Browse > Article
http://dx.doi.org/10.3745/KTCCS.2021.10.10.285

Development of Web Service for Liver Cirrhosis Diagnosis Based on Machine Learning  

Noh, Si-Hyeong (원광대학교 의료융합연구센터)
Kim, Ji-Eon (원광대학교 의료융합연구센터)
Lee, Chungsub (원광대학교 의료융합연구센터)
Kim, Tae-Hoon (원광대학교병원 스마트사업팀)
Kim, KyungWon (서울아산병원 Asan Image Metrics)
Yoon, Kwon-Ha (원광대학교병원)
Jeong, Chang-Won (원광대학교병원 스마트사업팀)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.10, no.10, 2021 , pp. 285-290 More about this Journal
Abstract
In the medical field, disease diagnosis and prediction research using artificial intelligence technology is being actively conducted. It is being released as a variety of products for disease diagnosis and prediction, which are most widely used in the application of artificial intelligence technology based on medical images. Artificial intelligence is being applied to diagnose diseases, to classify diseases into benign and malignant, and to separate disease regions for use in identification or reading according to the risk of disease. Recently, in connection with cloud technology, its utility as a service product is increasing. Among the diseases dealt with in this paper, liver disease is a disease with very high risk because it is difficult to diagnose early due to the lack of pain. Artificial intelligence technology was introduced based on medical images as a non-invasive diagnostic method for diagnosing these diseases. We describe the development of a web service to help the most meaningful clinical reading of liver cirrhosis patients. Then, it shows the web service process and shows the operation screen of each process and the final result screen. It is expected that the proposed service will be able to diagnose liver cirrhosis at an early stage and help patients recover through rapid treatment.
Keywords
DICOM; Medical Bigdata; Artificial Intelligence Training Platform; Machine Learning; Deep Learning;
Citations & Related Records
연도 인용수 순위
  • Reference
1 H. W. Lee, J. J. Sung, and S. H. Ahn, "Artificial intelligence in liver disease," Journal of Gastroenterology and Hepatology, Vol.36, Iss.3, pp.539-542, Mar. 2021.   DOI
2 S. Kaur, et al., "Medical diagnostic systems using artificial intelligence (AI) algorithms: Principles and perspectives," in IEEE Access, Vol.8, pp.228049-228069, 2020, doi: 10.1109/ACCESS.2020.3042273.   DOI
3 C. H. Jiang, et al., "Bioinformatics-based screening of key genes for transformation of liver cirrhosis to hepatocellular carcinoma," Journal of Translational Medicine, Vol.18, No.40, 2020. doi: 10.1186/s12967-020-02229-8.   DOI
4 Y. Zeng, et al., "Gut microbiota dysbiosis in patients with hepatitis B virus-induced chronic liver disease covering chronic hepatitis, liver cirrhosis and hepatocellular carcinoma," Journal of Viral Hepatitis, Vol.27, Iss.2, pp.143-155, Oct. 2019.   DOI
5 M. Nouri-Vaskeh, A. Malek Mahdavi, H. Afshan, L. Alizadeh, and M. Zarei, "Effect of curcumin supplementation on disease severity in patients with liver cirrhosis: A randomized controlled trial," Phytotherapy Research, Vol.34, Iss.6, pp.1446-1454, Feb. 2020.   DOI
6 O. Polat, A. Polat, and T. Ekici, "Automatic classification of volcanic rocks from thin section images using transfer learning networks," Neural Computing and Applications, 2021. doi: 10.1007/s00521-021-05849-3.   DOI
7 E. Y. Kwon, C.-W. Jeong, D. M. Kang, Y. R. Kim, Y. H. Lee, and K.-H. Yoon, "Development of common data module extension for radiology data (R_CDM): A pilot study to predict outcome of liver cirrhosis with using portal phase abdominal computed tomography data," ECR 2019, doi: 10.26044/ecr2019/C-1876.   DOI
8 J. S. Kim, and T. S. Chung, "Deep learning applications in medical image analysis", IEEE Access, Vol.6, pp.9375-9389, Dec. 2017, doi: 10.1109/ACCESS.2017.2788044,   DOI
9 Windowing (CT) [Internet], https://radiopaedia.org/articles/windowing-ct.
10 S. Gupta, G. Karanth, N. Pentapati, and V. R. Badri Prasad, "A web based framework for liver disease diagnosis using combined machine learning models," 2020 International Conference on Smart Electronics and Communication (ICOSEC), Oct. 2020, doi: 10.1109/ICOSEC49089.2020.9215454.   DOI