An observational study in Melanoma and Melanoma, Skin, sponsored by AI Labs Group S.L. Completed at 1 site in Spain. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-03-16.
Sponsored by AI Labs Group S.L · Observational
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. The main questions the study aims to answer are:
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
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.
Study Phases and Sample Size Plan
The investigation was planned in two phases to ensure a representative dataset:
Performance Evaluation Measures
The device's effectiveness is evaluated through the following pre-specified statistical metrics:
3,370 studies on the registry are indexed under Melanoma; 578 are open to participants now.
This study's enrollment of 105 is below the median of 187 across 488 observational studies indexed under Melanoma.
Browse Melanoma studies →AI Labs Group S.L is the lead sponsor of 8 studies on the registry; 1 is open to participants now.
Counted across the registry records on this site, refreshed daily.
Patients with skin lesions with suspected malignancy seen at the Dermatology Department of the Hospital Universitario Cruces and Hospital Universitario Basurto.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Malignancy means cancer. Criteria about another or earlier malignancy ask whether you have had a different cancer, usually within a set number of years.
Some are commonly allowed, such as treated skin cancers other than melanoma and early cancers removed completely — check the exceptions this study lists.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Cancer (malignancy)
Malignancy means cancer. Criteria about another or earlier malignancy ask whether you have had a different cancer, usually within a set number of years.
Some are commonly allowed, such as treated skin cancers other than melanoma and early cancers removed completely — check the exceptions this study lists.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
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.
Device: 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.
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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: At the time of the single clinical visit (Baseline).
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).
Time frame: 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).
Time frame: 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).
Time frame: 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).
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: At the time of the single clinical visit (Baseline).
Recruitment took place at the Dermatology Departments of Hospital Universitario Cruces and Hospital Universitario Basurto. Starting September 17, 2020, researchers used consecutive sampling to find adult patients with skin lesions suspected of malignancy. The process ended on November 13, 2023, after reaching a target ratio of melanoma cases. Investigators assessed eligibility during routine clinical visits.
| Milestone | Patients With Suspected Cutaneous Malignancy |
|---|---|
| Started | 105 |
| Completed | 103 |
| Not completed | 2 |
| Withdrew: Two participants were excluded from the final analysis due to non-conclusive clinical diagnoses. | 2 |
Measures the device's ability to distinguish between melanoma and non-melanoma cases using predicted probabilities.
| Probability area (value from 0 to 1) | Patients With Suspected Cutaneous Malignancy |
|---|---|
| Area Under the ROC Curve (AUC) for Melanoma Detection | 0.8482 (0.7629 to 0.9222) |
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.
| Proportion of correct cases | Patients With Suspected Cutaneous Malignancy |
|---|---|
| Accuracy for Melanoma Detection | 0.81 (0.65 to 0.94) |
The percentage of true positive melanoma cases correctly identified by the device.
| Proportion of true positives | Patients With Suspected Cutaneous Malignancy |
|---|---|
| Sensitivity for Melanoma Detection | 0.93 (0.88 to 0.98) |
The percentage of true negative (benign) cases correctly identified by the device.
| Proportion of true negatives | Patients With Suspected Cutaneous Malignancy |
|---|---|
| Specificity for Melanoma Detection | 0.80 (0.69 to 0.82) |
Evaluates if the correct diagnosis is within the Top-1 predictions across various skin disease categories (International Classification of Diseases).
| Proportion of correct matches | Patients With Suspected Cutaneous Malignancy |
|---|---|
| Top-1 Accuracy for Multiple ICD Categories | 0.55 (0.45 to 0.65) |
Evaluates if the correct diagnosis is within the Top-3 predictions across various skin disease categories (International Classification of Diseases).
| Proportion of correct matches | Patients With Suspected Cutaneous Malignancy |
|---|---|
| Top-3 Accuracy for Multiple ICD Categories | 0.7569 (0.67 to 0.83) |
Evaluates if the correct diagnosis is within the Top-5 predictions across various skin disease categories (International Classification of Diseases).
| Proportion of correct matches | Patients With Suspected Cutaneous Malignancy |
|---|---|
| Top-5 Accuracy for Multiple ICD Categories | 0.8422 (0.76 to 0.9) |
Includes AUC, Sensitivity, and Specificity for detecting any malignant lesion (not limited to melanoma).
| Probability area (value from 0 to 1) | Patients With Suspected Cutaneous Malignancy |
|---|---|
| Area Under the ROC Curve (AUC) for Malignancy Detection | 0.90 (0.84 to 0.94) |
The percentage of true positive malignant cases correctly identified by the device.
| Proportion of true positives | Patients With Suspected Cutaneous Malignancy |
|---|---|
| Sensitivity for Multiple Malignant Conditions Detection | 0.81 (0.72 to 0.88) |
The percentage of true negative (benign) cases correctly identified by the device.
| Proportion of true negatives | Patients With Suspected Cutaneous Malignancy |
|---|---|
| Specificity for Multiple Malignant Conditions Detection | 0.86 (0.77 to 0.94) |
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.
| Proportion of correct predictions | Patients With Suspected Cutaneous Malignancy |
|---|---|
| Positive Predictive Value (PPV) | 0.9247 (0.85 to 0.97) |
| Negative Predictive Value (NPV) | 0.6789 (0.54 to 0.81) |
Collected over At the baseline visit. Non-serious events are listed at a 0% frequency threshold.
| Group | Deaths | Serious | Other |
|---|---|---|---|
| Patients With Suspected Cutaneous Malignancy | 0/105 (0%) | 0/105 (0%) | 0/105 (0%) |
| Age, Continuous(years) | Patients With Suspected Cutaneous Malignancy |
|---|---|
| Mean | 62.1 ± 17.6 |
| Sex: Female, Male(Participants) | Patients With Suspected Cutaneous Malignancy |
|---|---|
| Female | 53 |
| Male | 52 |
| Race (NIH/OMB)(Participants) | Patients With Suspected Cutaneous Malignancy |
|---|---|
| American Indian or Alaska Native | 0 |
| Asian | 0 |
| Native Hawaiian or Other Pacific Islander | 0 |
| Black or African American | 0 |
| White | 102 |
| More than one race | 3 |
| Unknown or Not Reported | 0 |
| Region of Enrollment(Participants) | Patients With Suspected Cutaneous Malignancy |
|---|---|
| Spain | 105 |
| Baseline Image Quality (DIQA Score)(Units on the DIQA Scale.) | Patients With Suspected Cutaneous Malignancy |
|---|---|
| Median | 71.4 (22.8 to 87) |
PDFs hosted by the registry, ClinicalTrials.gov.
Plan to share: Undecided
Ran from Sep 2020 to primary completion in Nov 2023, 37 months.
Results were first posted in Mar 2026, 2 years 4 months after primary completion.
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