“Facial Acne Recognition with Artificial Intelligence” Joint Study: CHOLLEY & SUPSI Swiss University

A New Study on the Analysis of Facial Acne using Artificial Intelligence (AI) conducted in 2021 by CHOLLEY Swiss Company with decades of experience in the research & production of skin care products, in collaboration with the Department of Innovative Tehnologies of Supsi Swiss University & funded by INNOSUISSE (Swiss Innovation Agency), has been published in May 2022 in one of the most reputed Scientific Technology Journal DovePress & also in a Dermatology On-line Magazine DermatologyTimes.

No one ever reached this exceptional milestone in the use of Artificial Intelligence (AI) in the analysis of facial acne but today in 2022 this New Study from Switzerland has demonstrated that Artificial Intelligence has important potential in a variety of aspects of acne diagnosis and care.


Acne is a chronic inflammatory disease of the sebaceous gland, caused by elevated testosterone levels andexcessive colonization by Cutibacterium acnes, characterized by follicular hyperkeratinization, which leads to immune reactions and inflammation. Although it is considered a predominantly youthful problem (with 95% of boys and 83% of girls at 16 years old experiencing it), acne actually also affects adult patients, but more particularly women in its two specific forms: late acne (also known as persistent, when emerging in adolescence and continuing to adulthood; accounting for 80% of cases) and late-onset (if first presenting in adulthood). It is believed that genetic and hormonal factors contribute to the pathogenesis of AFA (Adult Female Acne), characterized by chronic evolution, requiring maintenance treatment, in some cases for years. Acne lesions are located on the face, neck, chest and back.

While not a serious disorder, acne, when it occurs in a severe form, can induce unsightly and permanent scarring. Both acne and the resulting scars can negatively affect the psyche. Psychological issues such as dissatisfaction with appearance, embarrassment, self-consciousness, lack of self-confidence, and social dysfunction such as reduced/avoidance of social interactions with peers and opposite gender, reduced employment opportunities have been documented. For these reasons, timely diagnosis and treatment are important and desirable. The enormous spread of the disease, combined with the lack of availability of dermatologists, means that the waiting time for a visit is very long, even over a month.


The scope of this Research has been to develop a Deep Learning System that, using images produced with a mobile device, could make assessments and be as effective as a dermatologist.

Teledermatology is an extremely modern discipline that makes it possible to provide real-time consultancy services to patients wherever they are. In this sense, the progress made in the last decade by the Artificial Intelligence (AI) sector has enabled a number of studies such as this one on teledermatology that were once unthinkable.

This study has examined New Algorithms developed by SUPSI Swiss University & CHOLLEY, and the use of much larger databases of images to train a classification model to recognize whether a patient has acne.

The study carried out on the use of Deep Learning Techniques for the analysis of facial problems related to acne gave good insights.



These multiple figures are an example of preprocessing based on semantic segmentation made by Artificial Intelligence.

First figure on left is the original photo from mobile, central figure is the segmentation map computed by Artificial Intelligence on original photo, 
the final image on right is used with the classification model.


The results of this Research show that the trained classification model developed from CHOLLEY & SUPSI Swiss University achieved a final average score of 60.84% in distinguishing between Acne affected and unaffected faces, result that, if compared to other techniques proposed in the literature, can be considered as state-of-the-art.

Foto Dottor Quattrini

Dr. Andrea Quattrini

Foto Rick

    Eng. Rick Paydar

There is still a long and complex way to go before we have an artificial intelligence-based instrument capable of analyzing dermatological pathologies at the same level as an experienced dermatologist,” said Dr. Andrea Quattrini, one of the Researcher of the Department of Innovative Technologies, Institute of Information Technologies and Networking in SUPSI Swiss University.

Although this study shows that, with good data availability, a series of algorithms can learn to recognize a specific pathology” said Eng. Rick Paydar, the Technical Director of CHOLLEY SA, adding: “It must be considered that many other algorithms would have to be integrated to have a complete assessment of the patient’s skin health”.

Literature Medical Pubblication

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