Automatic SCOring of Atopic Dermatitis using Deep Learning (ASCORAD): A Pilot Study

Neural Biomarkers for Identifying Atopic Dermatitis and Assessing Acupuncture Treatment Response Using Resting-State fMRI

Purpose

Only a few studies have focused on the brain mechanisms underlying the itch processing in AD patients, and a neural biomarker has never been studied in AD patients. We aimed to develop a deep learning model-based neural signature which can extract the relevant temporal dynamics, discriminate between AD and healthy control (HC), and between AD patients who responded well to acupuncture treatment and those who did not.

Patients and Methods

We recruited 41 AD patients (22 male, age mean ± SD: 24.34 ± 5.29) and 40 HCs (20 male, age mean ± SD: 26.4 ± 5.32), and measured resting-state functional MRI signals. After preprocessing, 38 functional regions of interest were applied to the functional MRI signals. A long short-term memory (LSTM) was used to extract the relevant temporal dynamics for classification and train the prediction model. Bootstrapping and 4-fold cross-validation were used to examine the significance of the models.

Results

For the identification of AD patients and HC, we found that the supplementary motor area (SMA), posterior cingulate cortex (PCC), temporal pole, precuneus, and dorsolateral prefrontal cortex showed significantly greater prediction accuracy than the chance level. For the identification of high and low responder to acupuncture treatment, we found that the lingual-parahippocampal-fusiform gyrus, SMA, frontal gyrus, PCC and precuneus, paracentral lobule, and primary motor and somatosensory cortex showed significantly greater prediction accuracy than the chance level.

Conclusion

We developed and evaluated a deep learning model-based neural biomarker that can distinguish between AD and HC as well as between AD patients who respond well and those who respond less to acupuncture. Using the intrinsic neurological abnormalities, it is possible to diagnose AD patients and provide personalized treatment regimens.

Artificial Intelligence: A Snapshot of Its Application in Chronic Inflammatory and Autoimmune Skin Diseases

Immuno-correlated dermatological pathologies refer to skin disorders that are closely associated with immune system dysfunction or abnormal immune responses. Advancements in the field of artificial intelligence (AI) have shown promise in enhancing the diagnosis, management, and assessment of immuno-correlated dermatological pathologies. This intersection of dermatology and immunology plays a pivotal role in comprehending and addressing complex skin disorders with immune system involvement. The paper explores the knowledge known so far and the evolution and achievements of AI in diagnosis; discusses segmentation and the classification of medical images; and reviews existing challenges, in immunological-related skin diseases. From our review, the role of AI has emerged, especially in the analysis of images for both diagnostic and severity assessment purposes. Furthermore, the possibility of predicting patients' response to therapies is emerging, in order to create tailored therapies.

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.

Evaluation of atopic dermatitis severity using artificial intelligence

Atopic dermatitis is a prevalent and persistent chronic inflammatory skin disorder that poses significant challenges when it comes to accurately assessing its severity. The aim of this study was to evaluate deep learning models for automated atopic dermatitis severity scoring using a dataset of Aceh ethnicity individuals in Indonesia. The dataset of clinical images was collected from 250 patients at Dr. Zainoel Abidin Hospital, Banda Aceh, Indonesia and labeled by dermatologists as mild, moderate, severe, or none. Five pretrained convolutional neural networks (CNN) architectures were evaluated: ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The evaluation metrics, including accuracy, precision, sensitivity, specificity, and F1-score, were employed to assess the models. Among the models, ResNet50 emerged as the most proficient, demonstrating an accuracy of 89.8%, precision of 90.00%, sensitivity of 89.80%, specificity of 96.60%, and an F1-score of 89.85%. These results highlight the potential of incorporating advanced, data-driven models into the field of dermatology. These models can serve as invaluable tools to assist dermatologists in making early and precise assessments of atopic dermatitis severity and therefore improve patient care and outcomes.

Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

PURPOSE OF REVIEW

Artificial intelligence (AI) has increasingly been used in healthcare. Given the capacity of AI to handle large data and complex relationships between variables, AI is well suited for applications in healthcare. Recently, AI has been applied to allergy research.

RECENT FINDINGS

In this article, we review how AI technologies have been utilized in basic science and clinical allergy research for asthma, atopic dermatitis, rhinology, adverse reactions to drugs and vaccines, food allergy, anaphylaxis, urticaria, and eosinophilic gastrointestinal disorders. We discuss barriers for AI adoption to improve the care of patients with atopic diseases. These studies demonstrate the utility of applying AI to the field of allergy to help investigators expand their understanding of disease pathogenesis, improve diagnostic accuracy, enable prediction for treatments and outcomes, and for drug discovery.

The clinical value of dual-energy CT imaging in preoperative evaluation of pathological types of gastric cancer

BACKGROUND

Gastric cancer (GC) is the fifth most common cancer worldwide and the third leading cause of cancer death. Due to the low rate of early diagnosis, most patients are already in the advanced stage and lose the chance of radical surgery.

OBJECTIVE

To investigate the clinical value of computed tomography (CT) dual-energy imaging in preoperative evaluation of pathological types of gastric cancer patients.

METHODS

121 patients with gastric cancer were selected. Dual-energy CT imaging was performed on the patients. The CT values of virtual noncontrast (VNC) images and iodine concentration of the lesion were measured, and the standardized iodine concentration ratio was calculated. The iodine concentration, iodine concentration ratio and CT values of VNC images of different pathological types were analyzed and compared.

RESULTS

The iodine concentration and iodine concentration ratio of gastric mucinous carcinoma patients in venous phase and parenchymal phase were lower than those of gastric non-mucinous carcinoma patients, and the differences were statistically significant (P< 0.05). The iodine concentration and iodine concentration ratio of patients with mucinous adenocarcinoma in venous phase and parenchymal phase were lower than those of patients with choriocarcinoma, and the differences were statistically significant (P< 0.05). The iodine concentration and iodine concentration ratio of middle and high differentiated adenocarcinoma patients in venous phase and parenchymal phase were lower than those of low differentiated adenocarcinoma patients, and the differences were statistically significant (P< 0.05). However, there was no significant difference in CT values of VNC images among venous, arterial, and parenchymal phases in all pathological types of gastric cancer patients (P> 0.05).

CONCLUSION

Dual-energy CT imaging plays an important role in the preoperative evaluation of patients with gastric cancer. The pathological types of gastric cancer are different, and the iodine concentration will change accordingly. Dual-energy CT imaging can effectively evaluate the pathological types of gastric cancer and has high clinical application value.