AIMC Topic: Humans

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Facilitating Trust Calibration in Artificial Intelligence-Driven Diagnostic Decision Support Systems for Determining Physicians' Diagnostic Accuracy: Quasi-Experimental Study.

JMIR formative research
BACKGROUND: Diagnostic errors are significant problems in medical care. Despite the usefulness of artificial intelligence (AI)-based diagnostic decision support systems, the overreliance of physicians on AI-generated diagnoses may lead to diagnostic ...

Assessing Visitor Expectations of AI Nursing Robots in Hospital Settings: Cross-Sectional Study Using the Kano Model.

JMIR nursing
BACKGROUND: Globally, the rates at which the aging population and the prevalence of chronic diseases are increasing are substantial. With declining birth rates and a growing percentage of older individuals, the demand for nursing staff is steadily ri...

Distinguishing among standing postures with machine learning-based classification algorithms.

Experimental brain research
The purpose of our study was to evaluate the accuracy with which classification algorithms could distinguish among standing postures based on center-of-pressure (CoP) trajectories. We performed a secondary analysis of published data from three studie...

Automated Classification of Coronary Plaque on Intravascular Ultrasound by Deep Classifier Cascades.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Intravascular ultrasound (IVUS) is the gold standard modality for in vivo visualization of coronary arteries and atherosclerotic plaques. Classification of coronary plaques helps to characterize heterogeneous components and evaluate the risk of plaqu...

Active Inference and Deep Generative Modeling for Cognitive Ultrasound.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasound (US) has the unique potential to offer access to medical imaging to anyone, everywhere. Devices have become ultraportable and cost-effective, akin to the stethoscope. Nevertheless, and despite many advances, US image quality and diagnostic...

Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasound computed tomography (USCT) quantifies acoustic tissue properties such as the speed-of-sound (SOS). Although full-waveform inversion (FWI) is an effective method for accurate SOS reconstruction, it can be computationally challenging for lar...

Boosting Cardiac Color Doppler Frame Rates With Deep Learning.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Color Doppler echocardiography enables visualization of blood flow within the heart. However, the limited frame rate impedes the quantitative assessment of blood velocity throughout the cardiac cycle, thereby compromising a comprehensive analysis of ...

BEAS-Net: A Shape-Prior-Based Deep Convolutional Neural Network for Robust Left Ventricular Segmentation in 2-D Echocardiography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Left ventricle (LV) segmentation of 2-D echocardiography images is an essential step in the analysis of cardiac morphology and function and-more generally-diagnosis of cardiovascular diseases (CVD). Several deep learning (DL) algorithms have recently...

Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging. In this study, we propose novel alternatives to the traditional iVFM optimization scheme using physics-informed neural networks (PINNs) and a ...

Hardware-Independent Deep Signal Processing: A Feasibility Study in Echocardiography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Deep learning (DL) models have emerged as alternative methods to conventional ultrasound (US) signal processing, offering the potential to mimic signal processing chains, reduce inference time, and enable the portability of processing chains across h...