Artificial intelligence and kidney transplantation

Abstract

Artificial intelligence and its primary subfield, machine learning, have started to gain widespread use in medicine, including the field of kidney transplantation. We made a review of the literature that used artificial intelligence techniques in kidney transplantation. We located six main areas of kidney transplantation that artificial intelligence studies are focused on: Radiological evaluation of the allograft, pathological evaluation including molecular evaluation of the tissue, prediction of graft survival, optimizing the dose of immunosuppression, diagnosis of rejection, and prediction of early graft function. Machine learning techniques provide increased automation leading to faster evaluation and standardization, and show better performance compared to traditional statistical analysis. Artificial intelligence leads to improved computer-aided diagnostics and quantifiable personalized predictions that will improve personalized patient care.

Artificial Intelligence in Organ Transplantation: Surveying Current Applications, Addressing Challenges and Exploring Frontiers

“…Artificial intelligence (AI) is a modern approach that differs from traditional statistical methods [5,6]. It can handle complicated datasets with effectiveness because it can consider several variables at once [7]. This attribute is particularly vital in the field of solid organ transplantation, where conventional statistical methods may not suffice in offering comprehensive evaluations of diverse outcome measures, particularly intricate events such as graft loss.…”

“…Beyond its influence in many different fields of medicine, AI has left a significant impact on the kidney transplant field [51]. It has significantly enhanced our ability to match kidney donors and recipients with precision [52,53], predict kidney graft survival with previously unobtainable accuracy [49], diagnose rejection, optimize immunosuppressive dosage, and provide post-transplant care [7], and enable a more thorough and prognostic approach to patient care throughout the transplant process [8,48].…”

A Machine Learning Prediction Model for Immediate Graft Function After Deceased Donor Kidney Transplantation

“…27,28 Another modeling approach recently used in the healthcare field involves the use of artificial intelligence or, more specifically, machine learning (ML), in which algorithms learn patterns from a dataset without being explicitly programmed with prespecified rules. 29 Artificial intelligence has been used more frequently in medicine and specifically in transplantation in the last decade, with an increase in publications in the last 2 y. [30][31][32][33] There are ML models that predict allograft survival, 34,35 DGF, [36][37][38][39] immunosuppressive dose optimization, 40 rejection diagnosis, 41 and waitlist time for KTx.…”

A paradigm shift from office to home-based blood pressure measurement approaches in kidney transplant recipients

“…A recent advanced AI technology analyzing the light reflected from bare skin on an individual's face, using an optical technique known as photoplethysmography may provide a promising way to measure BP by using the smartphone camera for contactless, video-based technique requiring only a finger clip, arm cuff or other hardware [40]. There is no specific published study implementing AI/ML for BP measurement in KTR [41]; however, the application of AI for assessing BP and volume status in hemodialysis patients showed the potential utility of AI to guide patient care and management. Current and future implementation of AI/ML for BP measurement and hypertension management in KTR also requires collaboration between several stakeholders including clinicians, ML experts, and policymakers.…”