bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease

Advancements in artificial intelligence for atopic dermatitis: diagnosis, treatment, and patient management

Atopic dermatitis (AD) is a common and complex skin disease that significantly affects the quality of life of patients. The latest advances in artificial intelligence (AI) technology have introduced new methods for diagnosing, treating, and managing AD. AI has various innovative applications in the diagnosis and treatment of atopic dermatitis, with particular emphasis on its significant benefits in medical diagnosis, treatment monitoring, and patient care. AI algorithms, especially those that use deep learning techniques, demonstrate strong performance in recognizing skin images and effectively distinguishing different types of skin lesions, including common AD manifestations. In addition, artificial intelligence has also shown promise in creating personalized treatment plans, simplifying drug development processes, and managing clinical trials. Despite challenges in data privacy and model transparency, the potential of artificial intelligence in advancing AD care is enormous, bringing the future to precision medicine and improving patient outcomes. This manuscript provides a comprehensive review of the application of AI in the process of AD disease for the first time, aiming to play a key role in the advancement of AI in skin health care and further enhance the clinical diagnosis and treatment of AD.

Atopic dermatitis: a comprehensive updated review of this intriguing disease with futuristic insights

Atopic dermatitis (AD) is a paradigmatic prevalent, long-lasting, and inflammatory skin condition with a diverse range of clinical manifestations. The etiology and clinical symptoms of AD are influenced by complex pathophysiological processes, which involve a strong genetic component, epidermal dysfunction, and immunological dysregulation, and a strong influence of other physiological and environmental factors. The FDA has approved targeted and well-tolerated immunomodulators including biologics like dupilumab and crisaborole, and small molecules such as baricitinib, as novel therapies for AD. They effectively treat AD but are too expensive for most patients. The review provides an update on the state of knowledge of AD pathogenesis, discusses the available diagnostic and scoring indices, and provides a scientific foundation for treatment methods for AD. This review also presents data on clinical efficacy of innovative treatments' considering recent guidelines, emphasizing the newest medications and ongoing trials. Finally, the new implication of artificial intelligence (AI) in AD management is explored, where AI can speed up diagnosis and therapy. The PubMed, Google Scholar, and ScienceDirect databases were used for this review.

An enhancing diagnostic pulmonary diseases diagnostic method for differentiating talaromycosis from tuberculosis

Talaromycosis (TSM) affects immunocompromised individuals, particularly those with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS), causing varied pulmonary abnormalities on chest computed tomography (CT). These features overlap with pulmonary tuberculosis, making accurate differentiation essential for appropriate treatment. This study utilized real patient data from the First Affiliated Hospital of Wenzhou Medical University. A machine learning model, termed bIPCACO-FKNN, was developed, integrating an ant colony optimization (ACO) algorithm with a fuzzy k-nearest neighbors (FKNNs) classifier. This model introduces an incremental proportional-integral-derivative control strategy to enhance the search efficiency of ACO. Comparative analysis with several algorithms in the CEC 2017 benchmark functions confirms the superior performance of the IPCACO. Applying the bIPCACO-FKNN model for the prediction of pulmonary TSM achieved a prediction accuracy of 98.196% and a specificity of 99.500%, thus demonstrating its significant efficacy in accurately distinguishing between pulmonary TSM and tuberculosis. This provides an efficient and reliable machine learning tool for the differentiation between pulmonary TSM and tuberculosis.

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.

CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images

To improve the detection of COVID-19, this paper researches and proposes an effective swarm intelligence algorithm-driven multi-threshold image segmentation (MTIS) method. First, this paper proposes a novel RIME structure integrating the Co-adaptive hunting and dispersed foraging strategies, called CDRIME. Specifically, the Co-adaptive hunting strategy works in coordination with the basic search rules of RIME at the individual level, which not only facilitates the algorithm to explore the global optimal solution but also enriches the population diversity to a certain extent. The dispersed foraging strategy further enriches the population diversity to help the algorithm break the limitation of local search and thus obtain better convergence. Then, on this basis, a new multi-threshold image segmentation method is proposed by combining the 2D non-local histogram with 2D Kapur entropy, called CDRIME-MTIS. Finally, the results of experiments based on IEEE CEC2017, IEEE CEC2019, and IEEE CEC2022 demonstrate that CDRIME has superior performance than some other basic, advanced, and state-of-the-art algorithms in terms of global search, convergence performance, and escape from local optimality. Meanwhile, the segmentation experiments on COVID-19 X-ray images demonstrate that CDRIME is more advantageous than RIME and other peers in terms of segmentation effect and adaptability to different threshold levels. In conclusion, the proposed CDRIME significantly enhances the global optimization performance and image segmentation of RIME and has great potential to improve COVID-19 diagnosis.

Enhanced Harris hawks optimization-based fuzzy k-nearest neighbor algorithm for diagnosis of Alzheimer's disease

In order to stop deterioration and give patients with Alzheimer's disease (AD) early therapy, it is crucial to correctly diagnose AD and its early stage, mild cognitive impairment (MCI). A framework for diagnosing AD is presented in this paper, which includes magnetic resonance imaging (MRI) image preprocessing, feature extraction, and the Fuzzy k-nearest neighbor algorithm (FKNN) model. In particular, the framework's novelty lies in the use of an improved Harris Hawks Optimization (HHO) algorithm named SSFSHHO, which integrates the Sobol sequence and Stochastic Fractal Search (SFS) mechanisms for optimizing the parameters of FKNN. The HHO method improves the quality of the initial population overall by incorporating the Sobol sequence, and the SFS mechanism increases the algorithm's capacity to get out of the local optimum solution. Comparisons with other classical meta-heuristic algorithms, state-of-the-art HHO variants in low and high dimensions, and enhanced meta-heuristic algorithms on 30 typical IEEE CEC2014 benchmark test problems show that the overall performance of SSFSHHO is significantly better than other comparative algorithms. Moreover, the created framework based on the SSFSHHO-FKNN model is employed to classify AD and MCI using MRI scans from the ADNI dataset, achieving high classification performance for 6 representative cases. Experimental findings indicate that the proposed algorithm performs better than a number of high-performance optimization algorithms and classical machine learning algorithms, thus offering a promising approach for AD classification. Additionally, the proposed strategy can successfully identify relevant features and enhance classification performance for AD diagnosis.