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.

Understanding short-term transmission dynamics of methicillin-resistant Staphylococcus aureus in the patient room.

OBJECTIVE: Little is known about the short-term dynamics of methicillin-resistant Staphylococcus aureus (MRSA) transmission between patients and their immediate environment. We conducted a real-life microbiological evaluation of environmental MRSA contamination in hospital rooms in relation to recent patient activity. DESIGN: Observational pilot study. SETTING: Two hospitals, hospital 1 in Zurich, Switzerland, and hospital 2 in Ann Arbor, Michigan, United States. PATIENTS: Inpatients with MRSA colonization or infection. METHODS: At baseline, the groin, axilla, nares, dominant hands of 10 patients and 6 environmental high-touch surfaces in their rooms were sampled. Cultures were then taken of the patient hand and high-touch surfaces 3 more times at 90-minute intervals. After each swabbing, patients' hands and surfaces were disinfected. Patient activity was assessed by interviews at hospital 1 and analysis of video footage at hospital 2. A contamination pressure score was created by multiplying the number of colonized body sites with the activity level of the patient. RESULTS: In total, 10 patients colonized and/or infected with MRSA were enrolled; 40 hand samples and 240 environmental samples were collected. At baseline, 30% of hands and 20% of high-touch surfaces yielded MRSA. At follow-up intervals, 8 (27%) of 30 patient hands, and 10 (6%) of 180 of environmental sites were positive. Activity of the patient explained 7 of 10 environmental contaminations. Patients with higher contamination pressure score showed a trend toward higher environmental contamination. CONCLUSION: Environmental MRSA contamination in patient rooms was highly dynamic and was likely driven by the patient's MRSA body colonization pattern and the patient activity.