Clinical trial · Observational
AI in Histipathological Diagnosis of Bcc
Evaluation of Artificial Intelligence Algorithms Performance in the Histopathological Diagnosis of Basal Cell Carcinoma
NCT07786376CI-TRIAL-00123812AI\bccnot yet recruitingClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
The goal of this study is to evaluate the diagnostic performance of an Artificial Intelligence (AI) algorithm in the histopathological diagnosis of bcc compared to certified dermatopathologists
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Basal Cell Cancer | Skin Basal Cell Carcinoma | ALIAS | 0.90 |
Interventions
Interventions (0)
Data not yet available
No intervention recorded.
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- the diagnostic accuracy of the artificial intelligence algorithm, evaluated primarily by its sensitivity and specificity in correctly identifying basal cell carcinoma (BCC) from histopathological images.
- timeFrame
- one year
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: * Histopathological slides diagnosed as BCC. * Slides with adequate staining and preservation allowing clear visualization of dermatopathological features. Exclusion Criteria: * Slides with poor staining quality or significant artifacts interfering with histopathological interpretation. * Slides that were damaged, faded, or inadequately preserved. * Cases with uncertain or inconclusive original diagnoses. * Slides that could not be successfully digitized due to technical limitations ex very short or too long slides.
References
Publications (0)
Data not yet available
No reference posted for this study.