[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100651900":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":10,"centralContacts":19,"locations":29,"responsibleParty":177,"collaborators":179,"id":204,"slug":205,"hasResults":206,"nctId":207,"briefTitle":208,"officialTitle":209,"acronym":210,"eligibilityCriteria":211,"healthyVolunteers":212,"sex":213,"minAge":214,"maxAge":10,"enrollmentInfo":215,"targetDuration":218,"studyType":219,"phases":10,"briefSummary":220,"conditions":221,"keywords":224,"overallStatus":32,"whyStopped":10,"lastUpdateSubmitDate":233,"lastUpdatePostDateStruct":234,"startDateStruct":237,"completionDateStruct":239,"leadSponsor":241,"locationsCount":242},{"fullName":5,"class":6},"Region Skane","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Participants undergoing skin imaging and skin characteristic assessment",null,"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.",[13],"Other: Skin imaging and skin characteristic assessment",[15],{"type":6,"name":16,"description":17,"armGroupLabels":18,"otherNames":10},"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.",[9],[20,25],{"name":21,"role":22,"phone":23,"phoneExt":10,"email":24},"Åsa Ingvar, MD PhD","CONTACT","+46 46172243","asa.ingvar@skane.se",{"name":26,"role":22,"phone":27,"phoneExt":10,"email":28},"Anna Asphult, Research nurse","+46 46 172113","teleforskning.hud.sus@skane.se",[30,46,61,70,84,98,112,123,134,149,163],{"facility":31,"status":32,"city":33,"state":10,"zip":10,"country":34,"countryCode":35,"cosmosGeoPoint":36,"geoPoint":41,"contacts":42},"Hospital de Clínicas de Porto Alegre","RECRUITING","Porto Alegre","Brazil","BR",{"type":37,"coordinates":38},"Point",[39,40],-51.23019,-30.03283,{"lat":40,"lon":39},[43],{"name":44,"role":22,"phone":45,"phoneExt":10,"email":10},"Leandro Linhares Leite, MD PhD","+55 51 3359-8000",{"facility":47,"status":48,"city":49,"state":10,"zip":10,"country":50,"countryCode":51,"cosmosGeoPoint":52,"geoPoint":56,"contacts":57},"Clinica Universidad de los Andes","NOT_YET_RECRUITING","Santiago","Chile","CL",{"type":37,"coordinates":53},[54,55],-70.64827,-33.45694,{"lat":55,"lon":54},[58],{"name":59,"role":22,"phone":60,"phoneExt":10,"email":10},"Pilar Bofill, MD PhD","+56 2 2618 3100",{"facility":62,"status":32,"city":49,"state":10,"zip":10,"country":50,"countryCode":51,"cosmosGeoPoint":63,"geoPoint":65,"contacts":66},"Hospital Clínico Universidad de Chile",{"type":37,"coordinates":64},[54,55],{"lat":55,"lon":54},[67],{"name":68,"role":22,"phone":69,"phoneExt":10,"email":10},"Irene Araya, MD PhD","+56 2 2978 8000",{"facility":71,"status":48,"city":72,"state":10,"zip":10,"country":73,"countryCode":74,"cosmosGeoPoint":75,"geoPoint":79,"contacts":80},"Xiangya Hospital, Central South University","Changsha","China","CN",{"type":37,"coordinates":76},[77,78],112.97087,28.19874,{"lat":78,"lon":77},[81],{"name":82,"role":22,"phone":83,"phoneExt":10,"email":10},"Yi Xiao, MD PhD","+86 731 8432 8888",{"facility":85,"status":48,"city":86,"state":10,"zip":10,"country":87,"countryCode":88,"cosmosGeoPoint":89,"geoPoint":93,"contacts":94},"Department of Dermatology, Odense University Hospital","Odense","Denmark","DK",{"type":37,"coordinates":90},[91,92],10.38831,55.39594,{"lat":92,"lon":91},[95],{"name":96,"role":22,"phone":97,"phoneExt":10,"email":10},"Tine Vestergaard, MD PhD","+45 66 11 33 33",{"facility":99,"status":48,"city":100,"state":10,"zip":10,"country":101,"countryCode":102,"cosmosGeoPoint":103,"geoPoint":107,"contacts":108},"Queen Elisabeth Central Hospital","Blantyre","Malawi","MW",{"type":37,"coordinates":104},[105,106],35.00854,-15.78499,{"lat":106,"lon":105},[109],{"name":110,"role":22,"phone":111,"phoneExt":10,"email":10},"Kelvin Mponda, MD PhD","+265 1 874 333",{"facility":113,"status":114,"city":115,"state":10,"zip":10,"country":116,"countryCode":117,"cosmosGeoPoint":118,"geoPoint":122,"contacts":10},"Department of Dermatology, Hospital Universitario 12 de Octubre","COMPLETED","Madrid","Spain","ES",{"type":37,"coordinates":119},[120,121],-3.70256,40.4165,{"lat":121,"lon":120},{"facility":124,"status":125,"city":126,"state":10,"zip":10,"country":127,"countryCode":128,"cosmosGeoPoint":129,"geoPoint":133,"contacts":10},"University of Colombo","ENROLLING_BY_INVITATION","Colombo","Sri Lanka","LK",{"type":37,"coordinates":130},[131,132],79.84868,6.93548,{"lat":132,"lon":131},{"facility":135,"status":32,"city":136,"state":137,"zip":138,"country":139,"countryCode":140,"cosmosGeoPoint":141,"geoPoint":145,"contacts":146},"Department of Dermatology Lund, Skåne University Hospital","Lund","Skåne County","22185","Sweden","SE",{"type":37,"coordinates":142},[143,144],13.19321,55.70584,{"lat":144,"lon":143},[147],{"name":21,"role":22,"phone":148,"phoneExt":10,"email":24},"+46 46 172243",{"facility":150,"status":32,"city":151,"state":152,"zip":10,"country":139,"countryCode":140,"cosmosGeoPoint":153,"geoPoint":157,"contacts":158},"Department of Dermatology, Sahlgrenska University Hospital","Gothenburg","Västra Götaland County",{"type":37,"coordinates":154},[155,156],11.96679,57.70716,{"lat":156,"lon":155},[159],{"name":160,"role":22,"phone":161,"phoneExt":10,"email":162},"Magdalena Claeson, MD PhD","+46 31 342 10 00","magdalena.claeson@vgregion.se",{"facility":164,"status":32,"city":165,"state":10,"zip":10,"country":166,"countryCode":167,"cosmosGeoPoint":168,"geoPoint":172,"contacts":173},"Siriraj Hospital, Mahidol University","Bangkok","Thailand","TH",{"type":37,"coordinates":169},[170,171],100.50144,13.75398,{"lat":171,"lon":170},[174],{"name":175,"role":22,"phone":176,"phoneExt":10,"email":10},"Rungsima Wanitphakdeedecha, MD PhD","+66 2 419 1000",{"type":178,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR",[180,182,184,186,189,191,193,195,197,199,201,203],{"name":181,"class":6},"Sahlgrenska University Hospital",{"name":183,"class":6},"Odense University Hospital",{"name":185,"class":6},"Hospital Universitario 12 de Octubre",{"name":187,"class":188},"Queen Elizabeth Central Hospital, Blantyre, Malawi","UNKNOWN",{"name":190,"class":6},"University of Chile",{"name":192,"class":6},"Universidad de los Andes, Chile",{"name":194,"class":6},"Mahidol University",{"name":196,"class":6},"Monash University",{"name":198,"class":6},"The University of Queensland",{"name":200,"class":6},"Erasmus University Rotterdam",{"name":202,"class":6},"Xiangya Hospital of Central South University",{"name":124,"class":6},"100651900","skin-type-determination-using-image-artificial-intelligence-100651900",false,"NCT07765303","Skin Type Determination Using Image Artificial Intelligence","Skin Pigment Type, Phototype and Photodamage Determination Using Image Analyses Powered by Artificial Intelligence - SPAI Study","SPAI","Inclusion Criteria:\n\n* Aged 18 years or older\n* Able and willing to provide informed consent (oral or written, according to local regulations)\n* Willing to complete the study questionnaire\n* Willing to undergo non-invasive skin imaging and skin characteristic assessments of predefined sites on the upper arm and forearm\n\nExclusion Criteria:\n\n* Younger than 18 years of age\n* Unable to provide informed consent\n* Unable to complete study procedures\n* 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",true,"ALL","18 Years",{"count":216,"type":217},1500,"ESTIMATED","1 Day","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.\n\nThis 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.\n\nThe 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.\n\nThe 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.\n\nThe 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.\n\nAn 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.\n\nThe 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.",[222,223],"Skin Ageing","Skin",[225,226,227,228,229,230,231,232],"skin tone","pigmentation","skin color","chronic sun damage","photodamage","sun sensitivity","phototype","Fitzpatrick type","2026-08-10",{"date":235,"type":236},"2026-08-14","ACTUAL",{"date":238,"type":236},"2025-04-28",{"date":240,"type":217},"2028-12-31",{"name":5,"class":6},11]