Articles

Artificial Intelligence in Healthcare

AI, Machine Learning, and Deep and Intelligent Medicine Simplified for Everyone

Academic Edition, Chapter 47 - References/Further Reading

 

  1. Yuan Q, Zhang H, Deng T, Tang S, Yuan X, Tang W, Xie Y, Ge H, Wang X, Zhou Q, Xiao X. Role of Artificial Intelligence in Kidney Disease. International Journal of Medical Sciences. 2020;17(7):970.
  2. Koyner JL, Carey KA, Edelson DP, Churpek MM. The development of a machine learning inpatient acute kidney injury prediction model. Critical care medicine. 2018 Jul 1;46(7):1070-7.
  3. Zimmerman LP, Reyfman PA, Smith AD, Zeng Z, Kho A, Sanchez-Pinto LN, Luo Y. Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements. BMC medical informatics and decision making. 2019 Jan;19(1):1-2.
  4. van Gastel MD, Edwards ME, Torres VE, Erickson BJ, Gansevoort RT, Kline TL. Automatic measurement of kidney and liver volumes from MR images of patients affected by autosomal dominant polycystic kidney disease. Journal of the American Society of Nephrology. 2019 Aug 1;30(8):1514-22.
  5. Tey WK, Kuang YC, Ooi MP, Khoo JJ. Automated quantification of renal interstitial fibrosis for computer-aided diagnosis: A comprehensive tissue structure segmentation method. Computer methods and programs in biomedicine. 2018 Mar 1;155:109-20.
  6. Kannan S, Morgan LA, Liang B, Cheung MG, Lin CQ, Mun D, Nader RG, Belghasem ME, Henderson JM, Francis JM, Chitalia VC. Segmentation of glomeruli within trichrome images using deep learning. Kidney international reports. 2019 Jul 1;4(7):955-62.
  7. Hueso M, Vellido A, Montero N, Barbieri C, Ramos R, Angoso M, Cruzado JM, Jonsson A. Artificial intelligence for the artificial kidney: Pointers to the future of a personalized hemodialysis therapy. Kidney Diseases. 2018;4(1):1-9.
  8. Barbieri C, Cattinelli I, Neri L, Mari F, Ramos R, Brancaccio D, Canaud B, Stuard S. Development of an artificial intelligence model to guide the management of blood pressure, fluid volume, and dialysis dose in end-stage kidney disease patients: proof of concept and first clinical assessment. Kidney Diseases. 2019;5(1):28-33.
  9. Brier ME, Gaweda AE. Artificial intelligence for optimal anemia management in end-stage renal disease. Kidney International. 2016 Aug 1;90(2):259-61.
  10. Niel O, Bastard P, Boussard C, Hogan J, Kwon T, Deschênes G. Artificial intelligence outperforms experienced nephrologists to assess dry weight in pediatric patients on chronic hemodialysis. Pediatric Nephrology. 2018 Oct 1;33(10):1799-803.
  11. Bhatia G, Wagle M, Jethnani N, Bhagtani J, Chandak A. Machine learning for prediction of life of arteriovenous fistula. In2018 3rd International Conference for Convergence in Technology (I2CT) 2018 Apr 6 (pp. 1-6). IEEE.
  12. Shah M, Naik N, Somani BK, Hameed BZ. Artificial intelligence (AI) in urology-Current use and future directions: An iTRUE study. Turkish Journal of Urology. 2020 May 27.