Clinical trial · Observational
CNN-Based AI Versus Physicians for Solitary Skin Lesion Diagnosis
Comparison of a CNN-Based Artificial Intelligence Model With Dermatologists and Non-Dermatologist Physicians in the Diagnosis of Solitary Skin Lesions
- 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 observational study is to evaluate the diagnostic accuracy of a CNN-based artificial intelligence model in patients with solitary skin lesions. The main questions it aims to answer are: * What is the diagnostic performance (sensitivity and specificity) of the CNN-based model in identifying solitary skin lesions using macroscopic clinical images? * How does the diagnostic accuracy of the CNN-based model compare with the evaluations performed by dermatologists and non-dermatologist physicians? Researchers will compare the AI model's diagnostic outputs to the independent evaluations of dermatologists and non-dermatologist physicians to see if the AI model can achieve a diagnostic performance comparable to or better than human clinicians. Participants (physicians acting as clinical readers) will: * Independently review a predefined set of anonymized macroscopic clinical images sourced from a retrospective patient archive. * Provide a primary diagnosis for each lesion based solely on the images, without access to patient history or histopathological results. * Submit their assessments to be compared against the gold standard (histopathological diagnosis) and the AI model's results.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Skin Neoplasm Malignant | Malignant Skin Neoplasm | ALIAS | 0.90 |
| Skin Neoplasms | Skin Neoplasm | ONTOLOGY_EXACT | 0.98 |
| Solitary Skin Lesions | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (1)
- label
- Development and Validation Cohort
- description
- "This is a single-arm retrospective study consisting of 17,625 archived clinical records with confirmed histopathological diagnoses. The cohort will serve as the primary dataset for AI model development. A specific subset of the test dataset will be independently evaluated by a panel of dermatologists and non-dermatologist physicians through a multiple-choice diagnostic task. The AI model's performance will be compared against both the gold-standard histopathological results and the diagnostic accuracy of the human observers."
Primary outcomes (1)
- measure
- Diagnostic accuracy of the CNN-based artificial intelligence model
- timeFrame
- Baseline (Retrospective data analysis will be completed within 4 months)
- description
- The diagnostic accuracy of the convolutional neural network (CNN)-based artificial intelligence model in the diagnosis of solitary skin lesions will be evaluated using accuracy and area under the receiver operating characteristic curve (ROC-AUC) values based on macroscopic clinical images.
Secondary outcomes (4)
- measure
- Difference in diagnostic performance between the CNN-based model and dermatologists
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: * Patients who have provided informed consent for the use of their clinical images in scientific research. * Clinical images with a resolution exceeding 224x224 pixels, ensuring compatibility with the artificial intelligence architecture. * Retrospective records of solitary skin lesions with confirmed diagnoses. Exclusion Criteria: * Patients who have not consented to the use of their clinical photographs for research purposes. * Images containing potentially identifiable personal information or visual features that compromise patient anonymity. * Images with a resolution lower than 224x224 pixels or poor diagnostic quality (e.g., blurring, significant occlusion). * Duplicate images or entries for the same lesion.
References
Publications (0)
Data not yet available