Influenza Diagnosis Deep Learning: Machine Learning Approach for Pharyngeal Image Infection

Chetan Chaudhari; Sapana Fegade; Sasanko Sekhar Gantayat; Kumari Jugnu; Vikash Sawan

doi:10.4108/eetpht.10.5613

Authors

Chetan Chaudhari Raisoni Group of Institutions
Sapana Fegade SSBT College of Engineering and Technology
Sasanko Sekhar Gantayat Koneru Lakshmaiah Education Foundation
Kumari Jugnu National Institute of Technology
Vikash Sawan Monad University

DOI:

https://doi.org/10.4108/eetpht.10.5613

Keywords:

Influenza, Deep Learning Model, Pharyngeal Image, AI Model, Heat Maps

Abstract

INTRODUCTION: Annual influenza epidemics and rare pandemics represent a significant global health risk. Since the upper respiratory tract is the primary target of influenza, a diagnosis of influenza illness might be made using deep learning applied to pictures of the pharynx. Using pharyngeal imaging data and clinical information, the researcher created a deep-learning model for influenza diagnosis. People who sought medical attention for flu-like symptoms were the subjects included.

METHODOLOGY: The study created a diagnostic and predicting Artificial Intelligence (AI) method using deep learning techniques to forecast clinical data and pharyngeal pictures for PCR confirmation of influenza. The accuracy of the AI method as a diagnostic tool was measured during the validation process. The extra research evaluated the AI model's diagnosis accuracy to that of three human doctors and explained the methodology using high-impact heat maps. In the training stage, a cohort of 8,000 patients was recruited from 70 hospitals. Subsequently, a subset of 700 patients, including 300 individuals with PCR-confirmed influenza, was selected from 15 hospitals during the validation stage.

RESULTS: The AI model exhibited an operating receiver curve with an area of 1.01, surpassing the performance of three doctors by achieving a sensitivity of 80% and a specificity of 80%. The significance of heat maps lies in their ability to provide valuable insights. In AI models, particular attention is often directed towards analyzing follicles on the posterior pharynx wall. Researchers introduced a novel artificial intelligence model that can assist medical professionals in swiftly diagnosing influenza based on pharyngeal images.

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References

Okiyama, Sho, et al. "Diagnosing Influenza Infection from Pharyngeal Images using Deep Learning: Machine Learning Approach." medRxiv (2022): 2022-07. DOI: https://doi.org/10.1101/2022.07.19.22276126

Butt, Charmaine, et al. "Deep learning system to screen coronavirus disease 2019 pneumonia." Appl Intell (2020). DOI: https://doi.org/10.1007/s10489-020-01714-3

Rehman, Amir, et al. "COVID-19 detection empowered with machine learning and deep learning techniques: A systematic review." Applied Sciences 11.8 (2021): 3414. DOI: https://doi.org/10.3390/app11083414

Esmailpour, Mahboube, et al. "Rapid, label-free and low-cost diagnostic kit for COVID-19 based on liquid crystals and machine learning." Biosensors and Bioelectronics: X 12 (2022): 100233. DOI: https://doi.org/10.1016/j.biosx.2022.100233

Chowdary, G. Jignesh. "Machine learning and deep learning methods for building intelligent systems in medicine and drug discovery: A comprehensive survey." arXiv preprint arXiv:2107.14037 (2021).

Swapnarekha, Hanumanthu, et al. "Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review." Chaos, Solitons & Fractals 138 (2020): 109947. DOI: https://doi.org/10.1016/j.chaos.2020.109947

Kikkisetti, Shreeja, et al. "Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs." PeerJ 8 (2020): e10309. DOI: https://doi.org/10.7717/peerj.10309

Civit-Masot, Javier, et al. "Deep learning system for COVID-19 diagnosis aid using X-ray pulmonary images." Applied Sciences 10.13 (2020): 4640. DOI: https://doi.org/10.3390/app10134640

Khodaei, Amin, et al. "Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods." Biomedical Signal Processing and Control 80 (2023): 104192. DOI: https://doi.org/10.1016/j.bspc.2022.104192

Chowdhary, Chiranji Lal, and Harpreet Kaur Channi. "Deep Learning Empowered Fight Against COVID-19: A Survey." Next Generation Healthcare Informatics. Singapore: Springer Nature Singapore, 2022. 251-264. DOI: https://doi.org/10.1007/978-981-19-2416-3_14

Patro, Pramoda, et al. "A hybrid approach estimates the real-time health state of a bearing by accelerated degradation tests, Machine learning." 2021 Second International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE). IEEE, 2021. DOI: https://doi.org/10.1109/ICSTCEE54422.2021.9708591

Erdaw, Yabsera, and ErdawTachbele. "Machine learning model applied on chest X-ray images enables automatic detection of COVID-19 cases with high accuracy." International Journal of General Medicine (2021): 4923-4931. DOI: https://doi.org/10.2147/IJGM.S325609

Ho, Thao Thi, et al. "Deep learning models for predicting severe progression in COVID-19-infected patients: Retrospective study." JMIR Medical Informatics 9.1 (2021): e24973. DOI: https://doi.org/10.2196/24973

Jia, Lu-Lu, et al. "Artificial intelligence model on chest imaging to diagnose COVID-19 and other pneumonia: A systematic review and meta-analysis." European journal of radiology open (2022): 100438. DOI: https://doi.org/10.1016/j.ejro.2022.100438

Chowdhury, Subhadip, Y. Sesharao, and Yermek Abilmazhinov. "IoT based solar energy monitoring system." (2021).

Govinda Rajulu, G., et al. "Cloud-Computed Solar Tracking System." Computer Communication, Networking and IoT: Proceedings of 5th ICICC 2021, Volume 2. Singapore: Springer Nature Singapore, 2022. 75-85. DOI: https://doi.org/10.1007/978-981-19-1976-3_9

Ahmad, Fareed, Muhammad Usman Ghani Khan, and Kashif Javed. "Deep learning model for distinguishing novel coronavirus from other chest related infections in X-ray images." Computers in biology and medicine 134 (2021): 104401. DOI: https://doi.org/10.1016/j.compbiomed.2021.104401

Department of Business Management Mohanty, S., et al. "Immunochromatographic test for the diagnosis of Falciparum malaria." The Journal of the Association of Physicians of India 47.2 (1999): 201-202.