Advancements in Uric Acid Stone Detection: Integrating Deep Learning with CT Imaging and Clinical Assessments in the Upper Urinary Tract.

Journal: Urologia internationalis
PMID: 38432217

Abstract

INTRODUCTION: Among upper urinary tract stones, a significant proportion comprises uric acid stones. The aim of this study was to use machine learning techniques to analyze CT scans and blood and urine test data, with the aim of establishing multiple predictive models that can accurately identify uric acid stones.

Authors

  • Lichen Jin
    Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China, 495409279@qq.com.
  • Zongxin Chen
    Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China.
  • Yizhang Sun
    Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China.
  • Zhen Tian
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.
  • Xincheng Yi
    Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China.
  • Yuhua Huang
    Department of Pathology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in Southern China; Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China.

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