AIMC Topic: Tissue Donors

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Future technologies in total artificial heart development: can a robot become as good as a donor heart?

European heart journal Dec 21, 2022
Tissue Donors Robotics Heart Transplantation Heart, Artificial Humans Forecasting
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Impact of oocyte donor age and breed on embryo production in cattle, and relationship of dairy and beef embryo recipients on pregnancy and the subsequent performance of offspring: A review.

Reproduction, fertility, and development Dec 1, 2021
Genomic selection combined with in vitro embryo production (IVEP) with oocytes from heifer calves provides a powerful technology platform to reduce generation interval and significantly increase the rate of genetic gain in cattle. The ability to obta...
Oocytes Cattle Pregnancy Embryo, Mammalian Fertilization in Vitro Female Tissue Donors Animals Lactation
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Predicting Kidney Discard Using Machine Learning.

Transplantation Sep 1, 2021
BACKGROUND: Despite the kidney supply shortage, 18%-20% of deceased donor kidneys are discarded annually in the United States. In 2018, 3569 kidneys were discarded.
Kidney Transplantation Humans Female Young Adult Neural Networks, Computer Support Vector Machine Perfusion Risk Factors Decision Support Techniques Decision Trees Adult Donor Selection Aged Male Risk Assessment Predictive Value of Tests Middle Aged Tissue Donors Biopsy United States Kidney Function Tests
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[The first 50 robot-assisted donor nephrectomies : Lessons learned].

Der Urologe. Ausg. A Dec 1, 2020
BACKGROUND: Minimally invasive donor nephrectomy (DN) is considered the gold standard, but the role of robot-assisted surgery is still controversial.
Retrospective Studies Tissue Donors Kidney Treatment Outcome Humans Nephrectomy Robotics Living Donors Laparoscopy Germany
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Machine learning methods in organ transplantation.

Current opinion in organ transplantation Aug 1, 2020
PURPOSE OF REVIEW: Machine learning techniques play an important role in organ transplantation. Analysing the main tasks for which they are being applied, together with the advantages and disadvantages of their use, can be of crucial interest for cli...
Tissue Donors Organ Transplantation Tissue and Organ Procurement Donor Selection Humans Machine Learning
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Machine-learning algorithms for predicting results in liver transplantation: the problem of donor-recipient matching.

Current opinion in organ transplantation Aug 1, 2020
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...
Tissue and Organ Procurement Donor Selection Humans Neural Networks, Computer Machine Learning Prognosis Risk Factors Tissue Donors Graft Survival Liver Transplantation
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Artificial neural network and bioavailability of the immunosuppression drug.

Current opinion in organ transplantation Aug 1, 2020
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 ...
Artificial Intelligence Tissue Donors Graft Survival Proteomics Humans Neural Networks, Computer Immunosuppressive Agents Immunosuppression Therapy Biological Availability
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Graft Rejection Prediction Following Kidney Transplantation Using Machine Learning Techniques: A Systematic Review and Meta-Analysis.

Studies in health technology and informatics Aug 21, 2019
Kidney transplantation is recommended for patients with End-Stage Renal Disease (ESRD). However, complications, such as graft rejection are hard to predict due to donor and recipient variability. This study discusses the role of machine learning (ML)...
Graft Rejection Humans Machine Learning Kidney Transplantation Tissue Donors Graft Survival
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Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society Feb 1, 2018
In 2014, we reported a model for donor-recipient (D-R) matching in liver transplantation (LT) based on artificial neural networks (ANNs) from a Spanish multicenter study (Model for Allocation of Donor and Recipient in España [MADR-E]). The aim is to ...
Computer Simulation Female Reproducibility of Results Risk Factors Adult Male Middle Aged Risk Assessment Humans Neural Networks, Computer Algorithms Decision Support Techniques Liver Diseases Donor Selection London Treatment Outcome Time Factors Area Under Curve ROC Curve Liver Transplantation Tissue Donors Waiting Lists Graft Survival
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Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation.

Transplantation Apr 1, 2017
BACKGROUND: The ability to predict graft failure or primary nonfunction at liver transplant decision time assists utilization of scarce resource of donor livers, while ensuring that patients who are urgently requiring a liver transplant are prioritiz...
Patient Selection Treatment Failure Donor Selection Victoria Adult Graft Survival Humans Male Predictive Value of Tests Adolescent Machine Learning Algorithms Middle Aged Area Under Curve Time Factors Reproducibility of Results Decision Support Techniques Risk Assessment ROC Curve Liver Transplantation Risk Factors Databases, Factual Tissue Donors Aged Female Young Adult
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