AIMC Topic: Point-of-Care Systems

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Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications.

IEEE transactions on biomedical circuits and systems
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can fa...

Usability Evaluation of User Requirement-Based Teleconsultation Robots: A Preliminary Report from South Korea.

Methods of information in medicine
BACKGROUND: Telepresence robots used to deliver a point-of-care (POC) consultation system that may provide value to enable effective decision making by healthcare providers at care sites.

Electrochemical SARS-CoV-2 Sensing at Point-of-Care and Artificial Intelligence for Intelligent COVID-19 Management.

ACS applied bio materials
To manage the COVID-19 pandemic, development of rapid, selective, sensitive diagnostic systems for early stage β-coronavirus severe acute respiratory syndrome (SARS-CoV-2) virus protein detection is emerging as a necessary response to generate the bi...

A Point-of-Care, Real-Time Artificial Intelligence System to Support Clinician Diagnosis of a Wide Range of Skin Diseases.

The Journal of investigative dermatology
Dermatological diagnosis remains challenging for nonspecialists because the morphologies of primary skin lesions widely vary from patient to patient. Although previous studies have used artificial intelligence (AI) to classify lesions as benign or ma...

Point-of-Care Ultrasound.

Current cardiology reports
PURPOSE OF THE REVIEW: Point-of-care ultrasound using small ultrasound devices has expanded beyond emergency and critical care medicine to many other subspecialties. Awareness of the strengths and limitations of the technology and knowledge of the ap...

Bolus pharmacokinetics: moving beyond mass-based dosing to guide drug administration.

Journal of pharmacokinetics and pharmacodynamics
Despite the common approach of bolus drug dosing using a patient's mass, a more tailored approach would be to use empirically derived pharmacokinetic models. Previously, this could only be possible though the use of computer simulation using programs...

Are Convolutional Neural Networks Trained on ImageNet Images Wearing Rose-Colored Glasses?: A Quantitative Comparison of ImageNet, Computed Tomographic, Magnetic Resonance, Chest X-Ray, and Point-of-Care Ultrasound Images for Quality.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: Deep learning for medical imaging analysis uses convolutional neural networks pretrained on ImageNet (Stanford Vision Lab, Stanford, CA). Little is known about how such color- and scene-rich standard training images compare quantitatively...

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound.

IEEE transactions on medical imaging
Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scan...

Creation and Testing of a Deep Learning Algorithm to Automatically Identify and Label Vessels, Nerves, Tendons, and Bones on Cross-sectional Point-of-Care Ultrasound Scans for Peripheral Intravenous Catheter Placement by Novices.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: We sought to create a deep learning (DL) algorithm to identify vessels, bones, nerves, and tendons on transverse upper extremity (UE) ultrasound (US) images to enable providers new to US-guided peripheral vascular access to identify anato...

Are All Deep Learning Architectures Alike for Point-of-Care Ultrasound?: Evidence From a Cardiac Image Classification Model Suggests Otherwise.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
OBJECTIVES: Little is known about optimal deep learning (DL) approaches for point-of-care ultrasound (POCUS) applications. We compared 6 popular DL architectures for POCUS cardiac image classification to determine whether an optimal DL architecture e...