AIMC Topic: Ultrasonography

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Deep-Learning-Driven High Spatial Resolution Attenuation Imaging for Ultrasound Tomography (AI-UT).

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Ultrasonic attenuation can be used to characterize tissue properties of the human breast. Both quantitative ultrasound (QUS) and ultrasound tomography (USCT) can provide attenuation estimation. However, limitations have been identified for both appro...

Combining structural equation modeling analysis with machine learning for early malignancy detection in Bethesda Category III thyroid nodules.

Artificial intelligence in medicine
Atypia of Undetermined Significance (AUS), classified as Category III in the Bethesda Thyroid Cytopathology Reporting System, presents significant diagnostic challenges for clinicians. This study aims to develop a clinical decision support system tha...

Deep learning model for malignancy prediction of TI-RADS 4 thyroid nodules with high-risk characteristics using multimodal ultrasound: A multicentre study.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The automatic screening of thyroid nodules using computer-aided diagnosis holds great promise in reducing missed and misdiagnosed cases in clinical practice. However, most current research focuses on single-modal images and does not fully leverage th...

[Development of a machine learning-based diagnostic model for T-shaped uterus using transvaginal 3D ultrasound quantitative parameters].

Zhonghua yi xue za zhi
To develop a machine learning diagnostic model for T-shaped uterus based on quantitative parameters from 3D transvaginal ultrasound. A retrospective cross-sectional study was conducted, recruiting 304 patients who visited the hysteroscopy centre of...

LGF-Net: A multi-scale feature fusion network for thyroid nodule ultrasound image classification.

Journal of applied clinical medical physics
BACKGROUND: Thyroid cancer is one of the most common cancers in clinical practice, and accurate classification of thyroid nodule ultrasound images is crucial for computer-aided diagnosis. Models based on a convolutional neural network (CNN) or a tran...

A modular deep learning pipeline for enhanced plane-wave beamforming and B-mode image quality.

Medical physics
BACKGROUND: In ultrasound imaging using plane-wave (PW) techniques, image quality and contrast often suffer, especially when examining anechoic structures. Traditional beamforming methods like Delay-and-Sum or coherent PW compounding face limitations...

Enhancing HF-DL Model Validation for Liver Fibrosis Staging Through Sample Optimisation and Technical Integration.

Liver international : official journal of the International Association for the Study of the Liver
We read with great interest the article by Zhang et al. The study demonstrates that the deep learning model based on high-frequency ultrasound images significantly outperforms the low-frequency ultrasound model, FIB-4, APRI, and shear wave elastograp...

From Guidelines to Intelligence: How AI Refines Thyroid Nodule Biopsy Decisions.

Ultrasound in medicine & biology
OBJECTIVE: To evaluate the value of combining American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) with the Demetics ultrasound diagnostic system in reducing the rate of fine-needle aspiration (FNA) biopsies for thy...

Discriminating Clear Cell From Non-Clear Cell Renal Cell Carcinoma: A Machine Learning Approach Using Contrast-enhanced Ultrasound Radiomics.

Ultrasound in medicine & biology
OBJECTIVE: The aim of this investigation is to assess the clinical usefulness of a machine learning model using contrast-enhanced ultrasound (CEUS) radiomics in discriminating clear cell renal cell carcinoma (ccRCC) from non-ccRCC.

An orchestration learning framework for ultrasound imaging: Prompt-Guided Hyper-Perception and Attention-Matching Downstream Synchronization.

Medical image analysis
Ultrasound imaging is pivotal in clinical diagnostics due to its affordability, portability, safety, real-time capability, and non-invasive nature. It is widely utilized for examining various organs, such as the breast, thyroid, ovary, cardiac, and m...