AJNR. American journal of neuroradiology
Dec 31, 2020
BACKGROUND AND PURPOSE: Artificial intelligence algorithms have the potential to become an important diagnostic tool to optimize stroke workflow. Viz LVO is a medical product leveraging a convolutional neural network designed to detect large-vessel o...
BACKGROUND: The diagnosis of gastric cancer mainly relies on endoscopy, which is invasive and costly. The aim of this study is to develop a predictive model for the diagnosis of gastric cancer based on noninvasive characteristics.
BACKGROUND: A priority for health services is to reduce self-harm in young people. Predicting self-harm is challenging due to their rarity and complexity, however this does not preclude the utility of prediction models to improve decision-making rega...
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 ...
PURPOSE: The role of tumor-infiltrating lymphocytes (TILs) in predicting lymph node metastasis (LNM) in patients with T1 colorectal cancer (CRC) remains unclear. Furthermore, clinical utility of a machine learning-based approach has not been widely s...
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/HYPOTHESIS: To create a new strategy for monitoring pediatric otitis media (OM), we developed a brief, reliable, and objective method for automated classification using convolutional neural networks (CNNs) with images from otoscope.
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