DERM Health Economics Study

NCT ID: NCT04123678

Last Updated: 2021-08-13

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 700 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2020-02-26
• Study Completion Date: 2021-08-06

Brief Summary

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.

Detailed Description

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).

Conditions

Melanoma; Non-melanoma Skin Cancer

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

All

Patients attending a Medical Photography facility with at least 1 suspicious skin lesion will be approached to participate in the study. Participants will have an additional macro and dermoscopic image of each suspicious skin lesions suitable for photography. Photographs will be taken by a healthcare professional using an iPhone XR smart phone camera with a DL1 dermoscopic lens attachment. The images will be encrypted and electronically transmitted to Skin Analytics' cloud servers for analysis by DERM. The suspected diagnosis determined by DERM will be compared with dermatologist review and histologically confirmed diagnosis, where obtained. Healthcare resource utilization information and patient satisfaction data will also be collected

Deep Ensemble for the Recognition of Malignancy (DERM)

Intervention Type: DEVICE

AI-based decision support tool

Interventions

Deep Ensemble for the Recognition of Malignancy (DERM)

AI-based decision support tool

Intervention Type: DEVICE

Eligibility Criteria

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.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Innovate UK

OTHER_GOV

Sponsor Role: collaborator

Skin Analytics Limited

INDUSTRY

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Chelsea and Westminster Hospital

London, , United Kingdom

Countries

United Kingdom

References

Marsden H, Kemos P, Venzi M, Noy M, Maheswaran S, Francis N, Hyde C, Mullarkey D, Kalsi D, Thomas L. Accuracy of an artificial intelligence as a medical device as part of a UK-based skin cancer teledermatology service. Front Med (Lausanne). 2024 Mar 22;11:1302363. doi: 10.3389/fmed.2024.1302363. eCollection 2024.

Reference Type: DERIVED

PMID: 38585154 (View on PubMed)

Other Identifiers

DERM-005

Identifier Type: -

Identifier Source: org_study_id