Recent studies have combined multiple neuroimaging modalities to gain further understanding of the neurobiological substrates of aphasia. Following this line of work, the current study uses machine learning approaches to predict aphasia severity and ...
AIM: In this paper we proposed different architectures of convolutional neural network (CNN) to classify fatty liver disease in images using only pixels and diagnosis labels as input. We trained and validated our models using a dataset of 629 images ...
Accurate evaluation of tumor response to preoperative chemotherapy is crucial for assigning appropriate patients with colorectal liver metastases (CRLM) to surgery or conservative therapy. However, there is no well-recognized method for predicting pa...
OBJECTIVE: We aim to provide a thorough description of the efforts and outcomes of the Co-Pilot Project in Ukraine, which facilitates neurosurgical collaboration between American and Ukrainian physicians.
OBJECTIVE: To evaluate the efficacy and safety of robot-assisted percutaneous endoscopic lumbar discectomy (rPELD) using a specially designed orthopaedic robot with an intraoperative computed tomography-equipped suite for treatment of symptomatic lum...
OBJECTIVES: To assess the effect of a home-based over-ground robotic-assisted gait training program using the AlterG Bionic Leg orthosis on clinical functional outcomes in people with chronic stroke.
European journal of cancer (Oxford, England : 1990)
Dec 26, 2020
BACKGROUND: CDK4/6 inhibitors plus endocrine therapies are the current standard of care in the first-line treatment of HR+/HER2-negative metastatic breast cancer, but there are no well-established clinical or molecular predictive factors for patient ...
BACKGROUND: infection (CDI) is now the most common cause of healthcare-associated infections, with increasing prevalence, severity, and mortality of nosocomial and community-acquired CDI which makes up approximately one third of all CDI. There are a...
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