Evaluation of Artificial Intelligence Algorithms Performance in the Histopathological Diagnosis of Mycosis Fungoides
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 50
- 试验地点
- 1
- 主要终点
- The accuracy of artificial intelligence in histopathological diagnosis of Mycosis fungoides will be evaluated by sensitivity and specificity
研究概览
简要总结
The aim of this observational study is to evaluate the diagnostic performance of an AI algorithm in the histopathological diagnosis of MF compared to certified dermatopathologists.
详细描述
Mycosis fungoides (MF) is the most common form of primary cutaneous T-cell lymphoma. Its early histological features may overlap with benign inflammatory dermatoses, making diagnosis challenging.
This observational study aims to evaluate the diagnostic performance of HistoGPT in the histopathological diagnosis of MF compared with certified dermatopathologists.
H&E-stained skin biopsy slides will be digitized using a Leica Aperio GT450 whole-slide scanner at 40× magnification.
The resulting whole-slide images will be analyzed using HistoGPT, an AI-based histopathology platform.
The diagnostic performance of HistoGPT and certified dermatopathologists will be assessed and compared using appropriate diagnostic metrics, including accuracy, sensitivity, specificity, F1 score, and area under the ROC curve.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Slides will be included in the study if they meet the following criteria:
- •Histopathological slides diagnosed as MF.
- •Slides with adequate staining and preservation allowing clear visualization of histopathological features.
排除标准
- •Slides will be excluded if they meet any of the following criteria:
- •Slides with poor staining quality or significant artifacts interfering with histopathological interpretation.
- •Slides that were damaged, faded, or inadequately preserved.
- •Slides with uncertain or inconclusive original diagnoses.
- •Slides that could not be successfully digitized due to technical limitations ex very short or too long slides.
结局指标
主要结局
The accuracy of artificial intelligence in histopathological diagnosis of Mycosis fungoides will be evaluated by sensitivity and specificity
时间窗: 1 year
次要结局
未报告次要终点
研究者
Shimaa Ali Ahmed
Resident Dermatologist
Al-Azhar University