BACKGROUND: An emerging strategy to expand the donor pool is the use of a steatotic donor liver (SDLs; ≥ 30% macrosteatosis on biopsy). With the obesity epidemic and prevalence of nonalcoholic fatty liver disease, SDLs have been reported in 59% of al...
Lesion scores on procurement donor biopsies are commonly used to guide organ utilization for deceased-donor kidneys. However, frozen sections present challenges for histological scoring, leading to inter- and intra-observer variability and inappropri...
ASAIO journal (American Society for Artificial Internal Organs : 1992)
38552178
In kidney transplantation, pairing recipients with the highest longevity with low-risk allografts to optimize graft-donor survival is a complex challenge. Current risk prediction models exhibit limited discriminative and calibration capabilities and ...
INTRODUCTION: Despite significant progress over the last decades in the survival of kidney allografts, several risk factors remain contributing to worsening kidney function or even loss of transplants. We aimed to evaluate a new machine learning meth...
Research on solid organ transplantation has taken advantage of the substantial acquisition of medical data and the use of artificial intelligence (AI) and machine learning (ML) to answer diagnostic, prognostic, and therapeutic questions for many year...
PURPOSE: This study aimed to predict early graft failure (GF) in patients who underwent Descemet membrane endothelial keratoplasty based on donor characteristics.
The decision to accept a deceased donor organ offer for transplant, or wait for something potentially better in the future, can be challenging. Clinical decision support tools predicting transplant outcomes are lacking. This project uses interpretabl...
PURPOSE: To predict 10-year graft survival after deep anterior lamellar keratoplasty (DALK) and penetrating keratoplasty (PK) using a machine learning (ML)-based interpretable risk score.
International journal of surgery (London, England)
39116448
BACKGROUND: Accurate forecasting of clinical outcomes after kidney transplantation is essential for improving patient care and increasing the success rates of transplants. The authors' study employs advanced machine learning (ML) algorithms to identi...
Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
38684469
BACKGROUND: Outcome prediction for live-donor kidney transplantation improves clinical and patient decisions and donor selection. However, the currently used models are of limited discriminative or calibration power and there is a critical need to im...