Quantitative Ultrasound Assessment of Liver Fat Using Deep Learning and Clinical Data Integration (original) (raw)

References

  1. Wong, V. W., Ekstedt, M., Wong, G. L., & Hagström, H. (2023). Changing epidemiology, global trends and implications for outcomes of NAFLD. Journal of Hepatology, 79(3), 842–852. https://doi.org/10.1016/j.jhep.2023.04.036
    Article PubMed Google Scholar
  2. Simon, T. G., Roelstraete, B., Hagström, H., Sundström, J., & Ludvigsson, J. F. (2022). Non-alcoholic fatty liver disease and incident major adverse cardiovascular events: Results from a nationwide histology cohort. Gut, 71(9), 1867–1875. https://doi.org/10.1136/gutjnl-2021-325724
    Article PubMed Google Scholar
  3. Kim, D. Y. (2024). Changing etiology and epidemiology of hepatocellular carcinoma: Asia and worldwide. Journal Of Liver Cancer, 24(1), 62–70. https://doi.org/10.17998/jlc.2024.03.13
    Article PubMed PubMed Central Google Scholar
  4. Ballestri, S., Romagnoli, D., Nascimbeni, F., Francica, G., & Lonardo, A. (2015). Role of ultrasound in the diagnosis and treatment of nonalcoholic fatty liver disease and its complications. Expert Review of Gastroenterology & Hepatology, 9(5), 603–627. https://doi.org/10.1586/17474124.2015.1007955
    Article CAS Google Scholar
  5. Li, G., Zhang, X., Lin, H., Liang, L. Y., Wong, G. L., & Wong, V. W. (2022). Non-invasive tests of non-alcoholic fatty liver disease. Chinese Medical Journal, 135(5), 532–546. https://doi.org/10.1097/cm9.0000000000002027
    Article PubMed PubMed Central CAS Google Scholar
  6. Park, C. C., Nguyen, P., Hernandez, C., Bettencourt, R., Ramirez, K., Fortney, L., Hooker, J., Sy, E., Savides, M. T., Alquiraish, M. H., Valasek, M. A., Rizo, E., Richards, L., Brenner, D., Sirlin, C. B., & Loomba, R. (2017). Magnetic resonance elastography vs transient elastography in detection of fibrosis and noninvasive measurement of steatosis in patients with Biopsy-Proven nonalcoholic fatty liver disease. Gastroenterology, 152(3), 598–607e592. https://doi.org/10.1053/j.gastro.2016.10.026
    Article PubMed Google Scholar
  7. Hernaez, R., Lazo, M., Bonekamp, S., Kamel, I., Brancati, F. L., Guallar, E., & Clark, J. M. (2011). Diagnostic accuracy and reliability of ultrasonography for the detection of fatty liver: A meta-analysis. Hepatology, 54(3), 1082–1090. https://doi.org/10.1002/hep.24452
    Article PubMed Google Scholar
  8. Ferraioli, G., & Soares Monteiro, L. B. (2019). Ultrasound-based techniques for the diagnosis of liver steatosis. World Journal of Gastroenterology, 25(40), 6053–6062. https://doi.org/10.3748/wjg.v25.i40.6053
    Article PubMed PubMed Central Google Scholar
  9. Alshagathrh, F. M., & Househ, M. S. (2022). Artificial intelligence for detecting and quantifying fatty liver in ultrasound images: A systematic review. Bioengineering. https://doi.org/10.3390/bioengineering9120748
    Article PubMed PubMed Central Google Scholar
  10. Cao, W., An, X., Cong, L., Lyu, C., Zhou, Q., & Guo, R. (2020). Application of deep learning in quantitative analysis of 2-dimensional ultrasound imaging of nonalcoholic fatty liver disease. Journal of Ultrasound in Medicine, 39(1), 51–59. https://doi.org/10.1002/jum.15070
    Article PubMed Google Scholar
  11. Byra, M., Styczynski, G., Szmigielski, C., Kalinowski, P., Michałowski, Ł, Paluszkiewicz, R., Ziarkiewicz-Wróblewska, B., Zieniewicz, K., Sobieraj, P., & Nowicki, A. (2018). Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. International Journal of Computer Assisted Radiology and Surgery, 13(12), 1895–1903. https://doi.org/10.1007/s11548-018-1843-2
    Article PubMed PubMed Central Google Scholar
  12. Liu, F., Goh, G. B. B., Tiniakos, D., Wee, A., Leow, W. Q., Zhao, J. M., Rao, H. Y., Wang, X. X., Wang, Q., & Wan, W. K. (2020). qFIBS: An automated technique for quantitative evaluation of fibrosis, inflammation, ballooning, and steatosis in patients with nonalcoholic steatohepatitis. Hepatology, 71(6), 1953–1966.
    Article PubMed CAS Google Scholar
  13. Pirmoazen, A. M., Khurana, A., El Kaffas, A., & Kamaya, A. (2020). Quantitative ultrasound approaches for diagnosis and monitoring hepatic steatosis in nonalcoholic fatty liver disease. Theranostics, 10(9), 4277–4289. https://doi.org/10.7150/thno.40249
    Article PubMed PubMed Central CAS Google Scholar
  14. Gheorghe, E. C., Nicolau, C., Kamal, A., Udristoiu, A., Gruionu, L., & Saftoiu, A. (2023). Artificial intelligence (AI)-enhanced ultrasound techniques used in non-alcoholic fatty liver disease: Are they ready for prime time? Applied Sciences, 13(8), Article 5080.
    Article CAS Google Scholar
  15. Reis, D., Hong, K. J. (2023). J,. Real-time flying object detection with yolov8. arXiv. https://arxiv.org/abs/2305.09972
  16. Rhyou, S. Y., & Yoo, J. C. (2021). Cascaded deep learning neural network for automated liver steatosis diagnosis using ultrasound images. Sensors. https://doi.org/10.3390/s21165304
    Article PubMed PubMed Central Google Scholar
  17. S, R. (2018). 2020 Nov 11). Model evaluation, model selection, and algorithm selection in machine learning arXiv. https://arxiv.org/abs/1811.12808
  18. Pu, K., Wang, Y., Bai, S., Wei, H., Zhou, Y., Fan, J., & Qiao, L. (2019). Diagnostic accuracy of controlled attenuation parameter (CAP) as a non-invasive test for steatosis in suspected non-alcoholic fatty liver disease: A systematic review and meta-analysis. BMC Gastroenterology, 19(1), Article 51. https://doi.org/10.1186/s12876-019-0961-9
    Article PubMed PubMed Central Google Scholar
  19. Yoo, J. J., Yoo, Y. J., Moon, W. R., Kim, S. U., Jeong, S. W., Park, H. N., Park, M. G., Jang, J. Y., Park, S. Y., Kim, B. K., Park, J. Y., Kim, D. Y., Ahn, S. H., Han, K. H., Kim, S. G., Kim, Y. S., Kim, J. H., Yeon, J. E., & Byun, K. S. (2020). Correlation of the grade of hepatic steatosis between controlled attenuation parameter and ultrasound in patients with fatty liver: A multi-center retrospective cohort study. Korean Journal of Internal Medicine, 35(6), 1346–1353. https://doi.org/10.3904/kjim.2018.309
    Article PubMed Google Scholar
  20. Decharatanachart, P., Chaiteerakij, R., Tiyarattanachai, T., & Treeprasertsuk, S. (2021). Application of artificial intelligence in non-alcoholic fatty liver disease and liver fibrosis: A systematic review and meta-analysis. Therapeutic Advances in Gastroenterology, 14, 17562848211062807.
    Article PubMed PubMed Central CAS Google Scholar
  21. Alshagathrh, F. M., & Househ, M. S. (2022). Artificial intelligence for detecting and quantifying fatty liver in ultrasound images: A systematic review. Bioengineering, 9(12), Article 748.
    Article PubMed PubMed Central Google Scholar
  22. Nduma, B. N., Al-Ajlouni, Y. A., & Njei, B. (2023). The application of artificial intelligence (AI)-based ultrasound for the diagnosis of fatty liver disease: A systematic review. Cureus, 15(12), Article e50601. https://doi.org/10.7759/cureus.50601
    Article PubMed PubMed Central Google Scholar
  23. Acharya, U. R., Sree, S. V., Ribeiro, R., Krishnamurthi, G., Marinho, R. T., Sanches, J., & Suri, J. S. (2012). Data mining framework for fatty liver disease classification in ultrasound: A hybrid feature extraction paradigm. Medical Physics, 39(7), 4255–4264. https://doi.org/10.1118/1.4725759
    Article PubMed Google Scholar
  24. Zamanian, H., Mostaar, A., Azadeh, P., & Ahmadi, M. (2021). Implementation of combinational deep learning algorithm for non-alcoholic fatty liver classification in ultrasound images. Journal of Biomedical Physics & Engineering, 11(1), 73–84. https://doi.org/10.31661/jbpe.v0i0.2009-1180
    Article CAS Google Scholar
  25. Han, A., Byra, M., Heba, E., Andre, M. P., Erdman, J. W. Jr., Loomba, R., Sirlin, C. B., & O’Brien, W. D. Jr. (2020). Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat with radiofrequency ultrasound data using One-dimensional convolutional neural networks. Radiology, 295(2), 342–350. https://doi.org/10.1148/radiol.2020191160
    Article PubMed Google Scholar
  26. Gaber, A., Youness, H. A., Hamdy, A., Abdelaal, H. M., & Hassan, A. M. (2022). Automatic classification of fatty liver disease based on supervised learning and genetic algorithm. Applied Sciences, 12(1), Article 521.
    Article CAS Google Scholar

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