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| Name | Class |
|---|---|
| Servicio Vasco de Salud Osakidetza, Spain | UNKNOWN |
| Osakidetza | OTHER |
| Hospital de Basurto | OTHER |
| Hospital de Cruces |
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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:
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 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. | 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. | 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. | At the time of the single clinical visit (Baseline). |
| Measure | Description | Time Frame |
|---|---|---|
| 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). | At the time of the single clinical visit (Baseline). |
| Top-3 Accuracy for Multiple ICD Categories |
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Inclusion Criteria:
Exclusion Criteria:
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Patients with skin lesions with suspected malignancy seen at the Dermatology Department of the Hospital Universitario Cruces and Hospital Universitario Basurto.
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| Name | Affiliation | Role |
|---|---|---|
| Jesús Gardeazabal, PhD | Hospital de Cruces | Principal Investigator |
| Rosa MarÃa Ize, PhD | Hospital Universitario Basurto | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University Hospital of Cruces | Barakaldo | Biscay | 48903 | Spain |
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After enrollment, the most significant event was a pilot data analysis of the first 40 participants. Researchers found that the initial cohort, which only included high-suspicion cases requiring biopsy, was not representative of daily clinical practice. Consequently, the intended sample was extended to 200 to include common benign lesions like nevi. But finally, 105 patients were enrolled. Two participants were excluded from the final analysis due to non-conclusive clinical diagnoses.
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.
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| ID | Title | Description |
|---|---|---|
| FG000 | 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. |
| Title | Milestones | Reasons Not Completed | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
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|
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| ID | Title | Description |
|---|---|---|
| BG000 | 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. |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Mean |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | |||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | 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. | 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. | Posted | Number | 95% Confidence Interval | Probability area (value from 0 to 1) | At the time of the single clinical visit (Baseline). | Lesions | Lesions |
|
At the baseline visit
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | 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. |
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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.
| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Dr. Jordi Barrachina - Clinical Affairs Manager | AI Labs Group S.L. | +34 653 08 83 37 | jordibarrachina@legit.health |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Oct 28, 2021 | Feb 4, 2026 | Prot_SAP_000.pdf |
| ICF | No | No | Yes | Informed Consent Form | Oct 28, 2021 | Feb 4, 2026 | ICF_001.pdf |
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| ID | Term |
|---|---|
| D008545 | Melanoma |
| D004194 | Disease |
| ID | Term |
|---|---|
| D018358 | Neuroendocrine Tumors |
| D017599 | Neuroectodermal Tumors |
| D009373 | Neoplasms, Germ Cell and Embryonal |
| D009370 | Neoplasms by Histologic Type |
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| ID | Term |
|---|---|
| D003936 | Diagnosis, Computer-Assisted |
| ID | Term |
|---|---|
| D003933 | Diagnosis |
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| OTHER |
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|
Evaluates if the correct diagnosis is within the Top-3 predictions across various skin disease categories (International Classification of Diseases). |
| 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). | 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). | 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. | 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. | 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. | At the time of the single clinical visit (Baseline). |
| years |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Race (NIH/OMB) | Count of Participants | Participants |
|
| Region of Enrollment | Number | Participants |
|
| Baseline Image Quality (DIQA Score) | This measure evaluates the visual quality of the dermoscopic images captured at the start of the study. It ensures that the images used for AI analysis have sufficient resolution, focus, and lighting to be clinically useful. The score ranges from 0 to 10, where 0 represents the lowest visual quality and 10 represents the highest visual quality. | Median | Full Range | Units on the DIQA Scale. |
|
|
|
| Primary | 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. | 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. | Posted | Number | 95% Confidence Interval | Proportion of correct cases | At the time of the single clinical visit (Baseline) | Lesions | Lesions |
|
|
|
| Primary | Sensitivity for Melanoma Detection | The percentage of true positive melanoma cases correctly identified by the device. | 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. | Posted | Number | 95% Confidence Interval | Proportion of true positives | At the time of the single clinical visit (Baseline). | Lesions | Lesions |
|
|
|
| Primary | Specificity for Melanoma Detection | The percentage of true negative (benign) cases correctly identified by the device. | 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. | Posted | Number | 95% Confidence Interval | Proportion of true negatives | At the time of the single clinical visit (Baseline). | Lesions | Lesions |
|
|
|
| Secondary | 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). | 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. | Posted | Number | 95% Confidence Interval | Proportion of correct matches | At the time of the single clinical visit (Baseline). | Lesions | Lesions |
|
|
|
| Secondary | 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). | 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. | Posted | Number | 95% Confidence Interval | Proportion of correct matches | At the time of the single clinical visit (Baseline). | Lesions | Lesions |
|
|
|
| Secondary | 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). | 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. | Posted | Number | 95% Confidence Interval | Proportion of correct matches | At the time of the single clinical visit (Baseline). | Lesions | Lesions |
|
|
|
| Secondary | Area Under the ROC Curve (AUC) for Malignancy Detection | Includes AUC, Sensitivity, and Specificity for detecting any malignant lesion (not limited to melanoma). | 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. | Posted | Number | 95% Confidence Interval | Probability area (value from 0 to 1) | At the time of the single clinical visit (Baseline). | Lesions | Lesions |
|
|
|
| Secondary | Sensitivity for Multiple Malignant Conditions Detection | The percentage of true positive malignant cases correctly identified by the device. | 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. | Posted | Number | 95% Confidence Interval | Proportion of true positives | At the time of the single clinical visit (Baseline). | Lesions | Lesions |
|
|
|
| Secondary | Specificity for Multiple Malignant Conditions Detection | The percentage of true negative (benign) cases correctly identified by the device. | 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. | Posted | Number | 95% Confidence Interval | Proportion of true negatives | At the time of the single clinical visit (Baseline). | Lesions | Lesions |
|
|
|
| Secondary | 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. | 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. | Posted | Number | 95% Confidence Interval | Proportion of correct predictions | At the time of the single clinical visit (Baseline). | Lesions | Lesions |
|
|
|
| 0 |
| 105 |
| 0 |
| 105 |
| 0 |
| 105 |
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| D009369 | Neoplasms |
| D009380 | Neoplasms, Nerve Tissue |
| D018326 | Nevi and Melanomas |
| D012878 | Skin Neoplasms |
| D009371 | Neoplasms by Site |
| D012871 | Skin Diseases |
| D017437 | Skin and Connective Tissue Diseases |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |