's ability to distinguish between melanoma and non-melanoma cases using predicted probabilities.
Accuracy for Melanoma Detection
时间窗: At the time of the single clinical visit (Baseline)
Accuracy represents the percentage of all cases where the AI software's primary (top-ranked) prediction correctly matched the confirmed medical diagnosis. The "confirmed diagnosis" was determined by either a laboratory biopsy (the gold standard) or a consensus of expert dermatologists. To calculate this, the AI analyzed high-resolution dermoscopic images of skin lesions. The software succeeded if its highest-probability diagnosis category matched the actual disease category of the lesion. Only images meeting a minimum visual quality score (DIQA ≥ 5) were included in this analysis to ensure the results reflect performance in a professional clinical setting.
Sensitivity for Melanoma Detection
时间窗: At the time of the single clinical visit (Baseline).
The percentage of true positive melanoma cases correctly identified by the device.
Specificity for Melanoma Detection
时间窗: At the time of the single clinical visit (Baseline).
The percentage of true negative (benign) cases correctly identified by the device.
次要结局
- Top-1 Accuracy for Multiple ICD Categories(At the time of the single clinical visit (Baseline).)
- Top-3 Accuracy for Multiple ICD Categories(At the time of the single clinical visit (Baseline).)
- Top-5 Accuracy for Multiple ICD Categories(At the time of the single clinical visit (Baseline).)
- Area Under the ROC Curve (AUC) for Malignancy Detection(At the time of the single clinical visit (Baseline).)
- Sensitivity for Multiple Malignant Conditions Detection(At the time of the single clinical visit (Baseline).)
- Specificity for Multiple Malignant Conditions Detection(At the time of the single clinical visit (Baseline).)
- Predictive Values (PPV and NPV) for Malignancy(At the time of the single clinical visit (Baseline).)