Skin Pigment Type, Phototype and Photodamage Determination Using Image Analyses Powered by Artificial Intelligence - SPAI Study
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- Region Skane
- 入组人数
- 1,500
- 试验地点
- 11
- 主要终点
- Agreement between AI-derived skin pigmentation (tone) classification and objective skin pigmentation measured by colorimetry/ spectrophotometry (Individual Typology Angle, ITA)
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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
排除标准
- •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
研究组 & 干预措施
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.
干预措施: Skin imaging and skin characteristic assessment (Other)
结局指标
主要结局
Agreement between AI-derived skin pigmentation (tone) classification and objective skin pigmentation measured by colorimetry/ spectrophotometry (Individual Typology Angle, ITA)
时间窗: At completion of model development and testing (approximately 2029).
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.
Accuracy of AI-based skin phototype classification
时间窗: At completion of model development and testing (approximately 2029).
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).
Agreement between AI-derived skin pigmentation (tone) classification and clinician-assessed Monk Skin Tone Scale category
时间窗: At completion of model development and testing (approximately 2029).
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.
Agreement between AI-derived photodamage classification and the Clinical Photonumeric Scale for Photodamage Assessment
时间窗: At completion of model development and testing (approximately 2029).
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.
次要结局
- Agreement between AI-derived facial photodamage classification and the Glogau Photoaging Scale(At completion of model development and testing (approximately 2029).)
- Agreement between AI-derived skin pigmentation classification and participant self-reported skin tone(At completion of model development and testing (approximately 2029).)
- Agreement between AI-derived skin pigmentation classification and observer-reported skin tone(At completion of AI model (approximately 2029))
- Agreement between AI-derived forearm photodamage classification and the Forearm Skin Photoaging Scale(At completion of model development and testing (approximately 2029).)