AIMC Topic: Ultrasonography

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[Update: Small bowel diseases in computed tomography and magnetic resonance imaging].

Radiologie (Heidelberg, Germany)
CLINICAL/METHODICAL ISSUE: Radiological procedures play a crucial role in the diagnosis of small bowel disease. Due to a broad and quite nonspecific spectrum of symptoms, clinical evaluation is often difficult, and endoscopic procedures require signi...

A study on the optimal condition of ground truth area for liver tumor detection in ultrasound images using deep learning.

Journal of medical ultrasonics (2001)
PURPOSE: In recent years, efforts to apply artificial intelligence (AI) to the medical field have been growing. In general, a vast amount of high-quality training data is necessary to make great AI. For tumor detection AI, annotation quality is impor...

Applications of Artificial Intelligence in the Automatic Diagnosis of Focal Liver Lesions: A Systematic Review.

Journal of gastrointestinal and liver diseases : JGLD
BACKGROUND AND AIMS: Focal liver lesions (FLLs) are defined as abnormal solid or liquid masses differentiated from normal liver, frequently being clinically asymptomatic. The aim of this systematic review is to provide a comprehensive overview of cur...

Aggregated micropatch-based deep learning neural network for ultrasonic diagnosis of cirrhosis.

Artificial intelligence in medicine
Despite the advancements in the diagnosis of early-stage cirrhosis, the accuracy in the diagnosis using ultrasound is still challenging owing to the presence of various image artifacts, which results in poor visual quality of the textural and lower-f...

BiTNet: Hybrid deep convolutional model for ultrasound image analysis of human biliary tract and its applications.

Artificial intelligence in medicine
Certain life-threatening abnormalities, such as cholangiocarcinoma, in the human biliary tract are curable if detected at an early stage, and ultrasonography has been proven to be an effective tool for identifying them. However, the diagnosis often r...

Benchmark methodological approach for the application of artificial intelligence to lung ultrasound data from COVID-19 patients: From frame to prognostic-level.

Ultrasonics
Automated ultrasound imaging assessment of the effect of CoronaVirus disease 2019 (COVID-19) on lungs has been investigated in various studies using artificial intelligence-based (AI) methods. However, an extensive analysis of state-of-the-art Convol...

Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.

European radiology
OBJECTIVES: To investigate the predictive performance of the deep learning radiomics (DLR) model integrating pretreatment ultrasound imaging features and clinical characteristics for evaluating therapeutic response after neoadjuvant chemotherapy (NAC...

Breast Tumor Classification using Short-ResNet with Pixel-based Tumor Probability Map in Ultrasound Images.

Ultrasonic imaging
Breast cancer is the most common form of cancer and is still the second leading cause of death for women in the world. Early detection and treatment of breast cancer can reduce mortality rates. Breast ultrasound is always used to detect and diagnose ...

Parallelized ultrasound homodyned-K imaging based on a generalized artificial neural network estimator.

Ultrasonics
The homodyned-K (HK) distribution model is a generalized backscatter envelope statistical model for ultrasound tissue characterization, whose parameters are of physical meaning. To estimate the HK parameters is an inverse problem, and is quite compli...

AI-ENABLED ASSESSMENT OF CARDIAC FUNCTION AND VIDEO QUALITY IN EMERGENCY DEPARTMENT POINT-OF-CARE ECHOCARDIOGRAMS.

The Journal of emergency medicine
BACKGROUND: The adoption of point-of-care ultrasound (POCUS) has greatly improved the ability to rapidly evaluate unstable emergency department (ED) patients at the bedside. One major use of POCUS is to obtain echocardiograms to assess cardiac functi...