Theranostic roles of machine learning in clinical management of kidney stone disease
Issued Date
2023-01-01
Resource Type
eISSN
20010370
Scopus ID
2-s2.0-85143660049
Journal Title
Computational and Structural Biotechnology Journal
Volume
21
Start Page
260
End Page
266
Rights Holder(s)
SCOPUS
Bibliographic Citation
Computational and Structural Biotechnology Journal Vol.21 (2023) , 260-266
Suggested Citation
Sassanarakkit S., Hadpech S., Thongboonkerd V. Theranostic roles of machine learning in clinical management of kidney stone disease. Computational and Structural Biotechnology Journal Vol.21 (2023) , 260-266. 266. doi:10.1016/j.csbj.2022.12.004 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/81805
Title
Theranostic roles of machine learning in clinical management of kidney stone disease
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
Kidney stone disease (KSD) is a common illness caused by deposition of solid minerals formed inside the kidney. The disease prevalence varies, based on sociodemographic, lifestyle, dietary, genetic, gender, age, environmental and climatic factors, but has been continuously increasing worldwide. KSD is a highly recurrent disease, and the recurrence rate is about 11% within two years after the stone removal. Recently, machine learning has been widely used for KSD detection, stone type prediction, determination of appropriate treatment modality and prediction of therapeutic outcome. This review provides a brief overview of KSD and discusses how machine learning can be applied to diagnostics, therapeutics and prognostics in clinical management of KSD for better therapeutic outcome.