Abstract
Background: Allergic rhinitis (AR) is a prevalent global disorder affecting millions across the world among all age groups. This study delves into the etiology of AR, unravelling the natural progression of the disease from its inception and identifying causative factors. The primary aim of this investigation was to explore the etiology of allergic rhinitis, paving the way for informed decisions regarding prevention, treatment, or cure. Methods: Using Artificial Intelligence (AI) to delineate the clinical profiles of patients suffering from allergic rhinitis, including symptom severity, duration, and associated comorbidities were assessed in the fifteen case studies. Other parameters were studied by regular standard laboratory procedures as described in the text. Results: A total of 15 patients with age-wise distribution showed females were 60 % (9) and males 40 % (6). A total of 11 cases (73.33%) had IgE values between 20–100 IU/mL and 26.66 % (4) had IgE values above 100 IU/mL. In all other cases, IgE levels were less than 20 IU/mL and therefore were not considered important. Other data such as nasal congestion, rhinorrhea, sneezing, and nasal itching, were highlighted, alongside other associated manifestations and are presented in tables and graphs. Conclusions: It is concluded that in these 15 cases with complaints of AR, a correlation between the levels of IgE and age distribution, gender distribution, and eosinophil counts was observed. These factors were also found to correlate with IgE levels, indicating the severity of the disease. By understanding how the disease initially manifests and its underlying causes, valuable insights were gained into predicting its future course using AI. Additionally, patient education and awareness were enhanced based on individual clinical profiles. With a steady increase in the application of AI models for healthcare, the day is not far when AI may become the essential feature of all medical care in the future.
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