Arpita Singh, Dr. Anuradha Misra, Garima Srivastava, 2026. "Automated Diagnosis of HMPV Infections Using Medical Imaging and Clinical Data" ESP International Journal of Advancements in Science & Technology (ESP-IJAST) Volume 4, Issue 2: 57-61.
Human Metapneumovirus (HMPV) is a respiratory pathogen causing significant morbidity across pediatric, adult, and immunocompromised populations. Early diagnosis is critical, but conventional methods—such as RT-PCR and immunofluorescence—are time-consuming and costly, while radiological findings are non-specific. Recent advances in artificial intelligence (AI) and machine learning (ML) present opportunities for automated diagnosis by integrating medical imaging with clinical data.
[1] Falsey et al., “Human metapneumovirus infections in young and elderly adults,” New England Journal of Medicine, vol. 350, no. 5, pp. 443–450, 2004.
[2] R. Schildgen et al., “Human Metapneumovirus: Lessons learned over the first decade,” Clin Microbiol Rev, vol. 24, no. 4, pp. 734–754, 2011.
[3] D. Debur et al., “Radiologic findings in human metapneumovirus infection: a comparative analysis,” J Med Virol, vol. 82, no. 3, pp. 441–449, 2010.
[4] F. Widmer et al., “Clinical and radiographic characteristics of human metapneumovirus infection in adults,” J Clin Virol, vol. 46, no. 4, pp. 372– 376, 2009.
[5] K. Boivin et al., “Human metapneumovirus: characteristics of an important respiratory pathogen,” Clin Infect Dis, vol. 46, pp. 617–624, 2008.
[6] P. Walsh et al., “Differential diagnosis of viral pneumonias: clinical challenges,” Lancet Respir Med, vol. 7, no. 10, pp. 885–894, 2019.
[7] X. Wang et al., “ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,” CVPR, pp. 3462–3471, 2017.
[8] M. Jain et al., “Integration of clinical and imaging features for respiratory infections,” Front Med AI, vol. 2, pp. 1–9, 2021.
[9] A. Shorten and T. Khoshgoftaar, “A survey on image data augmentation for deep learning,” J Big Data, vol. 6, no. 60, pp. 1–48, 2019.
[10] I. Guyon et al., “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, pp. 389–422, 2002
[11] G. Huang et al., “Densely connected convolutional networks,” CVPR, pp. 4700–4708, 2017.
[12] J. Chen et al., “Multimodal deep learning for COVID-19 diagnosis,” Med Image Anal, vol. 67, p. 101812, 2021.
[13] A. Dosovitskiy et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” ICLR, 2021.
[14] L. Zhou et al., “Ensemble learning for pneumonia detection from chest X-rays,” Pattern Recognition Letters, vol. 140, pp. 1–8, 2020.
[15] S. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” Adv Neural Inf Process Syst, vol. 30, pp. 4765–4774, 2017.
HMPV, Human Metapneumovirus, Medical Imaging, Chest X-Ray, Machine Learning, Artificial Intelligence.