Published Paper Details:

Dengue fever, a mosquito-borne viral illness, poses a significant global public health threat, especially in tropical and subtropical regions. Caused by four distinct serotypes of the dengue virus, it manifests in a spectrum ranging from mild flu-like symptoms to severe complications such as hemorrhagic fever and shock syndrome. Despite extensive prevention and control efforts, dengue remains a burden with periodic outbreaks, particularly during rainy seasons when mosquito breeding peaks. Accurate and prompt diagnosis of dengue is crucial for effective clinical management and outbreak containment. Conventional diagnostic methods face challenges, including false negatives, delayed results, and reliance on labor-intensive laboratory tests, necessitating a shift towards more efficient and reliable diagnostic approaches. Deep learning models hold the potential to revolutionize dengue diagnosis by leveraging diverse data sources for early detection and prediction. By integrating clinical symptoms, laboratory findings, and epidemiological data, these models offer a comprehensive approach to dengue diagnosis, enabling rapid and accurate case identification. Deep learning algorithms can analyze vast amounts of data to identify patterns and early signs of infection that traditional methods might miss. Siddaganga Medical College & Research Institute is at the forefront of using real-time data for dengue surveillance and diagnosis. Through collaborations with research partners, Siddaganga Hospital has amassed extensive clinical data, including patient demographics, symptoms, and diagnostic test results. Utilizing this rich dataset, deep learning models can identify early signs of dengue infection, facilitating timely interventions and reducing the disease burden. This review highlights the importance of integrating real-time data and fostering collaboration between healthcare providers and data scientists to advance dengue diagnosis and surveillance. By harnessing the power of deep learning models and real-time data streams, institutions like Siddaganga Hospital can enhance their capacity for early detection and response to dengue outbreaks, ultimately mitigating the impact on public health.