AIMC Topic: Ultrasonography

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Deep learning and ultrasound feature fusion model predicts the malignancy of complex cystic and solid breast nodules with color Doppler images.

Scientific reports
This study aimed to evaluate the performance of traditional-deep learning combination model based on Doppler ultrasound for diagnosing malignant complex cystic and solid breast nodules. A conventional statistical prediction model based on the ultraso...

Simulator, machine learning, and artificial intelligence: Time has come to assist prenatal ultrasound diagnosis.

Journal of clinical ultrasound : JCU
In this Commentary authors investigated and extended the role of simulator in assisting obstetric sonographers in training program. The interconnection of different digitalized technologies such as digital data, artificial neuronal and convolutional ...

Development of a Deep Learning-Based System for Optic Nerve Characterization in Transorbital Ultrasound Images on a Multicenter Data Set.

Ultrasound in medicine & biology
OBJECTIVE: Characterization of the optic nerve through measurement of optic nerve diameter (OND) and optic nerve sheath diameter (ONSD) using transorbital sonography (TOS) has proven to be a useful tool for the evaluation of intracranial pressure (IC...

Accelerated Measurement of Carotid Plaque Volume Using Artificial Intelligence Enhanced 3D Ultrasound.

Annals of vascular surgery
BACKGROUND: Carotid plaque volume (CPV) can be measured by 3D ultrasound and may be a better predictor of stroke than stenosis, but analysis time limits clinical utility. This study tested the accuracy, reproducibility, and time saved of using an art...

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

European radiology experimental
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 an...

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

Journal of digital imaging
Abdominal ultrasonography has become an integral component of the evaluation of trauma patients. Internal hemorrhage can be rapidly diagnosed by finding free fluid with point-of-care ultrasound (POCUS) and expedite decisions to perform lifesaving int...

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 ...