Pregnancy

Image Quality Assessment of Fetal Brain MRI Using Multi-Instance Deep Learning Methods.

BACKGROUND: Due to random motion of fetuses and maternal respirations, image quality of fetal brain MRIs varies considerably. To address this issue, visual inspection of the images is performed during acquisition phase and after 3D-reconstruction, and the images are re-acquired if they are deemed to be of insufficient quality. However, this process is time-consuming and subjective. Multi-instance ...

Fetal MRI-based artificial intelligence in gestational age prediction--a practical solution to an unsolved problem?

Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning.

OBJECTIVES: To develop a convolutional neural network system to jointly segment and classify a hepatic lesion selected by user clicks in ultrasound im...

Tumor classification in automated breast ultrasound (ABUS) based on a modified extracting feature network.

People can get consistent Automated Breast Ultrasound (ABUS) images due to the imaging mechanism of scanning. Therefore, it has unique advantages in b...

Magnetically Actuated Drug Delivery Helical Microrobot with Magnetic Nanoparticle Retrieval Ability.

Therapeutic drug delivery microrobots capable of accurate targeting using an electromagnetic actuation (EMA) system are being developed. However, thes...

Application of deep learning to establish a diagnostic model of breast lesions using two-dimensional grayscale ultrasound imaging.

PURPOSE: There are currently few specific artificial intelligence (AI) studies for Breast Imaging Reporting and Data System (BI-RADS) category 4A lesi...

Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning.

The clinical application of breast ultrasound for the assessment of cancer risk and of deep learning for the classification of breast-ultrasound image...

Intelligent Diagnosis of Thyroid Ultrasound Imaging Using an Ensemble of Deep Learning Methods.

: At present, thyroid disorders have a great incidence in the worldwide population, so the development of alternative methods for improving the diagno...

Deep learning model for predicting gestational age after the first trimester using fetal MRI.

OBJECTIVES: To evaluate a deep learning model for predicting gestational age from fetal brain MRI acquired after the first trimester in comparison to ...

Prediction of Coronary Stent Underexpansion by Pre-Procedural Intravascular Ultrasound-Based Deep Learning.

OBJECTIVES: The aim of this study was to develop pre-procedural intravascular ultrasound (IVUS)-based models for predicting the occurrence of stent un...

Statistical and Machine Learning Models for Classification of Human Wear and Delivery Days in Accelerometry Data.

Accelerometers are increasingly being used in biomedical research, but the analysis of accelerometry data is often complicated by both the massive siz...

Artificial intelligence to deep learning: machine intelligence approach for drug discovery.

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-targe...

Artificial intelligence in ultrasound.

Ultrasound (US), a flexible green imaging modality, is expanding globally as a first-line imaging technique in various clinical fields following with th...

Predicting women with depressive symptoms postpartum with machine learning methods.

Postpartum depression (PPD) is a detrimental health condition that affects 12% of new mothers. Despite negative effects on mothers' and children's hea...

Magnetic tri-bead microrobot assisted near-infrared triggered combined photothermal and chemotherapy of cancer cells.

Magnetic micro/nanorobots attracted much attention in biomedical fields because of their precise movement, manipulation, and targeting abilities. Howe...

Time-aware deep neural networks for needle tip localization in 2D ultrasound.

PURPOSE: Accurate placement of the needle is critical in interventions like biopsies and regional anesthesia, during which incorrect needle insertion ...

Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid-structure interaction models and machine learning methods with patient follow-up data: a feasibility study.

BACKGROUND: Coronary plaque vulnerability prediction is difficult because plaque vulnerability is non-trivial to quantify, clinically available medica...

Computational methods to automate the initial interpretation of lower extremity arterial Doppler and duplex carotid ultrasound studies.

BACKGROUND: Lower extremity arterial Doppler (LEAD) and duplex carotid ultrasound studies are used for the initial evaluation of peripheral arterial d...

A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI.

Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding. Manual segmentation of the cor...

Reverberation Noise Suppression in Ultrasound Channel Signals Using a 3D Fully Convolutional Neural Network.

Diffuse reverberation is ultrasound image noise caused by multiple reflections of the transmitted pulse before returning to the transducer, which degr...