Artificial Intelligent Decision Support for Skin Cancer Diagnostics in Primary Care

NCT06932172 | Recruiting DMC

• 2 other identifiers: CIV-23-03-0425742023-05996
• Study Type: interventional
• Target: 3,000
• Locations: 1 country
• Sites: 8

Brief Summary

Background: Artificial intelligence has in numerous studies shown high accuracy in detecting skin cancer when trained on various databases of dermatoscopic images. However, there are very few prospective studies conducted in real clinical settings directed at patients seeking healthcare for assessment of skin lesions, and nosuch studies at all in primary care, where the majority of patients are managed. Project aim: To study the accuracy, reliability, and clinical utility of an AI-based decision support system (Dermalyser), developed for primary care, in distinguishing skin cancer from benign lesions. Method: Cluster-randomized controlled trial at approx. 30 primary care centers in Sweden, Germany, Scotland, the Netherlands and Australia. At study start, the participating primary care centres in each country are equally randomised to either be enabled to use the Dermalyser (intervention phase) or to assess patients according to the standard clinical procedure (control phase). When half of the intended sample size (i.e. 1500 of 3000 participants) have been included, the primary care centres switch phase from intervention to control, or vice versa. During the intervention phase, the physicians may use (if found indicated) Dermalyser as a part of their clinical evaluation, whereas during the control phase the physicians follow their ordinary diagnostic routine without support from Dermalyser. This will direct the participants to either an intervention or a control cohort. Both groups will be followed for up to 5 years, with regard to the tumour diagnoses, proportions of skin cancer/benign lesions, and morbidity and mortality in skin cancer. Possible between-group differences will be investigated statistically. Potential benefits: If the Dermalyser prooves to be safe and diagnotically reliability, it could enhance the chance of detecting skin cancer in early stage in primary care, and to reduce the proportion of benign skin lesion unnecessarily excised or referred to dermatologist.

Conditions

Skin Cancer

Keywords

Artificial Intelligence (AI); Primary care; Diagnostics

Outcome Measures

Primary Outcomes (1)

• Proportion of skin cancers

  Proportion of skin cancers (melanoma, SCC or BCC) excised or referred to dermatologists, as described in percentage of total number of skin lesions included.

  From enrollment until all included lesions have been diagnosed following standard clinical investigation procedure, which we consider in the normal case will not exceed 6 months from inclusion.

Study Arms (2)

Possibility to use AI support

EXPERIMENTAL

The primary care physician may use the AI decision support (Dermalyser) in their assessment of skin lesions.

Device: Artificial inteligence based decision support to detect skin cancer

Control

NO INTERVENTION

Interventions

Artificial inteligence based decision support to detect skin cancer DEVICE

When assessing skin lesions in patients seeking primary care, the primary care physician may use the device to be evaluated in the study (Dermalyser) as a complementary diagnostic doecision support to differentiate skin cancers from benign skin lesions. However, the decision on clinical management of the lesion remains with the physician.

Possibility to use AI support

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Patients attending a primary care facility in order to have one or more skin lesions checked for skin cancer, or patients presenting with one or more skin lesions raising suspicion of skin cancer when noticed by the primary care physician.
• Willingness and ability to provide informed consent.

You may not qualify if:

• Individuals with skin type V and VI according to the Fitzpatrick's scale (darker brown or black coloured skin)

Sponsors & Collaborators

• Linkoeping University: lead
• Karolinska Institutet: collaborator
• Göteborg University: collaborator
• Region Östergötland: collaborator

Study Sites (8)

Finspång Primary Healthcare Centre

Finspång, Docent, 61230, Sweden

RECRUITING

Valla Primary Healthcare Centre

Linköping, Docent, 58213, Sweden

RECRUITING

Kärna Primary Care Centre

Linköping, Docent, 58662, Sweden

RECRUITING

Mjölby Primary Care Centre

Mjölby, Docent, 59530, Sweden

RECRUITING

Vikbolandet Primary Care Centre

Norrköping, Docent, 61024, Sweden

RECRUITING

Åby Primary Healthcare Centre

Norrköping, Docent, 61330, Sweden

RECRUITING

Skärvet Primary Healthcare Centre

Vaxjo, Docent, 35234, Sweden

RECRUITING

Ekholmen Primary Healthcare Centre

Linköping, 589 29, Sweden

RECRUITING

Related Publications (2)

• Helenason J, Ekstrom C, Falk M, Papachristou P. Exploring the feasibility of an artificial intelligence based clinical decision support system for cutaneous melanoma detection in primary care - a mixed method study. Scand J Prim Health Care. 2024 Mar;42(1):51-60. doi: 10.1080/02813432.2023.2283190. Epub 2024 Feb 7.

  PMID: 37982736 BACKGROUND

• Papachristou P, Soderholm M, Pallon J, Taloyan M, Polesie S, Paoli J, Anderson CD, Falk M. Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial. Br J Dermatol. 2024 Jun 20;191(1):125-133. doi: 10.1093/bjd/ljae021.

  PMID: 38234043 BACKGROUND

MeSH Terms

Conditions

Skin Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Skin Diseases; Skin and Connective Tissue Diseases

Central Study Contacts

Magnus Falk, Professor

CONTACT

+46708106176; magnus.falk@liu.se

Panos Papachristou, MD, PhD

CONTACT

+46702298892; panos.papachristou@ki.se

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER GOV
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Model Details: Cross-over cluster-randomized clinical trial

Study Record Dates

• First Submitted: January 9, 2025
• First Posted: April 17, 2025
• Study Start: November 4, 2025
• Primary Completion (Estimated): May 31, 2027
• Study Completion (Estimated): December 31, 2031
• Last Updated: January 12, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR
• Time Frame: Prior to study start (protocol, SAP, ICF) and at completion (CSR).
• Access Criteria: Provided a proper description of intended use (e.g. use for meta analysis, including description of study aim and design).

Locations

SE (8)