Prediction of the composition of urinary stones using deep learning-Reference-Cited by-同舟云学术

Prediction of the composition of urinary stones using deep learning

Published:2022 Issue:4 Volume:63 Page:441
ISSN:2466-0493
Container-title:Investigative and Clinical Urology
language:en
Short-container-title:Investig Clin Urol

Author:

Kim Ui Seok¹^ORCID,Kwon Hyo Sang¹^ORCID,Yang Wonjong¹^ORCID,Lee Wonchul¹^ORCID,Choi Changil¹^ORCID,Kim Jong Keun¹^ORCID,Lee Seong Ho¹^ORCID,Rim Dohyoung²^ORCID,Han Jun Hyun¹^ORCID

Affiliation:

1. Department of Urology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea.

2. Department of Cognitive Science, Yonsei University, Seoul, Korea.

Publisher

The Korean Urological Association

Subject

Urology

Link

https://icurology.org/pdf/10.4111/icu.20220062

Reference19 articles.

1. A survey on deep learning in medical image analysis

2. Epidemiology of stone disease across the world

3. Eleven-year Cumulative Incidence and Estimated Lifetime Prevalence of Urolithiasis in Korea: a National Health Insurance Service-National Sample Cohort Based Study

4. Study on the prevalence and incidence of urolithiasis in Korea over the last 10 years: An analysis of National Health Insurance Data

5. Recent surgical treatments for urinary stone disease in a Korean population: National population‐based study

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