Abstract
End-stage renal disease is the permanent cessation of the nephrons' (kidney cells) capability to eliminate unwarranted body fluids and noxious wastes from the body. Whereas dialysis bids a nonnatural medium to sustain kidney-like function, by only 10% to say the least, kidney transplant has effectively passed the former therapeutic modality, resulting in improved health outcomes and longevity for kidney transplant recipients. Glomerular filtration rate and kidney biopsy are the mainstay methods for evaluation of kidney allograft function after transplant. However, these widely used diagnostic methods are devalued by marginal sensitivity, delayed disease indication, and clinical complications. Recently, the role of deep learning in early diagnosis of acute kidney allograft rejection has been investigated. Because deep learning can capture delicate underlying hierarchical data patterns, the ability to enhance computer-aided diagnostic models for the early projection of acute kidney allograft rejection could bring a real paradigm shift in the tactic that kidney transplant physicians utilize, allowing for precise and early identification of acute kidney allograft rejection.