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. We propose an automated system based on deep learning models (DLM) to quantify HE lesions' surface and determine their anatomical stratification. In this retrospective study, a team of 11 experienced dermatologists annotated eczema lesions in 312 HE pictures, and a medical student created anatomical maps of 215 hands pictures based on 37 anatomical subregions. Each data set was split into training and test pictures and used to train and evaluate two DLMs, one for anatomical mapping, the other for HE lesions segmentation. On the respective test sets, the anatomy DLM achieved average precision and sensitivity of 83% (95% confidence interval [CI] 80-85) and 85% (CI 82-88), while the HE DLM achieved precision and sensitivity of 75% (CI 64-82) and 69% (CI 55-81). The intraclass correlation of the predicted HE surface with dermatologists' estimated surface was 0.94 (CI 0.90-0.96). The proposed method automatically predicts the anatomical stratification of HE lesions' surface and can serve as support to evaluate hand eczema severity, improving reliability, precision and efficiency over manual assessment. Furthermore, the anatomical DLM is not limited to HE and can be applied to any other skin disease occurring on the hands such as lentigo or psoriasis.

[Artificial intelligence in image analysis-fundamentals and new developments]. / Künstliche Intelligenz in der Bildanalyse ­ Grundlagen und neue Entwicklungen.

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. MATERIALS AND METHODS: Following the annual ImageNet challenge, we review classical methods of machine learning for image analysis and demonstrate how these methods incorporated human expertise but failed to meet industrial requirements regarding performance and scalability. With the rise of deep learning based on artificial neural networks, these limitations could be overcome. We discuss important aspects of this technology including transfer learning and report on recent developments such as explainable AI and generative models. RESULTS: Deep learning models achieved performance on a par with human experts in a broad variety of diagnostic tasks and were shown to be suitable for industrialization. Therefore, current developments focus less on further improving accuracy but rather address open issues such as interpretability and applicability under clinical conditions. Upcoming generative models allow for entirely new applications. CONCLUSIONS: Deep learning has a history of remarkable success and has become the new technical standard for image analysis. The dramatic improvement these models brought over classical approaches enables applications in a rapidly increasing number of clinical fields. In dermatology, as in many other domains, artificial intelligence still faces considerable challenges but is undoubtedly developing into an essential tool of modern medicine.

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. We propose an automated deep learning model (DLM) to quantify lesions in terms of count and surface percentage from patient photographs. METHODS: In this retrospective study, two dermatologists and a student labeled 151 photographs of PP patients for pustules and brown spots. The DLM was trained and validated with 121 photographs, keeping 30 photographs as a test set to assess the DLM performance on unseen data. We also evaluated our DLM on 213 unstandardized, out-of-distribution photographs of various pustular disorders (referred to as the pustular set), which were ranked from 0 (no disease) to 4 (very severe) by one dermatologist for disease severity. The agreement between the DLM predictions and experts' labels was evaluated with the intraclass correlation coefficient (ICC) for the test set and Spearman correlation (SC) coefficient for the pustular set. RESULTS: On the test set, the DLM achieved an ICC of 0.97 (95% confidence interval [CI], 0.97-0.98) for count and 0.93 (95% CI, 0.92-0.94) for surface percentage. On the pustular set, the DLM reached a SC coefficient of 0.66 (95% CI, 0.60-0.74) for count and 0.80 (95% CI, 0.75-0.83) for surface percentage. CONCLUSIONS: The proposed method quantifies efflorescences from PP photographs reliably and automatically, enabling a precise and objective evaluation of disease activity.