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Development and Validation of a Deep Learning-Based Automatic Brain Segmentation and Classification Algorithm for Alzheimer Disease Using 3D T1-Weighted Volumetric Images.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Limited evidence has suggested that a deep learning automatic brain segmentation and classification method, based on T1-weighted brain MR images, can predict Alzheimer disease. Our aim was to develop and validate a deep learni...

Improving Accelerometry-Based Measurement of Functional Use of the Upper Extremity After Stroke: Machine Learning Versus Counts Threshold Method.

Neurorehabilitation and neural repair
BACKGROUND: Wrist-worn accelerometry provides objective monitoring of upper-extremity functional use, such as reaching tasks, but also detects nonfunctional movements, leading to ambiguity in monitoring results.

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography.

Scientific reports
Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development an...

Deep learning networks on chronic liver disease assessment with fine-tuning of shear wave elastography image sequences.

Physics in medicine and biology
Chronic liver disease (CLD) is currently one of the major causes of death worldwide. If not treated, it may lead to cirrhosis, hepatic carcinoma and death. Ultrasound (US) shear wave elastography (SWE) is a relatively new, popular, non-invasive techn...

Automated stroke lesion segmentation in non-contrast CT scans using dense multi-path contextual generative adversarial network.

Physics in medicine and biology
Stroke lesion volume is a key radiologic measurement in assessing prognosis of acute ischemic stroke (AIS) patients. The aim of this paper is to develop an automated segmentation method for accurately segmenting follow-up ischemic and hemorrhagic les...

Evaluation of machine learning-driven automated Kleihauer-Betke counting: A method comparison study.

International journal of laboratory hematology
INTRODUCTION: The Kleihauer-Betke (KB) test is the diagnostic standard for the quantification of fetomaternal hemorrhage (FMH). Manual analysis of KB slides suffers from inter-observer and inter-laboratory variability and low efficiency. Flow cytomet...

Radiomic Detection of EGFR Mutations in NSCLC.

Cancer research
Radiomics is defined as the use of automated or semi-automated post-processing and analysis of multiple features derived from imaging exams. Extracted features might generate models able to predict the molecular profile of solid tumors. The aim of th...

Reliability of robotic transcranial magnetic stimulation motor mapping.

Journal of neurophysiology
Robotic transcranial magnetic stimulation (TMS) is a noninvasive and safe tool that produces cortical motor maps using neuronavigational and neuroanatomical images. Motor maps are individualized representations of the primary motor cortex (M1) topogr...

Accuracy of machine learning-based prediction of medication adherence in clinical research.

Psychiatry research
Medication non-adherence represents a significant barrier to treatment efficacy. Remote, real-time measurement of medication dosing can facilitate dynamic prediction of risk for medication non-adherence, which in-turn allows for proactive clinical in...