Evaluation of therapeutic response to algorithm assisted improvement of oral mucosa damage in male AIDS patients
Abstract
HIV/AIDS is now the biggest cause of mortality in Africa and the fourth highest cause of death globally. Half or more of HIV-infected individuals and as many as 80% of AIDS patients develop oral lesions. People living with HIV/AIDS may benefit from early testing, diagnosis, and treatment if oral lesions are detected, as they are the initial clinical characteristics of the infection and strong indicators of immunodeficiency. Oral candidiasis (OPC), oral hairy leukoplakia (OHL), oral Kaposi’s sarcoma (OKS), and HIV-associated periodontal diseases were the subjects of this comprehensive review designed to assess the available data for the management of these and other common oral mucosa damage that are linked to HIV. Further exacerbating the condition are host variables such as xerostomia, smoking, dental caries, oral prosthesis, diabetes, and cancer treatments. A separate portion of this Worldwide Workshop discusses the treatment of salivary gland illness linked with HIV. Oral mucosa injury in AIDS patients does not have a reliable diagnostic approach. Improving the early diagnosis of Oral Mucosa damage in male AIDS patients was the primary goal of this work, which sought to construct an Artificial Intelligence (AI) diagnostic model with high sensitivity. To demonstrate a Gradient Boosting Regression (GBR) based method for assessing the efficacy of treatments for oral mucosa damage in AIDS patients. Both investigation and diagnostic applications may benefit from this predictor. Results show that, compared to existing models, the suggested AI and GBR methods can accurately predict oral mucosa deterioration in AIDS patients. This study significantly contributes to the profession by improving the accuracy of diagnoses and providing useful information for treatment options.
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