Solitary Skin LesionsNCT07415291
CNN-Based AI Versus Physicians for Solitary Skin Lesion Diagnosis
An observational study of Solitary Skin Lesions and Skin Neoplasms, run by Istanbul Training and Research Hospital at 1 site in Turkey (Türkiye).
Trial 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… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Solitary Skin Lesions, Skin Neoplasms, Skin Neoplasm Malignant
- How many people
- 17,625 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 15 Jan 2026to 31 May 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
S.B.Ü. İstanbul Eğitim ve Araştırma Hastanesi
Istanbul, Fatih, 34098
About this study
Official title: Comparison of a CNN-Based Artificial Intelligence Model With Dermatologists and Non-Dermatologist Physicians in the Diagnosis of Solitary Skin Lesions
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?
Read more
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.
Read the detailed description
This study is a retrospective, observational diagnostic accuracy study designed to evaluate the performance of a convolutional neural network (CNN)-based artificial intelligence model in the assessment of solitary skin lesions using macroscopic clinical images.
Macroscopic clinical images of solitary skin lesions with histopathological or clinically confirmed diagnoses will be retrospectively retrieved from the dermatology image archive of Istanbul Training and Research Hospital. All images and associated clinical documents will be anonymized prior to analysis, and any identifying visual or textual information will be removed. Data processing and analysis will be conducted in a secure, institution-based environment with restricted access limited to the study team.
A CNN-based artificial intelligence model will be developed using supervised learning techniques. Image preprocessing steps will include resizing to standardized input dimensions, color normalization, and removal of regions containing potentially identifiable information. The dataset will be partitioned into training, validation, and test subsets to enable model development, hyperparameter optimization, and independent performance evaluation. Model training and evaluation will be implemented using the PyTorch deep learning framework.
The diagnostic performance of the CNN-based model will be evaluated using standard classification metrics and will be compared with the independent assessments of dermatologists, dermatology residents, and non-dermatologist physicians who evaluate the same set of anonymized images without access to additional clinical or histopathological information. Comparative analyses will be performed to assess differences in diagnostic performance and agreement between the artificial intelligence model and physician groups.
- 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
- Medical subject headings
- Skin Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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." |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Baseline (Retrospective data analysis will be completed within 4 months)
Secondary outcomes (4)
Difference in diagnostic performance between the CNN-based model and dermatologists
The difference in diagnostic performance between the CNN-based artificial intelligence model and dermatologists will be evaluated based on accuracy metrics using the same set of macroscopic clinical images.
Measured: Baseline (Expected completion within 5 months)
Difference in diagnostic performance between the CNN-based model and non-dermatologist physicians
The difference in diagnostic performance between the CNN-based artificial intelligence model and non-dermatologist physicians will be evaluated based on accuracy metrics using the same image set.
Measured: Baseline (Expected completion within 5 months)
Sensitivity, specificity of the CNN-based model and physician groups
Sensitivity, specificity of the CNN-based artificial intelligence model and physician groups will be calculated and compared in the evaluation of solitary skin lesions.
Measured: Baseline (Expected completion within 5 months)
F1-score of the CNN-based model and physician groups
F1-score values of the CNN-based artificial intelligence model and physician groups will be calculated and compared in the evaluation of solitary skin lesions.
Measured: Baseline (Expected completion within 5 months)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Feb 2026
- Study start15 Jan 2026
- Primary completion30 Mar 2026 (estimated)
- Study completion31 May 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Solitary Skin LesionsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 17,625 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Istanbul Training and Research Hospital
- Sponsor type
- Other government
- Responsible party
- Principal investigator, Yunus Emre Ulusoy, Istanbul Training and Research Hospital
- Investigators
- Ayşe Esra Koku Aksu, MD, Study chair, Sağlık Bilimleri Üniversitesi İstanbul Eğitim ve Araştırma Hastanesi
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07415291
- Sponsor's protocol ID
- SBU-IEAH-KAEK-20260109-19
- Status last verified
- Feb 2026
- First submitted
- 10 Feb 2026
- Last updated
- 19 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared due to institutional data protection policies and the use of retrospectively collected, anonymized clinical images. Data are stored in a secure institutional environment and are accessible only to the study team.
Study at a glance
From the registry record, last updated by the sponsor on 19 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparison of a CNN-Based Artificial Intelligence Model With Dermatologists and Non-Dermatologist Physicians in the Diagnosis of Solitary Skin Lesions
- ClinicalTrials.gov ID
- NCT07415291
- Acronym / sponsor ID
- SBU-IEAH-KAEK-20260109-19
- Keywords
- Solitary Skin Lesions, Artificial Intelligence, Convolutional Neural Network, Dermatology, Diagnostic Accuracy, Clinical Images, Physician Comparison, Dermatologists
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 17,625 participants (estimated)
- Lead sponsor
- Istanbul Training and Research HospitalOther government
- Study start
- 15 Jan 2026 (actual)
- Primary completion
- 30 Mar 2026 (estimated)
- Study completion
- 31 May 2026 (estimated)
- First posted
- 17 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
This study is a retrospective, observational diagnostic accuracy study designed to evaluate the performance of a convolutional neural network (CNN)-based artificial intelligence model in the assessment of solitary skin lesions using macroscopic clinical images.
Macroscopic clinical images of solitary skin lesions with histopathological or clinically confirmed diagnoses will be retrospectively retrieved from the dermatology image archive of Istanbul Training and Research Hospital. All images and associated clinical documents will be anonymized prior to analysis, and any identifying visual or textual information will be removed. Data processing and analysis will be conducted in a secure, institution-based environment with restricted access limited to the study team.
A CNN-based artificial intelligence model will be developed using supervised learning techniques. Image preprocessing steps will include resizing to standardized input dimensions, color normalization, and removal of regions containing potentially identifiable information. The dataset will be partitioned into training, validation, and test subsets to enable model development, hyperparameter optimization, and independent performance evaluation. Model training and evaluation will be implemented using the PyTorch deep learning framework.
The diagnostic performance of the CNN-based model will be evaluated using standard classification metrics and will be compared with the independent assessments of dermatologists, dermatology residents, and non-dermatologist physicians who evaluate the same set of anonymized images without access to additional clinical or histopathological information. Comparative analyses will be performed to assess differences in diagnostic performance and agreement between the artificial intelligence model and physician groups.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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." |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Diagnostic accuracy of the CNN-based artificial intelligence model
Time frame: Baseline (Retrospective data analysis will be completed within 4 months)
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.
4 secondary outcomes
Difference in diagnostic performance between the CNN-based model and dermatologists
Time frame: Baseline (Expected completion within 5 months)
Difference in diagnostic performance between the CNN-based model and non-dermatologist physicians
Time frame: Baseline (Expected completion within 5 months)
Sensitivity, specificity of the CNN-based model and physician groups
Time frame: Baseline (Expected completion within 5 months)
F1-score of the CNN-based model and physician groups
Time frame: Baseline (Expected completion within 5 months)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Feb 2026.
- Individual participant dataSharing planned: No. Individual participant data will not be shared due to institutional data protection policies and the use of retrospectively collected, anonymized clinical images. Data are stored in a secure institutional environment and are accessible only to the study team.
Related trials for Solitary Skin Lesions
Not yet recruiting
Human-AI Uncertainty Callibration for Improved Skin Lesion Segmentation
Skin Lesions
Recruiting
Randomized Controlled Clinical Trial of Hyperthermia and Hydrogen Peroxide 3% in the Treatment of Cutaneous Warts
Warts
Not yet recruiting
A Study to Evaluate the Safety and Clinical Performance of a Dexpanthenol Medicated Hydrogel Patch in the Treatment of Standardized Superficial Abrasions
Skin Wounds, Abrasion
Not yet recruitingPhase 1
Study of LUT017 Gel to Improve Healing of Skin Wounds After Removal of Benign Lesions in Healthy Adults
Wound Healing, Acute Skin Wounds
Recruiting
A Clinical Investigation to Confirm the Ability of MammoWave in Breast Lesions Detection
Breast Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Ayşe Esra Koku Aksu, MD study chair
Sağlık Bilimleri Üniversitesi İstanbul Eğitim ve Araştırma Hastanesi
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Solitary Skin Lesions and Skin Neoplasms, run by Istanbul Training and Research Hospital at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 19 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic accuracy of the CNN-based artificial intelligence model (time frame: Baseline (Retrospective data analysis will be completed within 4 months)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Feb 2026.