TY - GEN
T1 - Multiclass Classification of Nonalcoholic Steatohepatitis Mouse Models Using Dielectric Properties as Disease Biomarker
AU - Samaddar, Poulami
AU - Gopalakrishnan, Keerthy
AU - Anvekar, Priyanka
AU - Samadder, Poushali
AU - Igreja E Sa, Ivone Cristina
AU - Bayer, Rachel
AU - Gaddam, Sunil
AU - Mitra, Dipankar
AU - Roy, Sayan
AU - Hirsova, Petra
AU - Arunachalam, Shivaram P.
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported in parts by GIH Division’s 2021 Research Innovation Award for Shivaram P. Arunachalam. Petra Hirsova received support from the Mayo Clinic Center for Biomedical Discovery, and National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health (NIH) under the Award Numbers P30DK084567 and R01DK130884.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Non-Alcoholic Steatohepatitis (NASH) is known as the key cause of cirrhosis in adults. As the name suggests, NASH is described as the excessive fat accumulation in the nonalcoholics. The genomic components play a vital role in the development and progression of the NASH. The existing imaging modalities have limited use in the diagnosis of NASH leading to delayed presentation of the disease. Owing to this, risk of hepatocellular carcinoma and the need for liver transplant is on a rising trend in patients with NASH. Even with the advent of new diagnostic techniques, biopsy is still considered the fundamental tool for confirming NASH. However, due to the highly invasive nature of the biopsy, its broad application becomes very difficult. Therefore, it is important to validate a tool which will identify the detection and progression of steatohepatitis and help in the timely diagnosis of the disease. Dielectric spectroscopy can be used to measure the dielectric properties of the tissue as a function of the frequency. This work introduces a feasibility study to classify between murine healthy liver and liver affected by two types of diets including nonalcoholic steatohepatitis using dielectric property of liver tissue as a biomarker. Multiclass classification using different machine learning models is performed. Among them, K-Nearest Neighbors Classifier and Random Forest Classifier showed good accuracy i.e., 89% and 90% respectively.
AB - Non-Alcoholic Steatohepatitis (NASH) is known as the key cause of cirrhosis in adults. As the name suggests, NASH is described as the excessive fat accumulation in the nonalcoholics. The genomic components play a vital role in the development and progression of the NASH. The existing imaging modalities have limited use in the diagnosis of NASH leading to delayed presentation of the disease. Owing to this, risk of hepatocellular carcinoma and the need for liver transplant is on a rising trend in patients with NASH. Even with the advent of new diagnostic techniques, biopsy is still considered the fundamental tool for confirming NASH. However, due to the highly invasive nature of the biopsy, its broad application becomes very difficult. Therefore, it is important to validate a tool which will identify the detection and progression of steatohepatitis and help in the timely diagnosis of the disease. Dielectric spectroscopy can be used to measure the dielectric properties of the tissue as a function of the frequency. This work introduces a feasibility study to classify between murine healthy liver and liver affected by two types of diets including nonalcoholic steatohepatitis using dielectric property of liver tissue as a biomarker. Multiclass classification using different machine learning models is performed. Among them, K-Nearest Neighbors Classifier and Random Forest Classifier showed good accuracy i.e., 89% and 90% respectively.
KW - Artificial Intelligence
KW - complex permittivity
KW - dielectric properties
KW - machine learning
KW - multiclass classification
KW - Nonalcoholic Steatohepatitis
UR - http://www.scopus.com/inward/record.url?scp=85146673498&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146673498&partnerID=8YFLogxK
U2 - 10.1109/BIBM55620.2022.9995712
DO - 10.1109/BIBM55620.2022.9995712
M3 - Conference contribution
AN - SCOPUS:85146673498
T3 - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
SP - 3137
EP - 3143
BT - Proceedings - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
A2 - Adjeroh, Donald
A2 - Long, Qi
A2 - Shi, Xinghua
A2 - Guo, Fei
A2 - Hu, Xiaohua
A2 - Aluru, Srinivas
A2 - Narasimhan, Giri
A2 - Wang, Jianxin
A2 - Kang, Mingon
A2 - Mondal, Ananda M.
A2 - Liu, Jin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022
Y2 - 6 December 2022 through 8 December 2022
ER -