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Graft Survival

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Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study.

F1000Research
A mechanism to predict graft failure before the actual kidney transplantation occurs is crucial to clinical management of chronic kidney disease patients.  Several kidney graft outcome prediction models, developed using machine learning methods, are...

Autologous fat graft assisted by stromal vascular fraction improves facial skin quality: A randomized controlled trial.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS
BACKGROUND: Cell-assisted lipotransfer (CAL) promotes the survival of fat grafts with high vascular density and improves skin quality by increasing collagen content. However, no study has quantified the changes on the skin surface, and rigorous metho...

[Renal graft survival in patients transplanted from organs of deceased donors].

Revista medica del Instituto Mexicano del Seguro Social
BACKGROUND: In Mexico, out of the total number of transplants it was reported, in 2014, a frequency of 29% of deceased donor renal transplantation (DDRT). The use of kidneys from deceased elderly donors is increasing over the years. Currently, some a...

Patterns of 1,748 Unique Human Alloimmune Responses Seen by Simple Machine Learning Algorithms.

Frontiers in immunology
Allele specific antibody response against the polymorphic system of HLA is the allogeneic response marker determining the immunological risk for graft acceptance before and after organ transplantation and therefore routinely studied during the patien...

Machine-learning algorithms for predicting results in liver transplantation: the problem of donor-recipient matching.

Current opinion in organ transplantation
PURPOSE OF REVIEW: Classifiers based on artificial intelligence can be useful to solve decision problems related to the inclusion or removal of possible liver transplant candidates, and assisting in the heterogeneous field of donor-recipient (D-R) ma...

Artificial neural network and bioavailability of the immunosuppression drug.

Current opinion in organ transplantation
PURPOSE OF REVIEW: The success of organ transplant is determined by number of demographic, clinical, immunological and genetic variables. Artificial intelligence tools, such as artificial neural networks (ANNs) or classification and regression trees ...

Machine Learning Applications in Solid Organ Transplantation and Related Complications.

Frontiers in immunology
The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning...

Deep learning identified pathological abnormalities predictive of graft loss in kidney transplant biopsies.

Kidney international
Interstitial fibrosis, tubular atrophy, and inflammation are major contributors to kidney allograft failure. Here we sought an objective, quantitative pathological assessment of these lesions to improve predictive utility and constructed a deep-learn...

Identify Hard-to-Place Kidneys for Early Engagement in Accelerated Placement With a Deep Learning Optimization Approach.

Transplantation proceedings
Recommended practices that follow match-run sequences for hard-to-place kidneys succumb to many declines, accruing cold ischemic time and exacerbating kidney quality that may lead to unnecessary kidney discard. Hard-to-place deceased donor kidneys ac...