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Completed

NCT Number: NCT05126173

DERM US and EU Validation Study

This study aims establish the effectiveness of Image Analysing Algorithm (DERM) to identify melanoma, Squamous Cell Carcinoma (SCC) and Basal Cell Carcinoma (BCC) when used to analyse dermoscopic images of skin lesions within the US and European population.

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Key information

Conditions

Malignant Skin Melanoma T0 Basal Cell Carcinoma Carcinoma Carcinoma, Basal Cell Carcinoma, Squamous Cell Neoplasms Neoplasms by Histologic Type Neoplasms, Basal Cell Neoplasms, Glandular and Epithelial Neoplasms, Squamous Cell Squamous Cell Carcinoma

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Universitaria Di Bologna, Bologna, Italy

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Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Willing and able to give informed consent for participation in the study,
  • Male or Female, aged 18 years or above,
  • Have at least one suitable skin lesion that will be biopsied due to a suspicion of skin cancer,

To be suitable for inclusion, a skin lesion must NOT have ANY of the following limitations:

located on an anatomical site of different skin structure: palms of hands or soles of feet (acral lesion), mucosal surfaces (lips and eyes) or under nail (ungal lesion), a diameter greater than the diameter of the dermoscopic lenses, located on an anatomical site unsuitable for photographing, including on surface of genitals and hair-bearing areas, has been previously biopsied, excised, treated or otherwise traumatised, located in an area of visible scarring or tattooing.

  • In the Investigator's opinion, able and willing to comply with all study requirements.

Exclusion criteria

  • Any other significant disease or disorder which, in the opinion of the Investigator, may either put the participant at risk because of participation in the study, or may influence the result of the study, or the participant's ability to participate in the study.

Treatment and study plan

Deep Ensemble for the Recognition of Malignancy (DERM)

Device

An AI-based diagnosis support tool

Primary outcomes

  1. Sensitivity of DERM to detect "Malignant conditions"

    Time frame: Through study completion, on average of 1 day

    Sensitivity of DERM to detect Melanoma, SCC and BCC combined

  2. Specificity of DERM to detect Malignant conditions.

    Time frame: Through study completion, on average of 1 day

    Specificity of DERM to detect Melanoma, SCC and BCC combined

Secondary outcomes

  1. Sensitivity of DERM to detect Melanoma

    Time frame: Through study completion, on average of 1 day

    Sensitivity of DERM to detect Melanoma

  2. Specificity of DERM to detect Melanoma

    Time frame: Through study completion, on average of 1 day

    Specificity of DERM to detect Melanoma

  3. Sensitivity of DERM to detect Squamous Cell Carcinoma

    Time frame: Through study completion, on average of 1 day

    Sensitivity of DERM to correctly classify SCC

  4. Specificity of DERM to detect Squamous Cell Carcinoma

    Time frame: Through study completion, on average of 1 day

    Specificity of DERM to correctly classify SCC

  5. Sensitivity of DERM to detect Basal Cell Carcinoma

    Time frame: Through study completion, on average of 1 day

    Sensitivity of DERM to correctly classify BCC

  6. Specificity of DERM to detect Basal Cell Carcinoma

    Time frame: Through study completion, on average of 1 day

    Specificity of DERM to correctly classify BCC

  7. Accuracy of mole/not mole algorithm

    Time frame: Through study completion, on average of 1 day

    Accuracy of mole/not mole algorithm

Other outcomes

  1. Diagnostic accuracy measures

    Time frame: Through study completion, on average of 1 day

    AUROC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) etc. of DERM to detect melanoma, SCC, BCC, premalignant, and benign conditions grouped and individually

  2. Probability that the most probable lesion label DERM returns matches the lesion diagnosis

    Time frame: Through study completion, on average of 1 day

    Probability that the alternative classification label DERM that returns, matches the lesion diagnosis

  3. The impact of patient characteristics on the diagnostic accuracy of DERM

    Time frame: Through study completion, on average of 1 day

    Such as sex, age, and Fitzpatrick skin type

  4. The impact of lesions characteristic on the diagnostic accuracy of DERM

    Time frame: Through study completion, on average of 1 day

    Such as location, size, growth, stage and sub-type

  5. AUROC of DERM to identify malignant conditions for each individual camera / lens type

    Time frame: Through study completion, on average of 1 day

    AUROC of DERM to identify malignant conditions for each individual camera / lens type

  6. Concordance of DERM results by each individual camera / lens type

    Time frame: Through study completion, on average of 1 day

    Concordance of DERM results by each individual camera / lens type

  7. Strength of association between correct classification and acceptance/rejection status of images

    Time frame: Through study completion, on average of 1 day

    Strength of association between correct classification and acceptance/rejection status of images

  8. AUROC of DERM when macro images are used both to train the algorithm and as test images

    Time frame: Through study completion, on average of 1 day

    AUROC of DERM when macro images are used both to train the algorithm and as test images

  9. Correlation between clinician assessment of likelihood of skin cancer with histopathology diagnosis

    Time frame: Through study completion, on average of 1 day

    Correlation between clinician assessment of likelihood of skin cancer with histopathology diagnosis

  10. Percentage of images taken that are rejected by the IQ check (MoleNotMole + Image Quality), where a second image is successfully taken

    Time frame: Through study completion, on average of 1 day

    Percentage of images taken that are rejected by the IQ check (MoleNotMole + Image Quality), where a second image is successfully taken

Sponsors and collaborators

Lead sponsor

Skin Analytics Limited

Industry

Registry information

Official study title

A Clinical Validation Study to Demonstrate the Effectiveness of an Artificial Intelligence Algorithm (DERM) to Identify Skin Cancer in Patients Undergoing a Skin Biopsy

Important dates

Study start
2022
Primary completion
2022
Study completion
2022
First posted
Nov 18, 2021
Registry last updated
Jun 28, 2023

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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