AI Medical Compendium Topic:
Ultrasonography

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Deep learning for classification of thyroid nodules on ultrasound: validation on an independent dataset.

Clinical imaging
OBJECTIVES: The purpose is to apply a previously validated deep learning algorithm to a new thyroid nodule ultrasound image dataset and compare its performances with radiologists.

Demystifying evidential Dempster Shafer-based CNN architecture for fetal plane detection from 2D ultrasound images leveraging fuzzy-contrast enhancement and explainable AI.

Ultrasonics
Ultrasound imaging is a valuable tool for assessing the development of the fetal during pregnancy. However, interpreting ultrasound images manually can be time-consuming and subject to variability. Automated image categorization using machine learnin...

Segmentation of thyroid glands and nodules in ultrasound images using the improved U-Net architecture.

BMC medical imaging
BACKGROUND: Identifying thyroid nodules' boundaries is crucial for making an accurate clinical assessment. However, manual segmentation is time-consuming. This paper utilized U-Net and its improved methods to automatically segment thyroid nodules and...

Calcification Detection in Intravascular Ultrasound (IVUS) Images Using Transfer Learning Based MultiSVM model.

Ultrasonic imaging
Cardiovascular disease serves as the leading cause of death worldwide. Calcification detection is considered an important factor in cardiovascular diseases. Currently, medical practitioners visually inspect the presence of calcification using intrava...

[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...