Skin Type Determination Using Image Artificial Intelligence

Skin, Skin Ageing

skin tone, pigmentation, skin color, chronic sun damage, photodamage, sun sensitivity, phototype, Fitzpatrick type

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.

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