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OpenTrials
Completed

NCT Number: NCT04123678

DERM Health Economics Study

This study aims to provide an initial assessment of the potential impact DERM could have on the number of onward referrals for a face to face dermatologist review and/or biopsy from a teledermatology-based service, and to improve the understanding of the patient pathways that exist.

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Key information

Conditions

Melanoma Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Germ Cell and Embryonal Neoplasms, Nerve Tissue Neuroectodermal Tumors Neuroendocrine Tumors Nevi and Melanomas Non-Melanoma Skin Cancer Skin Diseases Skin Neoplasms Skin and Connective Tissue Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Chelsea and Westminster Hospital

London, SW10 9NH, United Kingdom

About this study

DERM, an Artificial Intelligence (AI)-based diagnosis support tool, has been shown to be able to accurately identify melanoma, non-melanoma skin cancers (NMSC) and other conditions from historical images of suspicious skin lesions (moles).

This study aims to establish whether the use of DERM in the patient pathway could reduce the number of unnecessary referrals to dermatologist review and/or biopsy.

Suspicious skin lesions that are due to be photographed for a dermatologist to review, will have two additional photographs taken using a commonly available smart phone camera with and without a specific lens attachment. The images will be analysed by DERM, and the results compared to the clinician's diagnosis (all lesions) and histologically-confirmed diagnosis (any lesion that is biopsied).

Who can participate

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

Inclusion criteria

  • Participant is willing and able to give informed consent for participation in the study,
  • Male or Female, aged 18 years or above,
  • Has at least one suspicious skin lesion which is being photographed as part of Standard of Care (SoC),
  • In the Investigators 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 participants 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

AI-based decision support tool

Primary outcomes

  1. Referral rate

    Time frame: Study completion, on average 5 days

    The rate of unnecessary referrals for a face to face dermatologist review for the same detection rate between standard of care and DERM of lesions reviewed by teledermatology or DERM

Secondary outcomes

  1. Sensitivity of DERM on biopsied lesions

    Time frame: Study completion, on average 5 days

    Sensitivity of DERM on biopsied lesions, using histopathological confirmed diagnosis as gold-standard

  2. Specificity of DERM on biopsied lesions

    Time frame: Study completion, on average 5 days

    Specificity of DERM on biopsied lesions, using histopathological confirmed diagnosis as gold-standard

  3. False positive rate of DERM on biopsied lesions

    Time frame: Study completion, on average 5 days

    False positive rate of DERM on biopsied lesions, using histopathological confirmed diagnosis as gold-standard

  4. False negative rate of DERM on biopsied lesions

    Time frame: Study completion, on average 5 days

    False negative rate of DERM on biopsied lesions, using histopathological confirmed diagnosis as gold-standard

  5. Positive predictive value of DERM on biopsied lesions

    Time frame: Study completion, on average 5 days

    Positive predictive value of DERM on biopsied lesions, using histopathological confirmed diagnosis as gold-standard

  6. Number needed to biopsy by DERM on biopsied lesions

    Time frame: Study completion, on average 5 days

    Number needed to biopsy by DERM on biopsied lesions, using histopathological confirmed diagnosis as gold-standard

  7. Sensitivity of teledermatologists on biopsied lesions

    Time frame: Study completion, on average 5 days

    Sensitivity of teledermatologists on biopsied lesions, using histopathological confirmed diagnosis as gold-standard

  8. Specificity of teledermatologists on biopsied lesions

    Time frame: Study completion, on average 5 days

    Specificity of teledermatologists on biopsied lesions, using histopathological confirmed diagnosis as gold-standard

  9. False positive rate of teledermatologists on biopsied lesions

    Time frame: Study completion, on average 5 days

    False positive rate of teledermatologists on biopsied lesions, using histopathological confirmed diagnosis as gold-standard

  10. False negative rate of teledermatologists on biopsied lesions

    Time frame: Study completion, on average 5 days

    False negative rate of teledermatologists on biopsied lesions, using histopathological confirmed diagnosis as gold-standard

  11. Positive predictive value of teledermatologists on biopsied lesions

    Time frame: Study completion, on average 5 days

    Positive predictive value of teledermatologists on biopsied lesions, using histopathological confirmed diagnosis as gold-standard

  12. Negative predictive value of teledermatologists on biopsied lesions

    Time frame: Study completion, on average 5 days

    Negative predictive value of teledermatologists on biopsied lesions, using histopathological confirmed diagnosis as gold-standard

  13. Number needed to biopsy by teledermatologists on biopsied lesions

    Time frame: Study completion, on average 5 days

    Number needed to biopsy by teledermatologists on biopsied lesions, using histopathological confirmed diagnosis as gold-standard

  14. Sensitivity of DERM to identify benign conditions

    Time frame: Study completion, on average 5 days

    Sensitivity of DERM to identify benign conditions, using clinical diagnosis as gold-standard

  15. Specificity of DERM to identify benign conditions

    Time frame: Study completion, on average 5 days

    Specificity of DERM to identify benign conditions, using clinical diagnosis as gold-standard

  16. False positive rate of DERM to identify benign conditions

    Time frame: Study completion, on average 5 days

    False positive of DERM to identify benign conditions, using clinical diagnosis as gold-standard

  17. False negative rate of DERM to identify benign conditions

    Time frame: Study completion, on average 5 days

    False negative rate of DERM to identify benign conditions, using clinical diagnosis as gold-standard

  18. Positive predictive value of DERM to identify benign conditions

    Time frame: Study completion, on average 5 days

    Positive predictive of DERM to identify benign conditions, using clinical diagnosis as gold-standard

  19. Negative predictive value of DERM to identify benign conditions

    Time frame: Study completion, on average 5 days

    Negative predictive value of DERM to identify benign conditions, using clinical diagnosis as gold-standard

  20. Number needed to refer by DERM to identify benign conditions

    Time frame: Study completion, on average 5 days

    Number needed to refer by DERM to identify benign conditions, using clinical diagnosis as gold-standard

  21. Concordance of DERM result with clinical diagnosis

    Time frame: Study completion, on average 5 days

    Concordance of DERM result with clinical diagnosis

  22. Percent of patients attending teledermatology by referral route

    Time frame: Study completion, on average 5 days

    Percentage of patients referred to teledermatology through 2-week wait referral, general referral, direct to teledermatology, routine follow-up (etc) referral routes

  23. Time taken from general practitioner (GP) referral to diagnosis

    Time frame: Study completion, on average 5 days

    Time taken (days) from GP referral to either histopathology-confirmed or clinical diagnosis

  24. Estimated cost impact associated with introducing DERM into the patient pathway

    Time frame: Study completion, on average 5 days

    The cost of the number of referrals for face to face dermatologist review and/or biopsy that would have been saved / charged if DERM had been used to decide whether to refer the patient onwards

  25. Proportion of images submitted to DERM that cannot be analysed

    Time frame: Study completion, on average 5 days

    Proportion of images submitted to DERM that cannot be analysed

  26. Patient satisfaction survey

    Time frame: Study completion, on average 5 days

    Patient feedback on their experience of the service. Patients will rate whether they agree, or don't agree, with statements that assess their acceptance of having a computer involved in their diagnosis pathway

Sponsors and collaborators

Lead sponsor

Skin Analytics Limited

Industry

Collaborators

  • Innovate UK

Registry information

Official study title

Impact of an Artificial Intelligence Platform (DERM) on the Healthcare Resource Utilisation (HRU) Needed to Diagnose Skin Cancer When Used as Part of a United Kingdom-based Teledermatology Service

Important dates

Study start
2020
Primary completion
2021
Study completion
2021
First posted
Oct 11, 2019
Registry last updated
Aug 13, 2021

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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