Transplantation

Machine learning improves the accuracy of graft weight prediction in living donor liver transplantation.

Precise graft weight (GW) estimation is essential for planning living donor liver transplantation to select grafts of adequate size for the recipient. This study aimed to investigate whether a machine-learning model can improve the accuracy of GW estimation. Data from 872 consecutive living donors of a left lateral sector, left lobe, or right lobe to adults or children for living-related liver tra...

A deep learning based dual encoder-decoder framework for anatomical structure segmentation in chest X-ray images.

Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct structures, variations in the anatomical structure shape among different individuals, the presence of medical tools, such as pacemakers and catheters, an...

The Dresden Surgical Anatomy Dataset for Abdominal Organ Segmentation in Surgical Data Science.

Laparoscopy is an imaging technique that enables minimally-invasive procedures in various medical disciplines including abdominal surgery, gynaecology...

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

Recommended practices that follow match-run sequences for hard-to-place kidneys succumb to many declines, accruing cold ischemic time and exacerbating...

Recognizing the Differentiation Degree of Human Induced Pluripotent Stem Cell-Derived Retinal Pigment Epithelium Cells Using Machine Learning and Deep Learning-Based Approaches.

Induced pluripotent stem cells (iPSCs) can be differentiated into mesenchymal stem cells (iPSC-MSCs), retinal ganglion cells (iPSC-RGCs), and retinal ...

[Future perspectives in surgery from a German point of view].

In this article two aspects of the topic of future perspectives in surgery from a German point of view are discussed: firstly, topics of healthcare po...

Must-have Qualities of Clinical Research on Artificial Intelligence and Machine Learning.

In the field of computer science, known as artificial intelligence, algorithms imitate reasoning tasks that are typically performed by humans. The tec...

A Flexible Memristor Model With Electronic Resistive Switching Memory Behavior and Its Application in Spiking Neural Network.

Memristive technologies are attractive due to their non-volatility, high-density, low-power and compatibility with CMOS. For memristive devices, a mod...

Machine learning-based techniques to improve lung transplantation outcomes and complications: a systematic review.

BACKGROUND: Machine learning has been used to develop predictive models to support clinicians in making better and more reliable decisions. The high v...

Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction.

Recent advances in machine learning (ML) have revolutionized an extensive range of research and industry fields by successfully addressing intricate p...

Testing the delivery of human organ transportation with drones in the real world.

Last-mile transportation of human donor lungs in a densely populated urban environment has been made possible with drones.

Computed Tomography slice interpolation in the longitudinal direction based on deep learning techniques: To reduce slice thickness or slice increment without dose increase.

Large slice thickness or slice increment causes information insufficiency of Computed Tomography (CT) data in the longitudinal direction, which degrad...

A Brief Review of Artificial Intelligence in Genitourinary Oncological Imaging.

Genitourinary (GU) system is among the most commonly involved malignancy sites in the human body. Imaging plays a crucial role not only in diagnosis o...

Predicting multipotency of human adult stem cells derived from various donors through deep learning.

Adult stem cell-based therapeutic approaches have great potential in regenerative medicine because of their immunoregulatory properties and multidiffe...

Artificial Intelligence in Pediatric Nephrology-A Call for Action.

Artificial intelligence is playing an increasingly important role in many fields of clinical care to assist health care providers in patient managemen...

Risk Classification and Subphenotyping of Acute Kidney Injury: Concepts and Methodologies.

Acute kidney injury (AKI) is a complex syndrome with a paucity of therapeutic development. One aspect that could explain the lack of implementation sc...

Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor-Recipient Matching?

Liver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor-rec...

Multi-site cross-organ calibrated deep learning (MuSClD): Automated diagnosis of non-melanoma skin cancer.

Although deep learning (DL) has demonstrated impressive diagnostic performance for a variety of computational pathology tasks, this performance often ...

AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics.

Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can pred...

Identifying an Optimal Neuroinflammation Treatment Using a Nanoligomer Discovery Engine.

Acute activation of innate immune response in the brain, or neuroinflammation, protects this vital organ from a range of external pathogens and promot...