CNN-Based AI Versus Physicians for Solitary Skin Lesion Diagnosis

NCT07415291 | Active Not Recruiting

• 1 other identifier: SBU-IEAH-KAEK-20260109-19
• Study Type: observational
• Target: 17,625
• Locations: 1 country
• Sites: 1

Brief Summary

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

Solitary Skin Lesions; Skin Neoplasms; Skin Neoplasm Malignant

Keywords

Solitary Skin Lesions; Artificial Intelligence; Convolutional Neural Network; Dermatology; Diagnostic Accuracy; Clinical Images; Physician Comparison; Dermatologists; Non-Dermatologist Physicians; Deep Learning; Computer-Aided Diagnosis; Physician vs AI Performance

Outcome Measures

Primary Outcomes (1)

• Diagnostic accuracy of the CNN-based artificial intelligence model

  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.

  Baseline (Retrospective data analysis will be completed within 4 months)

Secondary Outcomes (4)

• Difference in diagnostic performance between the CNN-based model and dermatologists

  Baseline (Expected completion within 5 months)

• Difference in diagnostic performance between the CNN-based model and non-dermatologist physicians

  Baseline (Expected completion within 5 months)

• Sensitivity, specificity of the CNN-based model and physician groups

  Baseline (Expected completion within 5 months)

• F1-score of the CNN-based model and physician groups

  Baseline (Expected completion within 5 months)

Study Arms (1)

Development and Validation Cohort

"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."

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population consists of licensed physicians, including dermatologists, dermatology residents, and non-dermatologist physicians, who independently evaluate anonymized macroscopic clinical images of solitary skin lesions for diagnostic assessment.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Istanbul Training and Research Hospital: lead

Study Sites (1)

S.B.Ü. İstanbul Eğitim ve Araştırma Hastanesi

Istanbul, Fatih, 34098, Turkey (Türkiye)

Location

MeSH Terms

Conditions

Skin Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Skin Diseases; Skin and Connective Tissue Diseases

Study Officials

• Ayşe Esra Koku Aksu, MD

  Sağlık Bilimleri Üniversitesi İstanbul Eğitim ve Araştırma Hastanesi

  STUDY CHAIR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER GOV
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Dermatology Resident

Study Record Dates

• First Submitted: February 10, 2026
• First Posted: February 17, 2026
• Study Start: January 15, 2026
• Primary Completion: March 30, 2026
• Study Completion (Estimated): May 31, 2026
• Last Updated: February 19, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)