[Artificial intelligence to support telemedicine in Africa]. / Künstliche Intelligenz zur Unterstützung der Telemedizin am Beispiel Afrikas.

Telemedicine has been used in the daily routine of dermatologists for decades. The potential advantages are especially obvious in African countries having limited medical care, long geographical distances, and a meanwhile relatively well-developed telecommunication sector. National and international working groups support the establishment of teledermatological projects and in recent years have increasingly been using artificial intelligence (AI)-based technologies to support the local physicians. Ethnic variations represent a challenge in the development of automated algorithms. To further improve the accuracy of the systems and to be able to globalize, it is important to increase the amount of available clinical data. This can only be achieved with the active participation of local health care providers as well as the dermatological community and must always be in the interest of the individual patient.

Observer-independent assessment of psoriasis-affected area using machine learning.

BACKGROUND: Assessment of psoriasis severity is strongly observer-dependent, and objective assessment tools are largely missing. The increasing number of patients receiving highly expensive therapies that are reimbursed only for moderate-to-severe psoriasis motivates the development of higher quality assessment tools. OBJECTIVE: To establish an accurate and objective psoriasis assessment method based on segmenting images by machine learning technology. METHODS: In this retrospective, non-interventional, single-centred, interdisciplinary study of diagnostic accuracy, 259 standardized photographs of Caucasian patients were assessed and typical psoriatic lesions were labelled. Two hundred and three of those were used to train and validate an assessment algorithm which was then tested on the remaining 56 photographs. The results of the algorithm assessment were compared with manually marked area, as well as with the affected area determined by trained dermatologists. RESULTS: Algorithm assessment achieved accuracy of more than 90% in 77% of the images and differed on average 5.9% from manually marked areas. The difference between algorithm-predicted and photograph-based estimated areas by physicians was 8.1% on average. CONCLUSION: The study shows the potential of the evaluated technology. In contrast to the Psoriasis Area and Severity Index (PASI), it allows for objective evaluation and should therefore be developed further as an alternative method to human assessment.

Linkage between patients' characteristics and prescribed systemic treatments for psoriasis: a semantic connectivity map analysis of the Swiss Dermatology Network for Targeted Therapies registry.

BACKGROUND: Several treatment options are currently available for the treatment of psoriasis. OBJECTIVE: To explore the main associations between patients' characteristics and systemic treatments prescribed for psoriasis in a large group of patients observed in real-life clinical practice. METHODS: This was a retrospective analysis of baseline data collected within the Swiss Dermatology Network for Targeted Therapies registry in Switzerland between March 2011 and December 2017. Semantic map analysis was used in order to capture the best associations between variables taking into account other covariates in the system. RESULTS: A total of 549 patients (mean age 46.7 ± 14.7 years) were included in the analysis. Conventional therapies such as retinoids and methotrexate were associated with no previous systemic therapies for psoriasis, a moderate quality of life (QoL) at therapy onset and older age (≥60 years). Fumaric acid derivatives were associated with mild psoriasis (psoriasis area severity index < 10) and long disease duration (≥20 years). On the other side, cyclosporine and psoralen and ultraviolet A/ultraviolet B treatments were linked to a more severe condition, including impaired QoL, hospitalization and inability to work. Regarding biological therapies, both infliximab and adalimumab were connected to the presence of psoriatic arthritis, severe disease condition and other comorbidities, including chronic liver or kidney diseases and tuberculosis. Etanercept, ustekinumab and secukinumab were all connected to a complex history of previous systemic treatments for psoriasis, moderate disease condition, overweight and university education. CONCLUSIONS: The analysis shows multifaceted associations between patients' characteristics, comorbidities, disease severity and systemic treatments prescribed for psoriasis. In particular, our semantic map indicates that comorbidities play a central role in decision-making of systemic treatments usage for psoriasis. Future studies should further investigate specific connections emerging from our data.