An observational study in Skin Ageing and Skin, sponsored by Region Skane. Recruiting at 13 sites in 11 countries. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-10-13.
Sponsored by Region Skane · Observational
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.
Skin pigmentation, skin phototype, and photodamage are important determinants of skin cancer risk, dermatological disease presentation, treatment response, and prognosis. They also influence the performance of artificial intelligence (AI) systems developed for dermatological diagnosis and decision support. Despite their clinical importance, these characteristics are commonly assessed using subjective classification systems with limited accuracy and reproducibility. Misclassification occurs both in self-reported and clinician-reported assessments, which may reduce the precision of individualized risk assessments, prevention strategies, and clinical decision-making.
Current classification methods often rely on the Fitzpatrick skin phototype scale and visual assessment of skin pigmentation and photodamage. Although widely used, these approaches have recognized limitations, particularly across diverse populations and skin tones. Objective measurement techniques, such as reflectance spectrophotometry and colorimetry, provide more accurate assessments of skin pigmentation but are not routinely available in clinical practice and do not directly measure phototype or accumulated photodamage. Consequently, there is a need for more robust, scalable, and objective methods to characterize skin pigmentation, phototype, and photodamage.
Recent advances in deep learning have demonstrated high performance in image-based medical applications, including dermatology. However, most dermatological AI systems have focused on lesion detection and classification, while AI-based assessment of fundamental skin characteristics remains underdeveloped. Furthermore, many existing AI systems have been trained on datasets lacking detailed and reliable information on skin pigmentation, phototype, and photodamage, limiting their generalizability and raising concerns regarding fairness and performance across different skin types.
This international multicenter observational study aims to develop and validate deep learning algorithms capable of classifying skin pigmentation, skin phototype, and photodamage from clinical and dermoscopic skin images. The study will recruit adult participants from multiple countries representing a broad spectrum of skin pigmentation levels, phototypes, and sun exposure patterns.
Participants will complete standardized questionnaires, including the Fitzpatrick skin phototype questionnaire and questions related to sun exposure and skin characteristics. Clinical and dermoscopic images will be obtained from predefined anatomical sites on the upper arm and forearm. Skin pigmentation will be assessed using objective measurement methods, including colorimetry and/or spectrophotometry where available, as well as visual classification using the Monk Skin Tone Scale. Trained study personnel will additionally assess Fitzpatrick skin phototype and the degree of photodamage using established clinical scales.
Data collected from questionnaires, objective measurements, visual assessments, and imaging will be combined to create reference standards for skin pigmentation, phototype, and photodamage. Deep learning models will subsequently be developed using the collected clinical and dermoscopic images. Model performance will be evaluated against the reference standards using measures of diagnostic accuracy, including sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve.
The study will also investigate the agreement between patient-reported assessments, clinician assessments, objective pigmentation measurements, and AI-derived classifications. In addition, external validation studies will be performed using independent dermatological image datasets to assess model robustness and generalizability across different populations, geographic regions, and imaging systems.
The anticipated outcome of the study is the development of validated AI algorithms capable of providing standardized and objective assessments of skin pigmentation, phototype, and photodamage. Such tools may support future research, improve characterization of dermatological datasets, facilitate evaluation of AI fairness across skin types, and contribute to more personalized approaches to skin cancer prevention, risk stratification, and dermatological care.
25 studies on the registry are indexed under Photosensitivity Disorders; 4 are open to participants now.
Browse Photosensitivity Disorders studies →Region Skane is the lead sponsor of 386 studies on the registry; 122 are open to participants now.
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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.
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Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Adults aged 18 years and older recruited at participating study sites who undergo standardized clinical and dermoscopic skin imaging, skin pigmentation assessment, skin phototype assessment, photodamage assessment, and questionnaire completion. Data collected from participants will be used to develop and validate artificial intelligence algorithms for classification of skin pigmentation, skin phototype, and photodamage.
Other: Skin imaging and skin characteristic assessment
Participants 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.
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).
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).
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).
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).
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).
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).
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)
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).
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Plan to share: Yes — We plan to publish the image dataset with key measurement data publicly after the completion of the study.
Supporting information: Study protocol, Informed consent form
Time frame: IPD will be shared when the study is completed, 2029-2030.
Access criteria: No limitations. Images, measurement data, basic participant characteristics.
From the registry record's own update history. This site started tracking changes on Sep 25, 2026; for anything earlier, see the record history on ClinicalTrials.gov ↗
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