Skin Type Determination Using Image Artificial Intelligence
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorRegion Skane
About this trial
Skin color, how easily a person burns or tans in the sun (skin phototype), and the amount of chronic sun damage in the skin are important factors in skin health. These characteristics influence a person's risk of skin cancer, how skin diseases appear, how well treatments work, and how accurately doctors and artificial intelligence (AI) systems can diagnose skin conditions. However, current methods for classifying these characteristics are often imprecise and rely heavily on subjective assessments. As a result, both healthcare professionals and patients may incorrectly classify skin type, which can lead to inaccurate risk assessments and less personalized care.
This study aims to develop and validate AI algorithms that can accurately classify skin pigmentation, skin phototype, and accumulated sun damage using photographs of the skin. Unlike existing approaches, the study combines several different methods to create a more objective "ground truth" for training the AI. These methods include skin color measurements using spectrophotometry or colorimetry, assessments using the Monk Skin Tone Scale, questionnaires about sun sensitivity, and clinical evaluations by trained observers. By combining these data sources, the researchers hope to create a more reliable and scientifically robust classification system.
The study will recruit adults aged 18 years and older from several countries, including countries from all continents. Participants will complete a questionnaire about their skin, propensity to burn and sun exposure history. Researchers will then take standardized close-up and dermoscopic images of the skin on the arm and forearm, measure skin pigmentation using objective instruments when available, and assess skin phototype and sun damage. No invasive procedures will be performed, and no personally identifiable information will be collected.
The collected images and measurements will be used to train deep learning AI models. The researchers aim to develop algorithms that can classify skin pigmentation with at least 85% accuracy, skin phototype with at least 75% accuracy, and sun damage with at least 80% accuracy compared with the combined reference assessments. The algorithms will then be tested in independent datasets, including large dermatology image databases from Sweden, to evaluate how well they perform in different populations.
The study has several potential benefits. More accurate classification of skin characteristics could improve personalized skin cancer risk assessments and allow prevention advice to be tailored to individual needs. This may help identify people who would benefit from closer surveillance and stronger sun protection recommendations while avoiding unnecessary restrictions for people at lower risk. Improved classification could also enhance the diagnosis and management of inflammatory skin diseases and skin cancers, which can appear differently in people with different skin tones.
An additional goal is to address known biases in dermatology AI systems, which often perform less accurately in individuals with darker skin. By including participants with a wide range of skin tones and backgrounds, the researchers aim to contribute to the benchmarking of AI-driven medical devices wich hopefully can result in the development of fairer and more equitable AI tools.
The study involves minimal risk. Only photographs of the arm and forearm will be taken, and researchers will avoid capturing tattoos, prominent scars, or other identifying features. All data will be stored securely and only accessible to authorized researchers. The potential benefits of improving skin disease diagnosis, skin cancer prevention, and fairness in medical AI are considered to outweigh the small privacy risks associated with participation.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
Aged 18 years or older
Able and willing to provide informed consent (oral or written, according to local regulations)
Willing to complete the study questionnaire
Willing to undergo non-invasive skin imaging and skin characteristic assessments of predefined sites on the upper arm and forearm
Disqualifiers
Younger than 18 years of age
Unable to provide informed consent
Unable to complete study procedures
Tattoos, prominent scars, wounds, skin lesions, dressings, or other identifiable features at the predefined imaging sites that may interfere with image acquisition, assessment quality, or participant anonymity
Trial population
Adults aged 18 years and older will be recruited from dermatology clinics, hospital waiting areas, universities, and other public settings at participating study sites (at present 11 but more are being recruited). The study aims to include individuals representing a broad range of skin pigmentation levels, skin phototypes, and degrees of photodamage. Participants will undergo non-invasive skin imaging, skin characteristic assessments, and questionnaire-based data collection to support the development and validation of artificial intelligence algorithms for classification of skin pigmentation, phototype, and photodamage.
Trial design
Design model
Other
Time perspective
Cross-sectional
Treatments tested in this trial
Skin imaging and skin characteristic assessment
Other interventionParticipants undergo standardized clinical and dermoscopic skin imaging, skin pigmentation measurements, skin phototype assessments, photodamage assessments, and completion of questionnaires. Data are collected for the development and validation of artificial intelligence algorithms for classification of skin pigmentation, phototype, and photodamage.
Treatment groups
1,500 Participants
are divided into 1 treatment groupGroup A: Participants undergoing skin imaging and skin characteristic assessment1 intervention
Trial outcomes
Primary outcomes
1
Agreement between AI-derived skin pigmentation (tone) classification and objective skin pigmentation measured by colorimetry/ spectrophotometry (Individual Typology Angle, ITA)
Skin pigmentation will be measured objectively using spectrophotometry/ colorimetry and summarized as the Individual Typology Angle (ITA). AI-derived skin pigmentation classification will be compared with ITA values using correlation and agreement analyses. ITA is considered the primary reference standard for objective assessment of skin pigmentation in the interpretation of AI performance.
Time frame
At completion of model development and testing (approximately 2029).2
Accuracy of AI-based skin phototype classification
Accuracy of the deep learning algorithm in classifying skin phototype from clinical and dermoscopic images compared with the reference standard based on the validated Fitzpatrick skin phototype assessment (consisting of 6 categories).
Time frame
At completion of model development and testing (approximately 2029).3
Agreement between AI-derived skin pigmentation (tone) classification and clinician-assessed Monk Skin Tone Scale category
Skin pigmentation will be assessed visually by trained investigators using the Monk Skin Tone Scale (categories 1 (fair) to 10 (dark)). AI-derived classifications will be compared with clinician-assigned Monk categories using agreement and correlation analyses. The Monk Skin Tone Scale represents the principal visual reference standard for skin tone classification.
Time frame
At completion of model development and testing (approximately 2029).4
Agreement between AI-derived photodamage classification and the Clinical Photonumeric Scale for Photodamage Assessment
Photodamage will be assessed using the validated Clinical Photonumeric Scale (0-3 for 3 defined pigmentation categories) for Photodamage Assessment. AI-derived photodamage classifications will be compared with the photonumeric scale scores using agreement and correlation analyses. This outcome evaluates the agreement between AI-derived classifications and a validated photonumeric clinical assessment of photodamage.
Time frame
At completion of model development and testing (approximately 2029).Secondary outcomes
1
Agreement between AI-derived facial photodamage classification and the Glogau Photoaging Scale
Facial photodamage will be assessed by trained investigators using the Glogau Photoaging Scale (1-4). AI-derived photodamage classifications will be compared with Glogau categories using agreement and correlation analyses. The Glogau Photoaging Scale is a widely used clinical grading system for the assessment of facial photoaging.
Time frame
At completion of model development and testing (approximately 2029).2
Agreement between AI-derived skin pigmentation classification and participant self-reported skin tone
Participants will self-classify their skin tone using the a 5-category skin tone scale for constiutive and facultative skin tone derived from the validated Fitzpatrick skin type questionnaire. Agreement between AI-derived classifications and participant self-reported skin tone will be evaluated using correlation and agreement analyses. This outcome will assess whether AI reflects participants' own perception of their skin tone.
Time frame
At completion of model development and testing (approximately 2029).3
Agreement between AI-derived skin pigmentation classification and observer-reported skin tone
Observers will perform a visual assessment of participant complexion and classify participants into one of the 6 Fitzpatrick skin types. Agreement between AI-derived classifications and observer assessments will be evaluated using correlation and agreement analyses. This outcome will determine whether AI performs comparably to routine clinical visual assessment.
Time frame
At completion of AI model (approximately 2029)4
Agreement between AI-derived forearm photodamage classification and the Forearm Skin Photoaging Scale
Photodamage of the dorsal forearm will be assessed by trained investigators using the Forearm Skin Photoaging Scale. This scale is validated and includes assessments of wrinkles (0-4), lentigines (0-4), hypochromias (0-4), actinic keratoses (superficial and hypertrophic, each assessed at a scal 0-4), stellate pseudoscars (0-1), visible veins (0-1), visible purpura (0-1). elastosis (0-2) and loss of elasticity (0-2). AI-derived photodamage classifications will be compared both with the individual measures in the Forearm Skin Photoaging Scale but also to the aggregated score (wrinkle score x9 + lentigines score x4 + actinic keratoses superficial score x4 + actinic keratosis hypertrophic score x1 + visible purpura score x2+ psedoscar score x4 + elastosis score x8 + loss of elasticity score x 16) using agreement and correlation analyses. This outcome evaluates AI performance for assessing chronic sun-induced photodamage of the forearm.
Time frame
At completion of model development and testing (approximately 2029).Sponsors and contacts
Click on the lead sponsor to view all of their trials.
Region Skane
Lead sponsor
Sahlgrenska University Hospital
Collaborator
Odense University Hospital
Collaborator
Hospital Universitario 12 de Octubre
Collaborator
Queen Elizabeth Central Hospital, Blantyre, Malawi
Collaborator
University of Chile
Collaborator
Universidad de los Andes, Chile
Collaborator
Mahidol University
Collaborator
Monash University
Collaborator
The University of Queensland
Collaborator
Erasmus University Rotterdam
Collaborator
Xiangya Hospital of Central South University
Collaborator
University of Colombo
Collaborator
Source ClinicalTrials.gov