Automated Machine Learning Segmentation and Measurement of Urinary Stones on CT Scan.

ABSTRACT:

Objectives

To evaluate the performance of an engineered machine learning algorithm to identify kidney stones and measure stone characteristics without the need for human input.

Methods

We performed a cross-sectional study of 94 children and adults who had kidney stones identified on non-contrast CT. A previously developed deep learning algorithm was trained to segment renal anatomy and kidney stones and to measure stone features. The performance and speed of the algorithm to measure renal anatomy and kidney stone features were compared to the current gold standard of human measurement performed by 3 independent reviewers.

Results

The algorithm was 100% sensitive and 100% specific in detecting individual kidney stones. The mean stone volume segmented by the algorithm w

SUBMITTER: Babajide R 

PROVIDER: S-EPMC9936246 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature