The soft computing-based approach to investigate allergic diseases: a systematic review

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.

Relationship between single nucleotide polymorphism of NOS2 gene and inheritance of allergic rhinitis in children

Allergic rhinitis is a common chronic disease, and its high incidence has a great negative impact on the quality of life of many people, especially children. In this paper, through in-depth analysis of NOS2 gene polymorphism, the protective mechanism of NOS2 gene against AR was studied to provide theoretical and scientific basis for the diagnosis of children with AR. It was concluded that the concentration of Immunoglobulin E (IgE) in rs2297516 was 0.24 IU/mL compared with that in normal children. rs3794766 specific IgE concentration in the children group was increased by 0.36 IU/mL, which was higher than that in the healthy children group; the difference of rs7406657 specific IgE concentration between the children group and the healthy group was 0.03 IU/mL. The total serum IgE concentration in the healthy children group was lower than that in the infant group, and the change of Rs3794766 was the least, followed by rs2297516 and rs7406657. This means that rs7406657 is the highest, rs2297516 had general genetic correlation with AR patients, and rs3794766 had the least genetic correlation with AR patients. Among the three groups of SNP loci, the healthy children group was higher than the patient children group, indicating that AR reduces the gene frequency of the three loci, and the reduction of gene frequency will also increase the susceptibility of children to AR, because the frequency of gene occurrence will affect the gene sequence. In conclusion, smart medicine and gene SNPS can promote the detection and treatment of AR.

What are the highest yielding search strategy terms for systematic reviews in atopic dermatitis? A systematic review

The impact of search strategies on systematic reviews (SR) of atopic dermatitis (AD) is unknown. The purpose of this review was to evaluate search strategies used in SR of AD and their impact on the frequency of manuscripts identified. MEDLINE and EMBASE were searched for SR related to AD. Simulations were performed by running combinations of search terms in MEDLINE and EMBASE. Overall, 250 SR met inclusion criteria, of which 225 specified search strategies. SR using 5-6 terms (20.0% to 12.1%) or ≥ 7 (40.0% to 18.8%) terms decreased, whereas SR using 3-4 terms numerically increased (18.8% to 30.2%) and 1-2 terms remained similar (37.5% to 38.9%) from 1999-2009 to 2015-2019. The most commonly searched terms were "atopic dermatitis" (n = 166), followed by "eczema" (n = 156), "dermatitis atopic'" (n = 81), "atopic eczema" (n = 74), "neurodermatitis" (n = 59), "Besniers prurigo" (n = 29), "infantile eczema" (n = 27), and "childhood eczema" (n = 19). Simulations revealed that "eczema" and "atopic dermatitis" yielded the most hits. The number of search terms that maximized hits in MEDLINE and EMBASE was 5 and 4, respectively. Search strategies for AD were heterogeneous, with high proportions of search strategies providing few search hits. Future studies should use standardized and optimized search terms.

A Machine Learning Application to Predict Early Lung Involvement in Scleroderma: A Feasibility Evaluation

Introduction: Systemic sclerosis (SSc) is a systemic immune-mediated disease, featuring fibrosis of the skin and organs, and has the greatest mortality among rheumatic diseases. The nervous system involvement has recently been demonstrated, although actual lung involvement is considered the leading cause of death in SSc and, therefore, should be diagnosed early. Pulmonary function tests are not sensitive enough to be used for screening purposes, thus they should be flanked by other clinical examinations; however, this would lead to a risk of overtesting, with considerable costs for the health system and an unnecessary burden for the patients. To this extent, Machine Learning (ML) algorithms could represent a useful add-on to the current clinical practice for diagnostic purposes and could help retrieve the most useful exams to be carried out for diagnostic purposes. Method: Here, we retrospectively collected high resolution computed tomography, pulmonary function tests, esophageal pH impedance tests, esophageal manometry and reflux disease questionnaires of 38 patients with SSc, applying, with R, different supervised ML algorithms, including lasso, ridge, elastic net, classification and regression trees (CART) and random forest to estimate the most important predictors for pulmonary involvement from such data. Results: In terms of performance, the random forest algorithm outperformed the other classifiers, with an estimated root-mean-square error (RMSE) of 0.810. However, this algorithm was seen to be computationally intensive, leaving room for the usefulness of other classifiers when a shorter response time is needed. Conclusions: Despite the notably small sample size, that could have prevented obtaining fully reliable data, the powerful tools available for ML can be useful for predicting early lung involvement in SSc patients. The use of predictors coming from spirometry and pH impedentiometry together might perform optimally for predicting early lung involvement in SSc.

Noninvasive and minimally invasive techniques for the diagnosis and management of allergic diseases

Allergic diseases of the (upper and lower) airways, the skin and the gastrointestinal tract, are on the rise, resulting in impaired quality of life, decreased productivity, and increased healthcare costs. As allergic diseases are mostly tissue-specific, local sampling methods for respective biomarkers offer the potential for increased sensitivity and specificity. Additionally, local sampling using noninvasive or minimally invasive methods can be cost-effective and well tolerated, which may even be suitable for primary or home care sampling. Non- or minimally invasive local sampling and diagnostics may enable a more thorough endotyping, may help to avoid under- or overdiagnosis, and may provide the possibility to approach precision prevention, due to early diagnosis of these local diseases even before they get systemically manifested and detectable. At the same time, dried blood samples may help to facilitate minimal-invasive primary or home care sampling for classical systemic diagnostic approaches. This EAACI position paper contains a thorough review of the various technologies in allergy diagnosis available on the market, which analytes or biomarkers are employed, and which samples or matrices can be used. Based on this assessment, EAACI position is to drive these developments to efficiently identify allergy and possibly later also viral epidemics and take advantage of comprehensive knowledge to initiate preventions and treatments.

Digital systems for improving outcomes in patients with primary immune defects

PURPOSE OF REVIEW

Healthcare has already been impacted by the fourth industrial revolution exemplified by tip of spear technology, such as artificial intelligence and quantum computing. Yet, there is much to be accomplished as systems remain suboptimal, and full interoperability of digital records is not realized. Given the footprint of technology in healthcare, the field of clinical immunology will certainly see improvements related to these tools.

RECENT FINDINGS

Biomedical informatics spans the gamut of technology in biomedicine. Within this distinct field, advances are being made, which allow for engineering of systems to automate disease detection, create computable phenotypes and improve record portability. Within clinical immunology, technologies are emerging along these lines and are expected to continue.

SUMMARY

This review highlights advancements in digital health including learning health systems, electronic phenotyping, artificial intelligence and use of registries. Technological advancements for improving diagnosis and care of patients with primary immunodeficiency diseases is also highlighted.

Artificial intelligence and the hunt for immunological disorders

PURPOSE OF REVIEW

Artificial intelligence has pervasively transformed many industries and is beginning to shape medical practice. New use cases are being identified in subspecialty domains of medicine and, in particular, application of artificial intelligence has found its way to the practice of allergy-immunology. Here, we summarize recent developments, emerging applications and obstacles to realizing full potential.

RECENT FINDINGS

Artificial/augmented intelligence and machine learning are being used to reduce dimensional complexity, understand cellular interactions and advance vaccine work in the basic sciences. In genomics, bioinformatic methods are critical for variant calling and classification. For clinical work, artificial intelligence is enabling disease detection, risk profiling and decision support. These approaches are just beginning to have impact upon the field of clinical immunology and much opportunity exists for further advancement.

SUMMARY

This review highlights use of computational methods for analysis of large datasets across the spectrum of research and clinical care for patients with immunological disorders. Here, we discuss how big data methods are presently being used across the field clinical immunology.

Artificial neural network identifies nonsteroidal anti-inflammatory drugs exacerbated respiratory disease (N-ERD) cohort

Background

To date, there has been no reliable in vitro test to either diagnose or differentiate nonsteroidal anti‐inflammatory drug (NSAID)–exacerbated respiratory disease (N‐ERD). The aim of the present study was to develop and validate an artificial neural network (ANN) for the prediction of N‐ERD in patients with asthma.

Methods

This study used a prospective database of patients with N‐ERD (n = 121) and aspirin‐tolerant (n = 82) who underwent aspirin challenge from May 2014 to May 2018. Eighteen parameters, including clinical characteristics, inflammatory phenotypes based on sputum cells, as well as eicosanoid levels in induced sputum supernatant (ISS) and urine were extracted for the ANN.

Results

The validation sensitivity of ANN was 94.12% (80.32%‐99.28%), specificity was 73.08% (52.21%‐88.43%), and accuracy was 85.00% (77.43%‐92.90%) for the prediction of N‐ERD. The area under the receiver operating curve was 0.83 (0.71‐0.90).

Conclusions

The designed ANN model seems to have powerful prediction capabilities to provide diagnosis of N‐ERD. Although it cannot replace the gold‐standard aspirin challenge test, the implementation of the ANN might provide an added value for identification of patients with N‐ERD. External validation in a large cohort is needed to confirm our results.