MelanomaLEGIT_MC_EVCDANCT06221397

AI-based Medical Device Validation for Early Melanoma Detection

An observational study of Melanoma and Melanoma, Skin, run by AI Labs Group S.L at 1 site in Spain.

Completed

Finished. The study has ended and is not enrolling. Results may be posted on the official record.

Trial summary

The goal of this observational study is to learn if a computer-aided diagnosis (CAD) system can help identify skin cancer (cutaneous melanoma). The research focuses on adults who have skin spots that a doctor thinks might be cancerous. Read more about this study

Who is it for?: Ages 18 years and older
Conditions the study is for: Melanoma, Melanoma, Skin
What is being tested?: AI-based Computer-Aided Diagnosis (CAD) Software for Skin Lesion Analysis.
Type of treatment: Medical device
How many people: 105 participants
Where: 1 siteSpain
Study dates: 17 Sep 2020to 13 Nov 2023
How long will I take part?: 1 Day
Phase: Observational
Are there results?: Yes, posted in the registrySummary in the doctors' view

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 18 years and older
All sexes
No healthy volunteers
Adult, Older adult

Study population: Patients with skin lesions with suspected malignancy seen at the Dermatology Department of the Hospital Universitario Cruces and Hospital Universitario Basurto.

Who may be able to join

Patients with skin lesions with suspected malignancy
Age over 18 years old
Patients who consent to participate in the study by signing the Informed Consent form

Who cannot join

Patients under 18 years of age

Trial locations and contacts

1 site in 1 country.

Spain1 site

University Hospital of Cruces
Barakaldo, Biscay, 48903

Other trials in Spain

About this study

Official title: Clinical Validation Study of a CAD System With Artificial Intelligence Algorithms for Early Noninvasive in Vivo Cutaneous Melanoma Detection

Can the artificial intelligence (AI) tool accurately identify melanoma in skin images?

How does the tool's accuracy compare to the clinical judgment of expert skin doctors (dermatologists)?

Researchers will compare the results from the AI tool to the final diagnosis made by doctors or through a skin biopsy. A biopsy is a medical test where a small piece of skin is removed and checked in a lab.

Participants will:

Have their skin spots photographed using a special camera attached to a smartphone.

Allow researchers to use their clinical data and biopsy results for the study.

The study does not change the medical care participants receive. Doctors will continue to treat participants as they normally would. By testing this tool, researchers hope to find a way to detect skin cancer earlier and more accurately

Read the detailed description

This study is designed to clinically validate a computer-aided diagnosis (CAD) system that utilizes artificial intelligence (AI) and machine vision to assist in the detection of cutaneous melanoma in its early stages. Cutaneous melanoma is a form of skin cancer that is treatable when identified early; however, differentiating early melanoma from benign skin lesions during visual examination presents a challenge for healthcare professionals.

Study Design and Methodology The research is a prospective, observational, and cross-sectional study conducted at Hospital Universitario Cruces and Hospital Universitario Basurto in Spain. The protocol evaluates the diagnostic performance of an AI device using clinical images without interfering with routine patient care.

Participant Selection: The study focuses on adults with skin lesions suspected of malignancy during regular clinical visits.
Image Acquisition: Researchers capture photographs of skin lesions using a smartphone equipped with a specialized dermoscopic camera.
Data Collection: Clinical and demographic data, such as age and sex, are collected alongside the digital images.
Preprocessing: All images undergo a standardized preprocessing step where the lesion is cropped to minimize background noise for the algorithm.
AI Analysis: The system processes the cropped images to generate a malignancy probability and a list of possible disease categories.
Reference Standard: AI predictions are compared against a composite "Gold Standard." This standard is based on pathological anatomy results from a biopsy or, where a biopsy is not clinically indicated, the consensus diagnosis of expert dermatologists with extensive clinical experience.

Study Phases and Sample Size Plan

The investigation was planned in two phases to ensure a representative dataset:

Initial Phase: An initial pilot phase to assess the preliminary workflow.
Extension Phase: A second phase intended to broaden the sample to include a wider range of benign lesions, such as nevi, to better reflect the distribution of cases seen in daily clinical practice.
Sample Size Target: The study was designed to recruit participants until a sufficient number of confirmed melanoma cases were achieved to provide the statistical power required to validate the device's performance metrics.

Performance Evaluation Measures

The device's effectiveness is evaluated through the following pre-specified statistical metrics:

Primary Metrics: Area Under the ROC Curve (AUC), sensitivity, and specificity for the detection of melanoma.
Secondary Metrics: Top-K accuracy (evaluating if the correct diagnosis is within the top 1, 3, or 5 predictions) and malignancy prediction values, including Positive Predictive Value (PPV) and Negative Predictive Value (NPV).
Safety Assessment: The protocol includes the systematic recording of any adverse events or product deficiencies encountered during the use of the device to evaluate its safety profile as a clinical decision-support tool.

Conditions: Melanoma, Melanoma, Skin
Being tested: AI-based Computer-Aided Diagnosis (CAD) Software for Skin Lesion Analysis.
Keywords: melanoma, primary care, dermatology, diagnosis, Artificial Intelligence, Severity
Medical subject headings: Melanoma, Disease

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

AI-based Computer-Aided Diagnosis (CAD) Software for Skin Lesion Analysis.Device
The intervention is a software-only medical device that utilizes artificial intelligence and machine vision algorithms to analyze digital images of the skin. Unlike traditional diagnostic tools, this system is designed to provide quantitative data on visible clinical signs and an interpretative distribution of possible disease categories (ICD codes).
Key Distinguishing Features
Non-Invasive Diagnostic Support: It acts as a clinical decision-support tool to help practitioners prioritize patients based on malignancy risk, rather than providing a standalone or confirmatory diagnosis.
Broad ICD Recognition: While many tools focus only on melanoma, this system is capable of recognizing a variety of ICD categories, including basal cell carcinoma, nevi, and dermatofibroma
Advanced Image Preprocessing: The system includes a Dermatology Image Quality Assessment (DIQA) algorithm to ensure images have sufficient visual quality before analysis.

Study groups in NCT06221397
Study group	Role	What they receive
Patients with suspected cutaneous malignancy		Group/Cohort Description The study group consists of adult patients (over 18 years old) who presented at the Dermatology Departments of Hospital Universitario Cruces and Hospital Universitario Basurto with skin lesions suspected of being malignant. As this is an observational study, participants were not assigned to any new medical interventions, drugs, or treatments as part of the research protocol. Receives: Device: AI-based Computer-Aided Diagnosis (CAD) Software for Skin Lesion Analysis.

What is being measured

The primary outcome is the main question the study is designed to answer.

Area Under the ROC Curve (AUC) for Melanoma Detection
Measures the device's ability to distinguish between melanoma and non-melanoma cases using predicted probabilities.
Measured: At the time of the single clinical visit (Baseline).
Accuracy for Melanoma Detection
Accuracy represents the percentage of all cases where the AI software's primary (top-ranked) prediction correctly matched the confirmed medical diagnosis. The "confirmed diagnosis" was determined by either a laboratory biopsy (the gold standard) or a consensus of expert dermatologists.
To calculate this, the AI analyzed high-resolution dermoscopic images of skin lesions. The software succeeded if its highest-probability diagnosis category matched the actual disease category of the lesion. Only images meeting a minimum visual quality score (DIQA ≥ 5) were included in this analysis to ensure the results reflect performance in a professional clinical setting.
Measured: At the time of the single clinical visit (Baseline)
Sensitivity for Melanoma Detection
The percentage of true positive melanoma cases correctly identified by the device.
Measured: At the time of the single clinical visit (Baseline).
Specificity for Melanoma Detection
The percentage of true negative (benign) cases correctly identified by the device.
Measured: At the time of the single clinical visit (Baseline).

Secondary outcomes (7)

Top-1 Accuracy for Multiple ICD Categories
Evaluates if the correct diagnosis is within the Top-1 predictions across various skin disease categories (International Classification of Diseases).
Measured: At the time of the single clinical visit (Baseline).
Top-3 Accuracy for Multiple ICD Categories
Evaluates if the correct diagnosis is within the Top-3 predictions across various skin disease categories (International Classification of Diseases).
Measured: At the time of the single clinical visit (Baseline).
Top-5 Accuracy for Multiple ICD Categories
Evaluates if the correct diagnosis is within the Top-5 predictions across various skin disease categories (International Classification of Diseases).
Measured: At the time of the single clinical visit (Baseline).
Area Under the ROC Curve (AUC) for Malignancy Detection
Includes AUC, Sensitivity, and Specificity for detecting any malignant lesion (not limited to melanoma).
Measured: At the time of the single clinical visit (Baseline).
Sensitivity for Multiple Malignant Conditions Detection
The percentage of true positive malignant cases correctly identified by the device.
Measured: At the time of the single clinical visit (Baseline).
Specificity for Multiple Malignant Conditions Detection
The percentage of true negative (benign) cases correctly identified by the device.
Measured: At the time of the single clinical visit (Baseline).
Predictive Values (PPV and NPV) for Malignancy
Measures the Positive Predictive Value (PPV) and Negative Predictive Value (NPV) to determine the probability that a "malignant" or "benign" result from the device is correct.
Measured: At the time of the single clinical visit (Baseline).

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

17 Sep 202013 Nov 2023

First posted24 Jan 2024
Study start17 Sep 2020
Primary completion13 Nov 2023
Study completion13 Nov 2023

Resources

Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
Other trials for MelanomaStudies 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: Case only
Time perspective: Cross-sectional
Enrolment: 105 participants (actual)
Target duration: 1 Day

IDs, dates and publications

ClinicalTrials.gov ID: NCT06221397
Sponsor's protocol ID: LEGIT_MC_EVCDAO_2019
Acronym: LEGIT_MC_EVCDA
Status last verified: Feb 2026
First submitted: 15 Jan 2024
Last updated: 16 Mar 2026
Results posted: Yes, on ClinicalTrials.gov
Data monitoring committee: No
FDA-regulated drug: No
FDA-regulated device: No
Individual data sharing: Undecided

Want to know more?

The sponsor has not listed a contact. The official record may have more details.

Talk to your doctor first and take this page with you. Only the study team can confirm whether you can join.

Questions to ask the study team

What is the study trying to find out?
What would I need to do, and how often would I visit?
Could I receive a placebo instead of the treatment?
What are the possible risks and benefits for me?
Will it cost me anything? Are travel or time paid for?
Can I keep seeing my own doctor during the study?
What happens when the study ends?

See locations

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Study at a glance

From the registry record, last updated by the sponsor on 16 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.

Official title: Clinical Validation Study of a CAD System With Artificial Intelligence Algorithms for Early Noninvasive in Vivo Cutaneous Melanoma Detection
ClinicalTrials.gov ID: NCT06221397
Acronym / sponsor ID: LEGIT_MC_EVCDA / LEGIT_MC_EVCDAO_2019
Conditions: Melanoma, Melanoma, Skin
Keywords: melanoma, primary care, dermatology, diagnosis, Artificial Intelligence, Severity
Study type: Observational
Recruitment status: Completed
Enrolment: 105 participants (actual)
Lead sponsor: AI Labs Group S.LIndustry (pharma, biotech, devices)
Collaborators: Servicio Vasco de Salud Osakidetza, Spain, Osakidetza, Hospital de Basurto, Hospital de Cruces
Study start: 17 Sep 2020 (actual)
Primary completion: 13 Nov 2023 (actual)
Study completion: 13 Nov 2023 (actual)
First posted: 24 Jan 2024 (actual)
Results: Posted 16 Mar 2026

Clinical summary

The research question as the sponsor registered it. This describes what the study plans to test, not what it found.

Can the artificial intelligence (AI) tool accurately identify melanoma in skin images?

How does the tool's accuracy compare to the clinical judgment of expert skin doctors (dermatologists)?

Participants will:

Have their skin spots photographed using a special camera attached to a smartphone.

Allow researchers to use their clinical data and biopsy results for the study.

Detailed description

Participant Selection: The study focuses on adults with skin lesions suspected of malignancy during regular clinical visits.
Image Acquisition: Researchers capture photographs of skin lesions using a smartphone equipped with a specialized dermoscopic camera.
Data Collection: Clinical and demographic data, such as age and sex, are collected alongside the digital images.
Preprocessing: All images undergo a standardized preprocessing step where the lesion is cropped to minimize background noise for the algorithm.
AI Analysis: The system processes the cropped images to generate a malignancy probability and a list of possible disease categories.
Reference Standard: AI predictions are compared against a composite "Gold Standard." This standard is based on pathological anatomy results from a biopsy or, where a biopsy is not clinically indicated, the consensus diagnosis of expert dermatologists with extensive clinical experience.

Study Phases and Sample Size Plan

The investigation was planned in two phases to ensure a representative dataset:

Initial Phase: An initial pilot phase to assess the preliminary workflow.
Extension Phase: A second phase intended to broaden the sample to include a wider range of benign lesions, such as nevi, to better reflect the distribution of cases seen in daily clinical practice.
Sample Size Target: The study was designed to recruit participants until a sufficient number of confirmed melanoma cases were achieved to provide the statistical power required to validate the device's performance metrics.

Performance Evaluation Measures

The device's effectiveness is evaluated through the following pre-specified statistical metrics:

Primary Metrics: Area Under the ROC Curve (AUC), sensitivity, and specificity for the detection of melanoma.
Secondary Metrics: Top-K accuracy (evaluating if the correct diagnosis is within the top 1, 3, or 5 predictions) and malignancy prediction values, including Positive Predictive Value (PPV) and Negative Predictive Value (NPV).
Safety Assessment: The protocol includes the systematic recording of any adverse events or product deficiencies encountered during the use of the device to evaluate its safety profile as a clinical decision-support tool.

Study design

Observational model: Case only
Time perspective: Cross-sectional
Target follow-up: 1 Day
Number of arms: 1

Arm	Type	Receives
Patients with suspected cutaneous malignancy	–	Device: AI-based Computer-Aided Diagnosis (CAD) Software for Skin Lesion Analysis.Group/Cohort Description The study group consists of adult patients (over 18 years old) who presented at the Dermatology Departments of Hospital Universitario Cruces and Hospital Universitario Basurto with skin lesions suspected of being malignant. As this is an observational study, participants were not assigned to any new medical interventions, drugs, or treatments as part of the research protocol.

Who may qualify

Ages: 18 years and older
Sex: All
Healthy volunteers: Not accepted
Age groups: Adult, Older adult
Study population: Patients with skin lesions with suspected malignancy seen at the Dermatology Department of the Hospital Universitario Cruces and Hospital Universitario Basurto.

Key inclusion criteria (3 listed)

Patients with skin lesions with suspected malignancy
Age over 18 years old
Patients who consent to participate in the study by signing the Informed Consent form

Key exclusion criteria (1 listed)

Patients under 18 years of age

Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.

Interventions

Intervention	Type	Arms
AI-based Computer-Aided Diagnosis (CAD) Software for Skin Lesion Analysis.The intervention is a software-only medical device that utilizes artificial intelligence and machine vision algorithms to analyze digital images of the skin. Unlike traditional diagnostic tools, this system is designed to provide quantitative data on visible clinical signs and an interpretative distribution of possible disease categories (ICD codes). Key Distinguishing Features Non-Invasive Diagnostic Support: It acts as a clinical decision-support tool to help practitioners prioritize patients based on malignancy risk, rather than providing a standalone or confirmatory diagnosis. Broad ICD Recognition: While many tools focus only on melanoma, this system is capable of recognizing a variety of ICD categories, including basal cell carcinoma, nevi, and dermatofibroma Advanced Image Preprocessing: The system includes a Dermatology Image Quality Assessment (DIQA) algorithm to ensure images have sufficient visual quality before analysis.	Device	Patients with suspected cutaneous malignancy

Primary outcome

Area Under the ROC Curve (AUC) for Melanoma Detection
Time frame: At the time of the single clinical visit (Baseline).
Measures the device's ability to distinguish between melanoma and non-melanoma cases using predicted probabilities.
Accuracy for Melanoma Detection
Time frame: At the time of the single clinical visit (Baseline)
Accuracy represents the percentage of all cases where the AI software's primary (top-ranked) prediction correctly matched the confirmed medical diagnosis. The "confirmed diagnosis" was determined by either a laboratory biopsy (the gold standard) or a consensus of expert dermatologists. To calculate this, the AI analyzed high-resolution dermoscopic images of skin lesions. The software succeeded if its highest-probability diagnosis category matched the actual disease category of the lesion. Only images meeting a minimum visual quality score (DIQA ≥ 5) were included in this analysis to ensure the results reflect performance in a professional clinical setting.
Sensitivity for Melanoma Detection
Time frame: At the time of the single clinical visit (Baseline).
The percentage of true positive melanoma cases correctly identified by the device.
Specificity for Melanoma Detection
Time frame: At the time of the single clinical visit (Baseline).
The percentage of true negative (benign) cases correctly identified by the device.

7 secondary outcomes

Top-1 Accuracy for Multiple ICD Categories
Time frame: At the time of the single clinical visit (Baseline).
Top-3 Accuracy for Multiple ICD Categories
Time frame: At the time of the single clinical visit (Baseline).
Top-5 Accuracy for Multiple ICD Categories
Time frame: At the time of the single clinical visit (Baseline).
Area Under the ROC Curve (AUC) for Malignancy Detection
Time frame: At the time of the single clinical visit (Baseline).
Sensitivity for Multiple Malignant Conditions Detection
Time frame: At the time of the single clinical visit (Baseline).
Specificity for Multiple Malignant Conditions Detection
Time frame: At the time of the single clinical visit (Baseline).
Predictive Values (PPV and NPV) for Malignancy
Time frame: At the time of the single clinical visit (Baseline).

Results and safety

Results posted to the registry on 16 Mar 2026, as reported by the sponsor. Figures are copied from the record, not re-analysed. Full results on ClinicalTrials.gov.

Participant flow

Group	Started	Completed
Patients With Suspected Cutaneous Malignancy	105	103

Primary outcome: Area Under the ROC Curve (AUC) for Melanoma Detection

Time frame: At the time of the single clinical visit (Baseline).. Number (95% Confidence Interval). Unit: Probability area (value from 0 to 1)

Measure	Patients With Suspected Cutaneous Malignancy n = 105
Value	0.8482 (0.7629 to 0.9222)

Population analysed: This difference is due to the fact that a single participant could have more than one lesion; as a result, there were 105 patients but 114 lesions registered.

Primary outcome: Accuracy for Melanoma Detection

Time frame: At the time of the single clinical visit (Baseline). Number (95% Confidence Interval). Unit: Proportion of correct cases

Measure	Patients With Suspected Cutaneous Malignancy n = 105
Value	0.81 (0.65 to 0.94)

Population analysed: This difference is due to the fact that a single participant could have more than one lesion; as a result, there were 105 patients but 114 lesions registered.

Primary outcome: Sensitivity for Melanoma Detection

Time frame: At the time of the single clinical visit (Baseline).. Number (95% Confidence Interval). Unit: Proportion of true positives

Measure	Patients With Suspected Cutaneous Malignancy n = 105
Value	0.93 (0.88 to 0.98)

Population analysed: This difference is due to the fact that a single participant could have more than one lesion; as a result, there were 105 patients but 114 lesions registered.

Primary outcome: Specificity for Melanoma Detection

Time frame: At the time of the single clinical visit (Baseline).. Number (95% Confidence Interval). Unit: Proportion of true negatives

Measure	Patients With Suspected Cutaneous Malignancy n = 105
Value	0.80 (0.69 to 0.82)

Population analysed: This difference is due to the fact that a single participant could have more than one lesion; as a result, there were 105 patients but 114 lesions registered.

7 secondary outcomes are reported in the full results.

Adverse events

Time frame: At the baseline visit. Other adverse events reported at 0% frequency or more

Group	Serious adverse events	Other adverse events	Deaths
Patients With Suspected Cutaneous Malignancy	0 of 105 (0%)	0 of 105 (0%)	0 of 105

Limitations and caveats (from the sponsor): The study's primary limitation was a smaller final sample size (105 participants) compared to the initial target (200), primarily due to the COVID-19 pandemic's impact on clinical recruitment. Additionally, the reliance on a specific hardware setup (DermLite Foto X and smartphones) means results may not generalize to all photographic equipment. Lastly, the exclusion of low-quality images (DIQA \< 5) implies the AI's performance depends on the user's ability to capture clear photos.

Loading the posted results from ClinicalTrials.gov…

Research links

Official record on ClinicalTrials.govLast updated by the sponsor 16 Mar 2026.
Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
Individual participant dataSharing planned: Undecided

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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.

Jesús Gardeazabal, PhD principal investigator

Hospital de Cruces

Rosa María Ize, PhD principal investigator

Hospital Universitario Basurto

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 Melanoma and Melanoma, Skin, run by AI Labs Group S.L at 1 site in Spain.

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 1 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: finished, as of the sponsor's last update on 16 Mar 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 Spain. 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?

Yes. Results are posted in the registry (first posted 16 Mar 2026). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.

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?

Area Under the ROC Curve (AUC) for Melanoma Detection (time frame: At the time of the single clinical visit (Baseline).). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT06221397.

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 16 Mar 2026.