Enhanced Deep Learning Approach for Accurate Eczema and Psoriasis Skin Detection

Single-cell analysis and machine learning identify psoriasis-associated CD8+ T cells serve as biomarker for psoriasis

Psoriasis is a chronic inflammatory skin disease, the etiology of which has not been fully elucidated, in which CD8+ T cells play an important role in the pathogenesis of psoriasis. However, there is a lack of in-depth studies on the molecular characterization of different CD8+ T cell subtypes and their role in the pathogenesis of psoriasis. This study aims to further expound the pathogenesy of psoriasis at the single-cell level and to explore new ideas for clinical diagnosis and new therapeutic targets. Our study identified a unique subpopulation of CD8+ T cells highly infiltrated in psoriasis lesions. Subsequently, we analyzed the hub genes of the psoriasis-specific CD8+ T cell subpopulation using hdWGCNA and constructed a machine-learning prediction model, which demonstrated good efficacy. The model interpretation showed the influence of each independent variable in the model decision. Finally, we deployed the machine learning model to an online website to facilitate its clinical transformation.

Efficient diagnosis of psoriasis and lichen planus cutaneous diseases using deep learning approach

The tendency of skin diseases to manifest in a unique and yet similar appearance, absence of enough competent dermatologists, and urgency of diagnosis and classification on time and accurately, makes the need of machine aided diagnosis blatant. This study is conducted with the purpose of broadening the research in skin disease diagnosis with computer by traversing the capabilities of deep Learning algorithms to classify two skin diseases noticeably close in appearance, Psoriasis and Lichen Planus. The resemblance between these two skin diseases is striking, often resulting in their classification within the same category. Despite this, there is a dearth of research focusing specifically on these diseases. A customized 50 layers ResNet-50 architecture of convolutional neural network is used and the results are validated through fivefold cross-validation, threefold cross-validation, and random split. By utilizing advanced data augmentation and class balancing techniques, the diversity of the dataset has increased, and the dataset imbalance has been minimized. ResNet-50 has achieved an accuracy of 89.07%, sensitivity of 86.46%, and specificity of 86.02%. With their promising results, these algorithms make the potential of machine aided diagnosis clear. Deep Learning algorithms could provide assistance to physicians and dermatologists by classification of skin diseases, with similar appearance, in real-time.

A Mobile Health App for Facilitating Disease Management in Children With Atopic Dermatitis: Feasibility and Impact Study

BACKGROUND

Inadequate control of atopic dermatitis (AD) increases the frequency of exacerbations and reduces the quality of life. Mobile health apps provide information and communication technology and may increase treatment adherence and facilitate disease management at home. The mobile health app, Atopic App, designed for patients and their caregivers, and the associated web-based patient education program, Atopic School, provide an opportunity for improving patients' and caregivers' engagement and adherence to the management of AD.

OBJECTIVE

This noninterventional, observational study aimed to explore the feasibility and potential impact on the management of AD in children by caregivers using the Atopic App mobile health app.

METHODS

The patient-oriented eczema measure (POEM) and numerical rating scale for the grading of pruritus were used as severity scores (scale range: 0-28). The artificial intelligence model of the app was used to assess the severity of AD based on the eczema area and severity index approach. The deidentified data enabled the analysis of the severity of AD, treatment plan history, potential triggers of flare-ups, usage of available features of the app, and the impact of patient education.

RESULTS

During a 12-month period, of the 1223 users who installed the app, 910 (74.4%) registered users were caregivers of children with AD. The web-based Atopic School course was accessed by 266 (29.2%) caregivers of children with AD, 134 (50.4%) of whom completed the course. Usage of the app was significantly more frequent among those who completed the Atopic School program than among those who did not access or did not complete the course (P<.001). Users who completed a second POEM 21 to 27 days apart exhibited a significant improvement of AD severity based on the POEM score (P<.001), with an average improvement of 3.86 (SD 6.85) points. The artificial intelligence severity score and itching score were highly correlated with the POEM score (r=0.35 and r=0.52, respectively).

CONCLUSIONS

The Atopic App provides valuable real-world data on the epidemiology, severity dynamics, treatment patterns, and exacerbation-trigger correlations in patients with AD. The significant reduction in the POEM score among users of the Atopic App indicates a potential impact of this tool on health care engagement by caregivers of children with AD.