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.
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. The findings of this study will help determine whether Artificial intelligence can serve as a diagnostic support tool for the histopathological diagnosis of mycosis fungoides.
Study Type
OBSERVATIONAL
Enrollment
50
Does not include intervention
Faculty of Medicine , Al Azhar university , Nasr city , Cairo , Egypt
Cairo, Egypt
The accuracy of artificial intelligence in histopathological diagnosis of Mycosis fungoides will be evaluated by sensitivity and specificity
Time frame: 1 year
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.