NCT06221397: LEGIT_MC_EVCDA

AI-based Medical Device Validation for Early Melanoma Detection

Completed Results posted Last updated 16 March 2026

What this trial tests

trial testing AI-based Computer-Aided Diagnosis (CAD) Software for Skin Lesion Analysis. in Melanoma in 105 participants. Completed in 13 November 2023.

Timeline

17 September 2020

Primary endpoint
13 November 2023

13 November 2023

Quick facts

Lead sponsor	AI Labs Group S.L
Status	Completed
Study type	OBSERVATIONAL
Enrollment	105
Start date	17 September 2020
Primary completion	13 November 2023
Estimated completion	13 November 2023
Sites	1 location across Spain

Drugs / interventions tested

• AI-based Computer-Aided Diagnosis (CAD) Software for Skin Lesion Analysis.

Conditions studied

• Melanoma — all drugs for Melanoma →
• Melanoma, Skin — all drugs for Melanoma, Skin →

Sponsor

AI Labs Group S.L — full company profile →

Who can join

18 and older, any sex, with Melanoma or Melanoma, Skin. Patients with the condition only — healthy volunteers not accepted.

Results — posted to ClinicalTrials.gov

Per-arm endpoint measurements with 95% confidence intervals where reported. Source: trial results section.

Area Under the ROC Curve (AUC) for Melanoma Detection Primary · 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.

Group	Value	95% CI
Patients With Suspected Cutaneous Malignancy	0.8482	0.7629 – 0.9222

Accuracy for Melanoma Detection Primary · 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 th

Group	Value	95% CI
Patients With Suspected Cutaneous Malignancy	0.81	0.65 – 0.94

Sensitivity for Melanoma Detection Primary · At the time of the single clinical visit (Baseline).

The percentage of true positive melanoma cases correctly identified by the device.

Group	Value	95% CI
Patients With Suspected Cutaneous Malignancy	0.93	0.88 – 0.98

Specificity for Melanoma Detection Primary · At the time of the single clinical visit (Baseline).

The percentage of true negative (benign) cases correctly identified by the device.

Group	Value	95% CI
Patients With Suspected Cutaneous Malignancy	0.80	0.69 – 0.82

Top-1 Accuracy for Multiple ICD Categories Secondary · At the time of the single clinical visit (Baseline).

Evaluates if the correct diagnosis is within the Top-1 predictions across various skin disease categories (International Classification of Diseases).

Group	Value	95% CI
Patients With Suspected Cutaneous Malignancy	0.55	0.45 – 0.65

Top-3 Accuracy for Multiple ICD Categories Secondary · At the time of the single clinical visit (Baseline).

Evaluates if the correct diagnosis is within the Top-3 predictions across various skin disease categories (International Classification of Diseases).

Group	Value	95% CI
Patients With Suspected Cutaneous Malignancy	0.7569	0.67 – 0.83

Top-5 Accuracy for Multiple ICD Categories Secondary · At the time of the single clinical visit (Baseline).

Evaluates if the correct diagnosis is within the Top-5 predictions across various skin disease categories (International Classification of Diseases).

Group	Value	95% CI
Patients With Suspected Cutaneous Malignancy	0.8422	0.76 – 0.9

Area Under the ROC Curve (AUC) for Malignancy Detection Secondary · At the time of the single clinical visit (Baseline).

Includes AUC, Sensitivity, and Specificity for detecting any malignant lesion (not limited to melanoma).

Group	Value	95% CI
Patients With Suspected Cutaneous Malignancy	0.90	0.84 – 0.94

Sensitivity for Multiple Malignant Conditions Detection Secondary · At the time of the single clinical visit (Baseline).

The percentage of true positive malignant cases correctly identified by the device.

Group	Value	95% CI
Patients With Suspected Cutaneous Malignancy	0.81	0.72 – 0.88

Specificity for Multiple Malignant Conditions Detection Secondary · At the time of the single clinical visit (Baseline).

The percentage of true negative (benign) cases correctly identified by the device.

Group	Value	95% CI
Patients With Suspected Cutaneous Malignancy	0.86	0.77 – 0.94

Predictive Values (PPV and NPV) for Malignancy Secondary · At the time of the single clinical visit (Baseline).

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.

Positive Predictive Value (PPV)

Group	Value	95% CI
Patients With Suspected Cutaneous Malignancy	0.9247	0.85 – 0.97

Negative Predictive Value (NPV)

Group	Value	95% CI
Patients With Suspected Cutaneous Malignancy	0.6789	0.54 – 0.81

Sponsor's own description

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

Publications & conference data

No peer-reviewed publications indexed yet for this trial. Completed trials usually publish results within 12-18 months.