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Ultrasonography

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Deep Learning for Ultrasonographic Assessment of Temporomandibular Joint Morphology.

Tomography (Ann Arbor, Mich.)
BACKGROUND: Temporomandibular joint (TMJ) disorders are a significant cause of orofacial pain. Artificial intelligence (AI) has been successfully applied to other imaging modalities but remains underexplored in ultrasonographic evaluations of TMJ.

Towards robust multimodal ultrasound classification for liver tumor diagnosis: A generative approach to modality missingness.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In medical image analysis, combining multiple imaging modalities enhances diagnostic accuracy by providing complementary information. However, missing modalities are common in clinical settings, limiting the effectiveness of...

Guided ultrasound acquisition for nonrigid image registration using reinforcement learning.

Medical image analysis
We propose a guided registration method for spatially aligning a fixed preoperative image and untracked ultrasound image slices. We exploit the unique interactive and spatially heterogeneous nature of this application to develop a registration algori...

Harnessing the oloid shape in magnetically driven robots to enable high-resolution ultrasound imaging.

Science robotics
Magnetic fields enable remote manipulation of objects and are ideal for medical applications because they pass through human tissue harmlessly. This capability is promising for surgical robots, allowing navigation deeper into the human anatomy and ac...

Prediction of Seronegative Hashimoto's thyroiditis using machine learning models based on ultrasound radiomics: a multicenter study.

BMC immunology
BACKGROUND: Seronegative Hashimoto's thyroiditis is often underdiagnosed due to the lack of antibody markers. Combining ultrasound radiomics with machine learning offers potential for early detection in patients with normal thyroid function.

A domain adaptation model for carotid ultrasound: Image harmonization, noise reduction, and impact on cardiovascular risk markers.

Computers in biology and medicine
Deep learning has been used extensively for medical image analysis applications, assuming the training and test data adhere to the same probability distributions. However, a common challenge arises when dealing with medical images generated by differ...

Development and validation of a framework for registration of whole-mount radical prostatectomy histopathology with three-dimensional transrectal ultrasound.

BMC urology
PURPOSE: Artificial intelligence (AI) has the potential to improve diagnostic imaging on multiple levels. To develop and validate these AI-assisted modalities a reliable dataset is of utmost importance. The registration of imaging to pathology is an ...

Synthetic Lung Ultrasound Data Generation Using Autoencoder With Generative Adversarial Network.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Class imbalance is a significant challenge in medical image analysis, particularly in lung ultrasound (LUS), where severe patterns are often underrepresented. Traditional oversampling techniques, which simply duplicate original data, have limited eff...

Unsupervised Test-Time Adaptation for Hepatic Steatosis Grading Using Ultrasound B-Mode Images.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasound (US) is considered a key modality for the clinical assessment of hepatic steatosis (i.e., fatty liver) due to its noninvasiveness and availability. Deep learning methods have attracted considerable interest in this field, as they are capab...

BentRay-NeRF: Bent-Ray Neural Radiance Fields for Robust Speed-of-Sound Imaging in Ultrasound Computed Tomography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasound computed tomography (USCT) is a promising technique for breast cancer detection because of its quantitative imaging capability of the speed of sound (SOS) of soft tissues and the fact that malignant breast lesions often have a higher SOS c...