KECERDASAN BUATAN DAN DIAGNOSIS RADIOGRAFI COVID-19
Keywords:
Covid-19, Artificial Intelligence, dada, difraksi sinar-XSynopsis
Buku ini mengulas secara komprehensif pemanfaatan kecerdasan buatan (Artificial Intelligence/AI) dalam diagnosis radiografi Covid-19. Dimulai dengan pemahaman dasar mengenai AI serta prinsip kerjanya, pembaca diajak menelusuri bagaimana teknologi ini berkembang dan menjadi alat penting dalam menghadapi pandemi.
Pembahasan dilanjutkan dengan aspek teknis radiografi toraks, kualitas citra, serta gambaran khas Covid-19 pada foto toraks dan komplikasinya. Buku ini juga menyoroti pengalaman nyata para peneliti dan klinisi Indonesia dalam mengembangkan aplikasi AI, termasuk sistem diagnostik “SiCoSa”, sebagai inovasi nasional dalam mendukung deteksi cepat Covid-19.
Buku ini juga memaparkan pengalaman para peneliti dan klinisi dalam mengembangkan serta menerapkan aplikasi kecerdasan buatan untuk analisis foto toraks, termasuk sistem “SiCoSa” hasil kolaborasi ITS–UNAIR. Ditulis oleh pakar multidisiplin, buku ini menjadi rujukan penting bagi akademisi, tenaga kesehatan, peneliti AI, dan siapa pun yang ingin memahami integrasi teknologi cerdas dalam dunia radiologi dan penanganan pandemi.
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References
Ali, N., Sazzad, M. A., & Haque, M. (2020). Review of artificial intelligence techniques in imaging data for COVID-19 diagnosis. Journal of Imaging.
Apostolopoulos, I. D., & Mpesiana, T. A. (2020). Covid-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine.
Arun, K. M., et al. (2020). Evaluating the performance of deep learning models for COVID-19 detection. Radiology: Artificial Intelligence.
Bernheim, A., Mei, X., Huang, M., et al. (2020). Chest CT findings in coronavirus disease 2019 (COVID-19): Relationship to duration of infection. Radiology.
Cascella, M., Rajnik, M., Cuomo, A., et al. (2020). Features, evaluation and treatment of coronavirus (COVID-19). StatPearls Publishing.
Chen, J., Wu, L., Zhang, J., et al. (2020). Deep learning-based model for detecting COVID-19 pneumonia on high-resolution CT. Radiology.
Chowdhury, M. E., Rahman, T., Khandakar, A., et al. (2020). Can AI help in screening viral and COVID-19 pneumonia? IEEE Access.
Cohen, J. P., Morrison, P., & Dao, L. (2020). COVID-19 image data collection. arXiv.
European Society of Radiology (ESR). (2020). ESR guidelines on COVID-19 imaging. ESR Publications.
Fang, Y., Zhang, H., Xie, J., et al. (2020). Sensitivity of chest CT for COVID-19 compared to RT-PCR. Radiology.
Guan, W., Ni, Z., Hu, Y., et al. (2020). Clinical characteristics of coronavirus disease 2019 in China. New England Journal of Medicine.
Huang, C., Wang, Y., Li, X., et al. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet.
Kassani, S. H., et al. (2020). Automatic detection of COVID-19 using deep convolutional neural networks. Pattern Recognition Letters.
Li, L., Qin, L., Xu, Z., et al. (2020). Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology.
Liu, F., Zhang, Q., Huang, C., et al. (2020). CT quantification pulmonary inflammation for COVID-19 severe-risk prediction. European Respiratory Journal.
Ozturk, T., Talo, M., Yildirim, E. A., et al. (2020). Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine.
Pan, F., Ye, T., Sun, P., et al. (2020). Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumonia. Radiology.
Shi, H., Han, X., Jiang, N., et al. (2020). Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China. The Lancet Infectious Diseases.
Wang, L., & Wong, A. (2020). COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. arXiv.
World Health Organization (WHO). (2020). Coronavirus disease (COVID-19) pandemic situation reports. WHO Publications.
Zhou, S., Wang, Y., Zhu, T., et al. (2020). CT features of coronavirus disease 2019 pneumonia in Wuhan, China. Radiology Communications.
BISAC
- MED003000 Medical / Allied Health Services / General
- COM004000 Computers / Artificial Intelligence / General
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February 23, 2026
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