Theranostic roles of machine learning in clinical management of kidney stone disease

Elsevier

Available online 5 December 2022

Computational and Structural Biotechnology JournalAuthor links open overlay panelAbstract

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.

Keywords

Artificial intelligence

Deep learning

Diagnostics

Outcome

Prognostics

Recurrence

Therapeutics

© 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

留言 (0)

沒有登入
gif