Skin AgeingSPAINCT07765303
Skin Type Determination Using Image Artificial Intelligence
An observational study of Skin Ageing and Skin, run by Region Skane at 13 sites in 11 countries.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Skin Ageing, Skin
- What is being tested?
- Skin imaging and skin characteristic assessment
- How many people
- 1,500 participants
- Where
- 13 sitesin 11 countries
- Study dates
- 28 Apr 2025to 31 Dec 2028
- How long will I take part?
- 1 Day
- Phase
- Observational
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study 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.
Who may be able to join
- 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
Who cannot join
- 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 locations and contacts
13 sites in 11 countries. Contact the study team or a site near you to ask about taking part.
13 sites in 11 countries
Chile2 sites, 2 recruiting
- Not yet recruiting
Clinica Universidad de los Andes
Santiago
Pilar Bofill, MD PhD, +56 2 2618 3100
- Recruiting
Hospital Clínico Universidad de Chile
Santiago
Irene Araya, MD PhD, +56 2 2978 8000
Sweden2 sites, 2 recruiting
- Recruiting
Department of Dermatology, Sahlgrenska University Hospital
Gothenburg, Västra Götaland County
Magdalena Claeson, MD PhD, +46 31 342 10 00, magdalena.claeson@vgregion.se
- Recruiting
Department of Dermatology Lund, Skåne University Hospital
Lund, Skåne County, 22185
Åsa Ingvar, MD PhD, +46 46 172243, asa.ingvar@skane.se
Brazil1 site, 1 recruiting
- Recruiting
Hospital de Clínicas de Porto Alegre
Porto Alegre
Leandro Linhares Leite, MD PhD, +55 51 3359-8000
China1 site, 1 recruiting
- Not yet recruiting
Xiangya Hospital, Central South University
Changsha
Yi Xiao, MD PhD, +86 731 8432 8888
Denmark1 site, 1 recruiting
- Not yet recruiting
Department of Dermatology, Odense University Hospital
Odense
Tine Vestergaard, MD PhD, +45 66 11 33 33
Malawi1 site, 1 recruiting
- Not yet recruiting
Queen Elisabeth Central Hospital
Blantyre
Kelvin Mponda, MD PhD, +265 1 874 333
Spain1 site
- Completed
Department of Dermatology, Hospital Universitario 12 de Octubre
Madrid
Sri Lanka1 site, 1 recruiting
- Enrolling by invitation
University of Colombo
Colombo
Thailand1 site, 1 recruiting
- Recruiting
Siriraj Hospital, Mahidol University
Bangkok
Rungsima Wanitphakdeedecha, MD PhD, +66 2 419 1000
About this study
Official title: Skin Pigment Type, Phototype and Photodamage Determination Using Image Analyses Powered by Artificial Intelligence - SPAI Study
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.
Read more
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.
Read the detailed description
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.
- Conditions
- Skin Ageing, Skin
- Being tested
- Skin imaging and skin characteristic assessment
- Keywords
- skin tone, pigmentation, skin color, chronic sun damage, photodamage, sun sensitivity, phototype, Fitzpatrick type
- Medical subject headings
- Photosensitivity Disorders
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Skin imaging and skin characteristic assessmentOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Participants undergoing skin imaging and skin characteristic assessment | 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. Receives: Other: Skin imaging and skin characteristic assessment |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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).
Measured: 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.
Measured: 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.
Measured: At completion of model development and testing (approximately 2029).
Secondary outcomes (4)
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: At completion of model development and testing (approximately 2029).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Aug 2026
- Study start28 Apr 2025
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (18)Papers the sponsor has linked to this study.
- Other trials for Skin AgeingStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 1,500 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Region Skane
- Sponsor type
- University, hospital or other
- Collaborators
- Sahlgrenska University Hospital, Odense University Hospital, Hospital Universitario 12 de Octubre, Queen Elizabeth Central Hospital, Blantyre, Malawi, University of Chile, Universidad de los Andes, Chile, Mahidol University, Monash University, The University of Queensland, Erasmus University Rotterdam, Xiangya Hospital of Central South University, University of Colombo
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07765303
- Sponsor's protocol ID
- 2024-07010-01
- Other IDs
- 3655/2024, 3756/2025
- Acronym
- SPAI
- Status last verified
- Jun 2026
- First submitted
- 15 Jun 2026
- Last updated
- 13 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: We plan to publish the image dataset with key measurement data publicly after the completion of the study.
Publications
Liu X, Sangers TE, Nijsten T, Kayser M, Pardo LM, Wolvius EB, Roshchupkin GV, Wakkee M. Predicting skin cancer risk from facial images with an explainable artificial intelligence (XAI) based approach: a proof-of-concept study. EClinicalMedicine. 2024 Mar 19;71:102550. doi: 10.1016/j.eclinm.2024.102550. eCollection 2024 May.
PubMed 38545426Haggenmuller S, Maron RC, Hekler A, Utikal JS, Barata C, Barnhill RL, Beltraminelli H, Berking C, Betz-Stablein B, Blum A, Braun SA, Carr R, Combalia M, Fernandez-Figueras MT, Ferrara G, Fraitag S, French LE, Gellrich FF, Ghoreschi K, Goebeler M, Guitera P, Haenssle HA, Haferkamp S, Heinzerling L, Heppt MV, Hilke FJ, Hobelsberger S, Krahl D, Kutzner H, Lallas A, Liopyris K, Llamas-Velasco M, Malvehy J, Meier F, Muller CSL, Navarini AA, Navarrete-Dechent C, Perasole A, Poch G, Podlipnik S, Requena L, Rotemberg VM, Saggini A, Sangueza OP, Santonja C, Schadendorf D, Schilling B, Schlaak M, Schlager JG, Sergon M, Sondermann W, Soyer HP, Starz H, Stolz W, Vale E, Weyers W, Zink A, Krieghoff-Henning E, Kather JN, von Kalle C, Lipka DB, Frohling S, Hauschild A, Kittler H, Brinker TJ. Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts. Eur J Cancer. 2021 Oct;156:202-216. doi: 10.1016/j.ejca.2021.06.049. Epub 2021 Sep 8.
PubMed 34509059Dadzie OE, Sturm RA, Fajuyigbe D, Petit A, Jablonski NG. The Eumelanin Human Skin Colour Scale: a proof-of-concept study. Br J Dermatol. 2022 Jul;187(1):99-104. doi: 10.1111/bjd.21277. Epub 2022 May 8.
PubMed 35349165Daneshjou R, Barata C, Betz-Stablein B, Celebi ME, Codella N, Combalia M, Guitera P, Gutman D, Halpern A, Helba B, Kittler H, Kose K, Liopyris K, Malvehy J, Seog HS, Soyer HP, Tkaczyk ER, Tschandl P, Rotemberg V. Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology: CLEAR Derm Consensus Guidelines From the International Skin Imaging Collaboration Artificial Intelligence Working Group. JAMA Dermatol. 2022 Jan 1;158(1):90-96. doi: 10.1001/jamadermatol.2021.4915.
PubMed 34851366Daneshjou R, Smith MP, Sun MD, Rotemberg V, Zou J. Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms: A Scoping Review. JAMA Dermatol. 2021 Nov 1;157(11):1362-1369. doi: 10.1001/jamadermatol.2021.3129.
PubMed 34550305Steele L, Tan XL, Olabi B, Gao JM, Tanaka RJ, Williams HC. Determining the clinical applicability of machine learning models through assessment of reporting across skin phototypes and rarer skin cancer types: A systematic review. J Eur Acad Dermatol Venereol. 2023 Apr;37(4):657-665. doi: 10.1111/jdv.18814. Epub 2023 Jan 2.
PubMed 36514990Williams JR, Frey C, Cohen GF. Cutaneous Sarcoidosis in Skin of Color. J Drugs Dermatol. 2023 Jul 1;22(7):695-697. doi: 10.36849/JDD.7008.
PubMed 37410043Hogue L, Harvey VM. Basal Cell Carcinoma, Squamous Cell Carcinoma, and Cutaneous Melanoma in Skin of Color Patients. Dermatol Clin. 2019 Oct;37(4):519-526. doi: 10.1016/j.det.2019.05.009.
PubMed 31466591Guitera P, Menzies SW, Argenziano G, Longo C, Losi A, Drummond M, Scolyer RA, Pellacani G. Dermoscopy and in vivo confocal microscopy are complementary techniques for diagnosis of difficult amelanotic and light-coloured skin lesions. Br J Dermatol. 2016 Dec;175(6):1311-1319. doi: 10.1111/bjd.14749. Epub 2016 Oct 12.
PubMed 27177158Ebede T, Papier A. Disparities in dermatology educational resources. J Am Acad Dermatol. 2006 Oct;55(4):687-90. doi: 10.1016/j.jaad.2005.10.068.
PubMed 17010750Lindqvist PG, Landin-Olsson M, Olsson H. Low sun exposure habits is associated with a dose-dependent increased risk of hypertension: a report from the large MISS cohort. Photochem Photobiol Sci. 2021 Feb;20(2):285-292. doi: 10.1007/s43630-021-00017-x. Epub 2021 Feb 18.
PubMed 33721253Taylor SC, Arsonnaud S, Czernielewski J; Hyperpigmentation Scale Study Group. The Taylor Hyperpigmentation Scale: a new visual assessment tool for the evaluation of skin color and pigmentation. Cutis. 2005 Oct;76(4):270-4.
PubMed 16315565English DR, Armstrong BK, Kricker A, Winter MG, Heenan PJ, Randell PL. Demographic characteristics, pigmentary and cutaneous risk factors for squamous cell carcinoma of the skin: a case-control study. Int J Cancer. 1998 May 29;76(5):628-34. doi: 10.1002/(sici)1097-0215(19980529)76:53.0.co;2-s.
PubMed 9610717Tan K, Lo SN, Cust AE, Wolfe R, Mar V. Sensitivity of two Australian melanoma risk tools to identify high-risk individuals among people presenting with their first primary melanoma. Australas J Dermatol. 2022 Aug;63(3):352-358. doi: 10.1111/ajd.13841. Epub 2022 May 6.
PubMed 35522684Vuong K, Armstrong BK, Drummond M, Hopper JL, Barrett JH, Davies JR, Bishop DT, Newton-Bishop J, Aitken JF, Giles GG, Schmid H, Jenkins MA, Mann GJ, McGeechan K, Cust AE. Development and external validation study of a melanoma risk prediction model incorporating clinically assessed naevi and solar lentigines. Br J Dermatol. 2020 May;182(5):1262-1268. doi: 10.1111/bjd.18411. Epub 2019 Sep 22.
PubMed 31378928