Image-Based Artificial Intelligence in Psoriasis Assessment: The Beginning of a New Diagnostic Era?

Psoriasis, a chronic inflammatory skin disease, affects millions of people worldwide. It imposes a significant burden on patients' quality of life and healthcare systems, creating an urgent need for optimized diagnosis, treatment, and management. In recent years, image-based artificial intelligence (AI) applications have emerged as promising tools to assist physicians by offering improved accuracy and efficiency.

AI-powered visual diagnosis of vulvar lichen sclerosus: A pilot study.

Background: Vulvar lichen sclerosus (VLS) is a chronic inflammatory skin condition associated with significant impairment of quality of life and potential risk of malignant transformation. However, diagnosis of VLS is often delayed due to its variable clinical presentation and shame-related late consultation. Machine learning (ML)-trained image recognition software could potentially facilitate early diagnosis of VLS.

The SLICE-3D dataset: 400,000 skin lesion image crops extracted from 3D TBP for skin cancer detection.

AI image classification algorithms have shown promising results when applied to skin cancer detection. Most public skin cancer image datasets are comprised of dermoscopic photos and are limited by selection bias, lack of standardization, and lend themselves to development of algorithms that can only be used by skilled clinicians. The SLICE-3D ("Skin Lesion Image Crops Extracted from 3D TBP") dataset described here addresses those concerns and contains images of over 400,000 distinct skin lesions from seven dermatologic centers from around the world.

Objective hand eczema severity assessment with automated lesion anatomical stratification.

Hand eczema (HE) is one of the most frequent dermatoses, known to be both relapsing and remitting. Regular and precise evaluation of the disease severity is key for treatment management. Current scoring systems such as the hand eczema severity index (HECSI) suffer from intra- and inter-observer variance.

Quantification of Efflorescences in Pustular Psoriasis Using Deep Learning.

Objectives: Pustular psoriasis (PP) is one of the most severe and chronic skin conditions. Its treatment is difficult, and measurements of its severity are highly dependent on clinicians' experience. Pustules and brown spots are the main efflorescences of the disease and directly correlate with its activity.

Improved diagnosis by automated macro- and micro-anatomical region mapping of skin photographs.

Background: The exact location of skin lesions is key in clinical dermatology. On one hand, it supports differential diagnosis (DD) since most skin conditions have specific predilection sites. On the other hand, location matters for dermatosurgical interventions.

[Artificial intelligence in image analysis-fundamentals and new developments].

Background: Since 2017, there have been several reports of artificial intelligence (AI) achieving comparable performance to human experts on medical image analysis tasks. With the first ratification of a computer vision algorithm as a medical device in 2018, the way was paved for these methods to eventually become an integral part of modern clinical practice.

Objectives: The purpose of this article is to review the main developments that have occurred over the last few years in AI for image analysis, in relation to clinical applications and dermatology.