Automated Machine Learning Segmentation and Measurement of Urinary Stones on CT Scan
Rilwan Babajide, Katerina Lembrikova, Justin Ziemba, James Ding, Yuemeng Li, Antoine Selman Fermin, Yong Fan, Gregory E Tasian
Urology . 2022 Jul 28;S0090-4295(22)00626-4. doi: 10.1016/j.urology.2022.07.029. Online ahead of print.
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 three independent reviewers.
Results: The algorithm was 100% sensitive and 100% specific in detecting individual kidney stones. The mean stone volume segmented by the algorithm was smaller than that of human reviewers and had moderate overlap (Dice score: 0.66). There was substantial variation between human reviewers in total segmented stone volume (Jaccard score: 0.17) and volume of the single largest stone (Jaccard score: 0.33). Stone segmentations performed by the machine learning algorithm more precisely approximated stone borders than those performed by human reviewers on qualitative assessment.
Conclusions: An engineered machine learning algorithm can identify and characterize stones more accurately and reliably than humans, which has the potential to improve the precision and efficiency of assessing kidney stone burden.
Read Full Article Here: https://doi.org/10.1016/j.urology.2022.07.029