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Development of an Aid to Melanoma Detection Using Artificial Intelligence Algorithms Based on Images From the VECTRA 3D System.

Development of Artificial Intelligence Algorithms to Help Detect Potential Melanomas, Using Images From the VECTRA 3D Whole Body 360 Imaging System, a 3D Whole-body Skin Scanner.

Status
Not yet recruiting
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT06999499
Acronym
SELF DETECT
Enrollment
1000
Registered
2025-05-31
Start date
2026-03-01
Completion date
2028-06-01
Last updated
2025-11-18

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

Conditions

Naevi

Brief summary

The background to this research is that frequent medical screening of the general population for melanoma is not feasible. The real challenge of this project is to develop an automatic process for detecting any potential melanoma. To this end, the project aims to design an algorithm to build a novel diagnostic aid that makes use of the similarity and disparity of pigmented lesions in the same patient. To achieve this, we need to obtain and structure a large database of images grouping all pigmented lesions per patient according to their similarities as perceived by dermatologists.

Interventions

OTHERScanner of the whole body using the VECTRA 3D

Whole body image acquisition using the VECTRA 3D Whole Body 360 Imaging System to detect potential melanoma

Sponsors

Assistance Publique Hopitaux De Marseille
Lead SponsorOTHER

Study design

Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE

Eligibility

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

Inclusion criteria

* Male or female aged 18 and over * Patient with more than 15 nevi (moles) of various phototypes (I to III) * Patient who has received information about the study and has not expressed any opposition * Patient who is a beneficiary or entitled person under a social security scheme

Exclusion criteria

* Patients with phototype V * Patients with chronic inflammatory skin diseases * Claustrophobic patients * Patients who are bedridden or handicapped * Patients who are excluded from another research protocol at the time of collection of the non-objection. * Patients covered by articles L1121-5 to 1121-8 of the French Public Health Code (minors, adults under guardianship or trusteeship, patients deprived of their liberty, pregnant or breast-feeding women), * Any other reason which, in the investigator's opinion, could interfere with the evaluation of the research objectives.

Design outcomes

Primary

MeasureTime frameDescription
development and validation of algorithms to identify lesions clinically suspected of being melanoma by a dermatologist (potentially malignant and/or ugly duckling).from enrollement to until 6 monthComparison of the results given by the analysis of the images by 3 dermatologists or by the software. Estimation of sensitivity and specificity thresholds of at least 93% (accuracy level 5%).

Secondary

MeasureTime frameDescription
concordance rate for malignant annotationsFrom enrollement to 6 month afterconcordance between the malignant yes-no annotations of each of the three dermatologists (2 to 2) will be tested.
concordance rate for ugly ducklingFrom enrollement to 6 month afterconcordance between the ugly duckling yes-no annotations of each of the three dermatologists (2 to 2) will be tested.
calculation of the proportion of melanomas confirmed by anatomopathologyFrom enrollement to 6 month afterFor each lesion identified as malignant or ugly duckling by the gold standard and removed for histological analysis: calculation of the proportion of melanomas confirmed by pathology.

Countries

France

Contacts

Primary ContactJilliana MONNIER Dr
promotion.interne@ap-hm.fr0491435817

Outcome results

None listed

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