Transplantation

An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images.

Medical image registration is an essential technique to achieve spatial consistency geometric positions of different medical images obtained from single- or multi-sensor, such as computed tomography (CT), magnetic resonance (MR), and ultrasound (US) images. In this paper, an improved unsupervised learning-based framework is proposed for multi-organ registration on 3D abdominal CT images. First, th...

Machine Learning Applications in Solid Organ Transplantation and Related Complications.

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 promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how a...

functional cell phenotyping reveals microdomain networks in colorectal cancer recurrence.

Tumors are dynamic ecosystems comprising localized niches (microdomains), possessing distinct compositions and spatial configurations of cancer and no...

The impact of training sample size on deep learning-based organ auto-segmentation for head-and-neck patients.

To investigate the impact of training sample size on the performance of deep learning-based organ auto-segmentation for head-and-neck cancer patients,...

Robotic mesh-supported pectopexy for pelvic organ prolapse: expanding the options of pelvic floor repair.

Pelvic organ prolapse affects 30-50% of the female population. For definitive treatment surgery is unavoidable. Sacrocolpopexy has been the gold stand...

Uterine Transplantation with Robot-assisted Uterus Retrieval from Living Donor: First Case in Brazil.

OBJECTIVE: To present the first robot-assisted uterus retrieval from a living donor for uterine transplantation in Brazil.

Automated segmentation of biventricular contours in tissue phase mapping using deep learning.

Tissue phase mapping (TPM) is an MRI technique for quantification of regional biventricular myocardial velocities. Despite its potential, clinical use...

Simultaneous Segmentation of Fetal Hearts and Lungs for Medical Ultrasound Images via an Efficient Multi-scale Model Integrated With Attention Mechanism.

Large scale early scanning of fetuses via ultrasound imaging is widely used to alleviate the morbidity or mortality caused by congenital anomalies in ...

A machine-learning-based method to predict adverse events in patients with dilated cardiomyopathy and severely reduced ejection fractions.

OBJECTIVE: Patients with dilated cardiomyopathy (DCM) and severely reduced left ventricular ejection fractions (LVEFs) are at very high risks of exper...

Machine learning model to predict hypotension after starting continuous renal replacement therapy.

Hypotension after starting continuous renal replacement therapy (CRRT) is associated with worse outcomes compared with normotension, but it is difficu...

Using machine learning to improve survival prediction after heart transplantation.

BACKGROUND: This study investigates the use of modern machine learning (ML) techniques to improve prediction of survival after orthotopic heart transp...

Machine learning approach to measurement of criticism: The core dimension of expressed emotion.

Expressed emotion (EE), a measure of the family's emotional climate, is a fundamental measure in caregiving research. A core dimension of EE is the le...

Machine learning in medicine: It has arrived, let's embrace it.

Machine learning and artificial intelligence (AI) have arrived in medicine and the healthcare community is experiencing significant growth in their ad...

A machine learning approach identifies 5-ASA and ulcerative colitis as being linked with higher COVID-19 mortality in patients with IBD.

Inflammatory bowel diseases (IBD), namely Crohn's disease (CD) and ulcerative colitis (UC) are chronic inflammation within the gastrointestinal tract....

Extended Spatially Localized Perturbation GAN (eSLP-GAN) for Robust Adversarial Camouflage Patches.

Deep neural networks (DNNs), especially those used in computer vision, are highly vulnerable to adversarial attacks, such as adversarial perturbations...

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy.

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease pro...

Patterns of Metastatic Disease in Patients with Cancer Derived from Natural Language Processing of Structured CT Radiology Reports over a 10-year Period.

Background Patterns of metastasis in cancer are increasingly relevant to prognostication and treatment planning but have historically been documented ...

Editorial: The National COVID Cohort Collaborative Consortium Combines Population Data with Machine Learning to Evaluate and Predict Risk Factors for the Severity of COVID-19.

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19) commonly presents with pne...

Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.

Epithelial-mesenchymal transition (EMT) and its reverse process, mesenchymal-epithelial transition (MET), are believed to play key roles in facilitati...

Reversal of thoracic sympathectomy through robot-assisted microsurgical sympathetic trunk reconstruction with sural nerve graft and additional end-to-side coaptation of the intercostal nerves: A case report.

Thoracic sympathectomy to treat palmar hyperhidrosis (PH) has widely been performed. Many patients regret the surgery due to compensatory hyperhidrosi...