AI Medical Compendium Topic:
Ultrasonography

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

Academic radiology
RATIONALE AND OBJECTIVES: To carry out radiomics analysis/deep convolutional neural network (CNN) based on B-mode ultrasound (BUS) and shear wave elastography (SWE) to predict response to neoadjuvant chemotherapy (NAC) in breast cancer patients.

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

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
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 2-zone, anterior-superior thoracic ultrasound protocol for B-line artifact detection with an 8-zone ...

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

Ultrasonics
Subacromial motion metrics can be extracted from dynamic shoulder ultrasonography, which is useful for identifying abnormal motion patterns in painful shoulders. However, frame-by-frame manual labeling of anatomical landmarks in ultrasound images is ...

On the importance of patient acceptance for medical robotic imaging.

International journal of computer assisted radiology and surgery
PURPOSE: Mutual acceptance is required for any human-to-human interaction. Therefore, one would assume that this also holds for robot-patient interactions. However, the medical robotic imaging field lacks research in the area of acceptance. This work...

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

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Deep learning (DL) can fail when there are data mismatches between training and testing data distributions. Due to its operator-dependent nature, acquisition-related data mismatches, caused by different scanner settings, can occur in ultrasound imagi...

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

Head & neck
OBJECTIVE: Little information is available about deep learning methods used in ultrasound images of salivary gland tumors. We aimed to compare the accuracy of the ultrasound-trained model to computed tomography or magnetic resonance imaging trained m...

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

Biomedical engineering online
Tracking points in ultrasound (US) videos can be especially useful to characterize tissues in motion. Tracking algorithms that analyze successive video frames, such as variations of Optical Flow and Lucas-Kanade (LK), exploit frame-to-frame temporal ...

Deep learning-based classification of breast lesions using dynamic ultrasound video.

European journal of radiology
PURPOSE: We intended to develop a deep-learning-based classification model based on breast ultrasound dynamic video, then evaluate its diagnostic performance in comparison with the classic model based on ultrasound static image and that of different ...

Precise angle estimation of capsule robot in ultrasound using heatmap guided two-stage network.

Computer methods and programs in biomedicine
PURPOSE: A capsule robot can be controlled inside gastrointestinal (GI) tract by an external permanent magnet outside of human body for finishing non-invasive diagnosis and treatment. Locomotion control of capsule robot relies on the precise angle fe...