This paper is in the following e-collection/theme issue:

Machine Learning (1501) Artificial Intelligence (1421) Clinical Informatics (1050) Bioinformatics Methods and Algorithms (14) Tools, Programs and Algorithms (275) Imaging Informatics (200)

Published on in Vol 9, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27177, first published .
Use of Deep Learning to Predict Acute Kidney Injury After Intravenous Contrast Media Administration: Prediction Model Development Study

Use of Deep Learning to Predict Acute Kidney Injury After Intravenous Contrast Media Administration: Prediction Model Development Study

Use of Deep Learning to Predict Acute Kidney Injury After Intravenous Contrast Media Administration: Prediction Model Development Study

Authors of this article:

Donghwan Yun1, 2 Author Orcid Image ;   Semin Cho2 Author Orcid Image ;   Yong Chul Kim2 Author Orcid Image ;   Dong Ki Kim2 Author Orcid Image ;   Kook-Hwan Oh2 Author Orcid Image ;   Kwon Wook Joo2 Author Orcid Image ;   Yon Su Kim1, 2 Author Orcid Image ;   Seung Seok Han1, 2 Author Orcid Image
Article Authors Cited by (7) Tweetations (4) Metrics

Journals

  1. Thongprayoon C, Pattharanitima P, Kattah A, Mao M, Keddis M, Dillon J, Kaewput W, Tangpanithandee S, Krisanapan P, Qureshi F, Cheungpasitporn W. Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury. Journal of Clinical Medicine 2022;11(21):6264 View
  2. Yan P, Duan S, Luo X, Zhang N, Deng Y. Development and validation of a deep neural network–based model to predict acute kidney injury following intravenous administration of iodinated contrast media in hospitalized patients with chronic kidney disease: a multicohort analysis. Nephrology Dialysis Transplantation 2023;38(2):352 View
  3. Gupta S, Motwani S, Seitter R, Wang W, Mu Y, Chute D, Sise M, Glazer D, Rosner B, Curhan G. Development and Validation of a Risk Model for Predicting Contrast-Associated Acute Kidney Injury in Patients With Cancer: Evaluation in Over 46,000 CT Examinations. American Journal of Roentgenology 2023;221(4):486 View
  4. Ma M, Wan X, Chen Y, Lu Z, Guo D, Kong H, Pan B, Zhang H, Chen D, Xu D, Sun D, Lang H, Zhou C, Li T, Cao C. A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study. Journal of Translational Medicine 2023;21(1) View
  5. Chen Y, Liu C, Shen Y, Kuo Y, Ko C, Chen T, Wu T, Shih Y. Development of real-time individualized risk prediction models for contrast associated acute kidney injury and 30-day dialysis after contrast enhanced computed tomography. European Journal of Radiology 2023;167:111034 View
  6. Najdaghi S, Davani D, Shafie D, Alizadehasl A. Predictive performance of machine learning models for kidney complications following coronary interventions: a systematic review and meta-analysis. International Urology and Nephrology 2024 View
  7. Al-Absi D, Simsekler M, Omar M, Anwar S. Exploring the role of Artificial Intelligence in Acute Kidney Injury management: a comprehensive review and future research agenda. BMC Medical Informatics and Decision Making 2024;24(1) View