Биология ва тиббиёт муаммолари 2025, №1 (158)
Тема статьи
AI - DRIVEN DETECTION OF TUBERCULOSIS IN PATIENTS WITH HYPERGLYCEMIA AND COMORBIDITIES (61-64)
Авторы
Dr. Bhabi Jeslina John Viji Beula, Ankita Deshpande, Jaydeep Prajapati, Saurabh Rai
Учреждение
Samarkand State Medical University, Republic Of Uzbekistan, Samarkand
Аннотация
In the world, one of the most concerning problems with respect to the population killers is diabetes and tuberculosis. It is ranked 2nd after HIV. There are many forms of conventional methods, such as blood tests, biopsy, and sputum analysis. But considering the amount of cases, there are many poorly diagnosed and untreated. With the growing advancements in the field of technology, AI (artificial intelligence) can help for the early and more accurate detection of TB (tuberculosis). Different methods in AI have paved the way, like data mining approaches, genetic algorithms, neural networks with multiple layers, and many more mentioned, which help in assisting better diagnosis in the medical field. AI algorithms have depicted great promises in the diagnosis of many tuberculosis co- morbidities like pulmonary tuberculosis using radiological methods, automated detection of Mycobacterium tuberculosis, and accurate analysis of genetic data for the diagnosis of drug- resistant. TB associated with hyperglycemia can be better processed with innovative techniques like natural language processing (NLP) and convolutional networks (CNNs). AI can predict the response of anti-TB drugs, which can help formulate the most convenient drug for the patient. There are some challenges that are associated with AI, like data privacy, limited internet access, and a lack of AI-skilled technicians. But solar cells can be considered for the better provision to utilize AI. AI in medicine and the healthcare community is still a tip of the iceberg, and there are many great innovations to exercise the practice to provide the best care.
Ключевые слова
tuberculosis, artificial intelligence, machine learning, health care, drug resistance, genetic algorithm, natural language processing, convolutional network.
Литературы
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