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iToBoS Prospective Data Acquisition and Feasibility Study (Barcelona)

iToBoS Prospective Data Acquisition Study

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05955443
Acronym
iToBoS BCN
Enrollment
350
Registered
2023-07-21
Start date
2023-01-13
Completion date
2024-12-20
Last updated
2024-06-25

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Skin Cancer

Keywords

melanoma, skin examination, artificial intelligence, total body photography

Brief summary

This study is recruiting patients at their regular skin examination appointments to participate in research. Participation involves having 3D total body photography, completing a 10-15 minute questionnaire, and providing a genetic sample. Normally, the total body photography is part of the patients standard care, as is the collection of a genetic sample. Consenting to this study involves consenting to the use of total body photography images (de-identified), questionnaire answers, and genetic risk information to be used for developing AI algorithms for image analysis of skin lesions, and melanoma-risk profiling for patients.

Detailed description

Patients are invited to participate at their regular skin examination visits at Hospital Clinic Barcelona. A member of the research team will read through the Participant Information and Consent Form (PICF) with the patient and answer any questions they may have. Once consent is given, the following study activities will take place. 1. Total Body Photography: this is done with the VECTRA machine that will require you to be photographed in your underwear. The VECTRA is a framework of 92 cameras which will simultaneously capture images of a patient holding one anatomical pose. VECTRA computer software is then used to construct a 3D avatar of the patient, enabling the image record of skin naevi. This imaging is normally part of the patient's skin examination visit. The images used for annotation in research will be de-identified (anonynmised) by firstly removing any images of the face and other identifying features (e.g. tattoos, scars, nipples), then processing the 3D avatar into small 2D images of 5x8cm section of skin. These methods are designed to protect patient privacy of images used for AI development. 2. Complete a Questionnaire: consists of 45 items/questions, taking approximately 10 minutes to complete. Questionnaire data is designed to collect information relevant to melanoma risk, while maintaining a low risk of being individually identifiable. topics cover include demographics, skin cancer history, sun/health behaviour, and phenotype information (e.g. hair/skin/eye colour). 3. Genetic sample: in some cases the hospital will already hold a genetic sample, in this case we ask patient consent to access this information. When no sample is on record, we will ask the patient to provide a saliva or blood sample for genetic analysis. Genetic information will be analysed by looking for single nucleotide polymorphisms (SNPs) relating to melanoma risk (approximately 80-100 SNPs to be analysed). A polygenic risk score (PRS) is then calculated based on the presence or absence of relevant SNPs. The final PRS will be used in the study (no individual genetic information will be shared outside the hospital. All de-identified data (image, survey, and PRS) are then used by the iToBoS consortium to develop a cognitive assistant (AI model) to provide clinician support in screening for melanoma using total body photography.

Interventions

None listed

Sponsors

Fundacion Clinic per a la Recerca Biomédica
CollaboratorOTHER
The University of Queensland
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes

Inclusion criteria

* Over 18 * Able to provide informed consent

Exclusion criteria

* Unable to stand and hold anatomica pose for 3D total body photography

Design outcomes

Primary

MeasureTime frameDescription
Incidence of skin diagnostic categoriesThrough study completion, an average of 1 yearImages will be used to label different skin diagnostic categories. These labelled images are then used as a training dataset for machine learning in developing algorithms for skin lesion diagnosis.

Countries

Spain

Contacts

Primary ContactClare A Primiero, PhD
c.bover@uq.edu.au+34639222592
Backup ContactJosep Malvehy, MD
jmalvehy@clinic.cat+34609660505

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026