Pregnancy

The role of artificial intelligence in hypertensive disorders of pregnancy: towards personalized healthcare.

INTRODUCTION: Guidelines advise ongoing follow-up of patients after hypertensive disorders of pregnancy (HDP) to assess cardiovascular risk and manage future patient-specific pregnancy conditions. However, there are limited tools available to monitor patients, with those available tending to be simple risk assessments that lack personalization. A promising approach could be the emerging artificial...

Bridging the simulation-to-real gap for AI-based needle and target detection in robot-assisted ultrasound-guided interventions.

BACKGROUND: Artificial intelligence (AI)-powered, robot-assisted, and ultrasound (US)-guided interventional radiology has the potential to increase the efficacy and cost-efficiency of interventional procedures while improving postsurgical outcomes and reducing the burden for medical personnel.

Review on deep learning fetal brain segmentation from Magnetic Resonance images.

Brain segmentation is often the first and most critical step in quantitative analysis of the brain for many clinical applications, including fetal ima...

Research on the impact of industrial robot application on the status of countries in manufacturing global value chains.

The fast growth in the installation of industrial robots has had a major impact on the comparative advantage of nations and the division of labor in g...

Using Deep Learning to Detect the Presence and Location of Hemoperitoneum on the Focused Assessment with Sonography in Trauma (FAST) Examination in Adults.

Abdominal ultrasonography has become an integral component of the evaluation of trauma patients. Internal hemorrhage can be rapidly diagnosed by findi...

Biodegradable Magnetic Hydrogel Robot with Multimodal Locomotion for Targeted Cargo Delivery.

Recent strides in the development of untethered miniature robots have shown the advantages of diverse actuation methods, flexible maneuverability, and...

Surgical Sealant with Integrated Shape-Morphing Dual Modality Ultrasound and Computed Tomography Sensors for Gastric Leak Detection.

Postoperative anastomotic leaks are the most feared complications after gastric surgery. For diagnostics clinicians mostly rely on clinical symptoms s...

Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer.

RATIONALE AND OBJECTIVES: To carry out radiomics analysis/deep convolutional neural network (CNN) based on B-mode ultrasound (BUS) and shear wave elas...

Creating an ignorance-base: Exploring known unknowns in the scientific literature.

BACKGROUND: Scientific discovery progresses by exploring new and uncharted territory. More specifically, it advances by a process of transforming unkn...

Two- Versus 8-Zone Lung Ultrasound in Heart Failure: Analysis of a Large Data Set Using a Deep Learning Algorithm.

OBJECTIVE: Scanning protocols for lung ultrasound often include 8 or more lung zones, which may limit real-world clinical use. We sought to compare a ...

Issue of Data Imbalance on Low Birthweight Baby Outcomes Prediction and Associated Risk Factors Identification: Establishment of Benchmarking Key Machine Learning Models With Data Rebalancing Strategies.

BACKGROUND: Low birthweight (LBW) is a leading cause of neonatal mortality in the United States and a major causative factor of adverse health effects...

Ultrasound guidance in navigated liver surgery: toward deep-learning enhanced compensation of deformation and organ motion.

PURPOSE: Accuracy of image-guided liver surgery is challenged by deformation of the liver during the procedure. This study aims at improving navigatio...

Deep learning algorithm for predicting subacromial motion trajectory: Dynamic shoulder ultrasound analysis.

Subacromial motion metrics can be extracted from dynamic shoulder ultrasonography, which is useful for identifying abnormal motion patterns in painful...

Differentiation between Phyllodes Tumors and Fibroadenomas through Breast Ultrasound: Deep-Learning Model Outperforms Ultrasound Physicians.

The preoperative differentiation of breast phyllodes tumors (PTs) from fibroadenomas (FAs) plays a critical role in identifying an appropriate surgica...

Calibrating Data Mismatches in Deep Learning-Based Quantitative Ultrasound Using Setting Transfer Functions.

Deep learning (DL) can fail when there are data mismatches between training and testing data distributions. Due to its operator-dependent nature, acqu...

Prenatal Diagnosis of Placenta Accreta Spectrum Disorders: Deep Learning Radiomics of Pelvic MRI.

BACKGROUND: Diagnostic performance of placenta accreta spectrum (PAS) by prenatal MRI is unsatisfactory. Deep learning radiomics (DLR) has the potenti...

Deep Learning-Based Computer-Aided Diagnosis for Breast Lesion Classification on Ultrasound: A Prospective Multicenter Study of Radiologists Without Breast Ultrasound Expertise.

Computer-aided diagnosis (CAD) systems for breast ultrasound interpretation have been primarily evaluated at tertiary and/or urban medical centers by...

Neural network combining with clinical ultrasonography: A new approach for classification of salivary gland tumors.

OBJECTIVE: Little information is available about deep learning methods used in ultrasound images of salivary gland tumors. We aimed to compare the acc...

A comparison of point-tracking algorithms in ultrasound videos from the upper limb.

Tracking points in ultrasound (US) videos can be especially useful to characterize tissues in motion. Tracking algorithms that analyze successive vide...

Deep learning-based markerless lung tumor tracking in stereotactic radiotherapy using Siamese networks.

BACKGROUND: Radiotherapy (RT) is involved in about 50% of all cancer patients, making it a very important treatment modality. The most common type of ...