Artificial Intelligence (AI) in Diagnosis, Mycosis Fungoides of Skin (Diagnosis)
Conditions
Brief summary
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.
Detailed description
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.
Interventions
OTHERthis study does not include any intervention
Does not include intervention
Sponsors
Al-Azhar University
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* 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.
Exclusion criteria
* 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.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The accuracy of artificial intelligence in histopathological diagnosis of Mycosis fungoides will be evaluated by sensitivity and specificity | 1 year |
Countries
Egypt
Contacts
CONTACTShimaa Ali Ahmed, Resident of dermatology
PRINCIPAL_INVESTIGATORShimaa Ali Ahmed
Al Azhar university for boys
Outcome results
None listed