A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction

• Published in: Scientific reports Vol. 9; no. 1; pp. 5948 - 11
• Main Authors: Abdeltawab, Hisham, Shehata, Mohamed, Shalaby, Ahmed, Khalifa, Fahmi, Mahmoud, Ali, El-Ghar, Mohamed Abou, Dwyer, Amy C., Ghazal, Mohammed, Hajjdiab, Hassan, Keynton, Robert, El-Baz, Ayman
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 11.04.2019; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308/53/2421; 692/308/575; Adolescent; Adult; Aged; Algorithms; Biomarkers; Creatinine; Diagnosis, Computer-Assisted - methods; Diffusion; Diffusion coefficient; Diffusion Magnetic Resonance Imaging; Female; Follow-Up Studies; Glomerular Filtration Rate; Graft rejection; Graft Rejection - diagnosis; Graft Rejection - etiology; Graft Rejection - pathology; Graft Survival; Humanities and Social Sciences; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Kidney Function Tests; Kidney transplantation; Kidney Transplantation - adverse effects; Magnetic resonance imaging; Male; Middle Aged; multidisciplinary; Neural networks; Neural Networks, Computer; NMR; Nuclear magnetic resonance; Perfusion; Postoperative Complications - diagnosis; Postoperative Complications - etiology; Postoperative Complications - pathology; Prognosis; Risk Factors; Science; Science (multidisciplinary); Transplants & implants; Young Adult

This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the fusion of both imaging markers and clinical biomark...