Molecular Pathogens 2024, Vol.15, No.1, 1-8 http://microbescipublisher.com/index.php/mp 5 Deep learning models can also be applied to the detection and classification of viral genomes. By analyzing viral genome data, deep learning models can automatically identify subtypes and variations of the virus, providing more accurate information for prevention, control, and treatment efforts. Deep learning models can also be used for studying disease mechanisms. By analyzing and mining case data, deep learning models can uncover key factors and mechanisms hidden within the data, contributing to a deeper understanding of the pathogenesis and transmission pathways of influenza A (H1N1) virus. Furthermore, deep learning models can be applied to epidemic prevention and control. By analyzing and predicting case data, deep learning models can identify focal points and challenges in prevention and control efforts, forecast the trends and scale of virus transmission, provide scientific evidence for formulating prevention and control measures, and offer more assistance and support to prevention and treatment efforts. 4 Artificial Intelligence in the Diagnosis of Influenza A (H1N1) Virus Case Studies 4.1 Artificial intelligence-assisted diagnosis of influenza A (H1N1) virus infection Researchers Utilizing artificial intelligence techniques, researchers have trained a model that assists doctors in diagnosis by analyzing and learning from case data. The model can automatically recognize patient symptoms and signs, and incorporate information such as age, gender, and medical history to predict the likelihood of influenza A (H1N1) virus infection. Compared to traditional diagnostic methods that heavily rely on the experience of doctors and laboratory testing, which have their limitations, the use of artificial intelligence as an aid can enhance diagnostic accuracy and efficiency (Lee and Ahn, 2020). Artificial intelligence, by automatically recognizing patient symptoms and signs, can reduce subjective errors made by doctors and improve diagnostic accuracy. Furthermore, by quickly analyzing large volumes of case data, it can enhance diagnostic efficiency and reduce patient waiting time. AI-assisted diagnosis can also provide doctors with more reference information to aid in formulating treatment plans and preventive measures, thereby reducing the patient's treatment duration and lowering the incidence of complications. 4.2 Instances of artificial intelligence in early diagnosis of influenza A (H1N1) virus infection Artificial intelligence has been widely applied in the early diagnosis of influenza A (H1N1) virus infection, showcasing a diverse range of examples. For instance, by analyzing patient medical records and clinical symptoms, artificial intelligence can quickly screen whether patients are infected with the flu virus. It can automatically analyze a large volume of patient data and identify key features associated with influenza virus infection, such as coughing, fever, and respiratory distress. This aids doctors in early detection and identification of infection cases, enabling prompt measures for isolation and treatment (Lee and Ahn, 2020). On the other hand, artificial intelligence also finds applications in medical imaging. By studying and analyzing imaging data such as X-rays, CT scans, and magnetic resonance imaging (MRI) related to influenza virus infection, artificial intelligence can identify features associated with the infection. For example, it can assist doctors in detecting signs of lung infection, such as lesions and infiltrations. It helps provide earlier and more accurate diagnostic results and aids doctors in formulating appropriate treatment plans. Furthermore, AI-based virus detection methods have been widely applied. Traditional virus detection typically requires complex laboratory equipment and techniques, taking a considerable amount of time to produce results. With the support of AI, machine learning techniques can be employed to rapidly analyze and identify viruses in patient samples such as blood, saliva, or nasopharyngeal swabs. This significantly reduces the time required for virus detection, accelerates the diagnostic process, and improves treatment efficacy. 4.3 Using artificial intelligence to predict the transmission trend of influenza A (H1N1) virus Researchers utilize artificial intelligence techniques to analyze and predict the transmission trend and scale of influenza A (H1N1) virus by examining historical case data and external factors. The model takes into account
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