Everything you need to know about ultrasound for diagnosis of gallbladder diseases

Early prediction of gallstone disease with a machine learning-based method from bioimpedance and laboratory data

Gallstone disease (GD) is a common gastrointestinal disease. Although traditional diagnostic techniques, such as ultrasonography, CT, and MRI, detect gallstones, they have some limitations, including high cost and potential inaccuracies in certain populations. This study proposes a machine learning-based prediction model for gallstone disease using bioimpedance and laboratory data. A dataset of 319 samples, comprising161 gallstone patients and 158 healthy controls, was curated. The dataset comprised 38 attributes of the participants, including age, weight, height, blood test results, and bioimpedance data, and it contributed to the literature on gallstones as a new dataset. State-of-the-art machine learning techniques were performed on the dataset to detect gallstones. The experimental results showed that vitamin D, C-reactive protein (CRP) level, total body water, and lean mass are crucial features, and the gradient boosting technique achieved the highest accuracy (85.42%) in predicting gallstones. The proposed technique offers a viable alternative to conventional imaging techniques for early prediction of gallstone disease.

Recent Advances in Endoscopic Ultrasound for Gallbladder Disease Diagnosis

Gallbladder (GB) disease is classified into two broad categories: GB wall-thickening and protuberant lesions, which include various lesions, such as adenomyomatosis, cholecystitis, GB polyps, and GB carcinoma. This review summarizes recent advances in the differential diagnosis of GB lesions, focusing primarily on endoscopic ultrasound (EUS) and related technologies. Fundamental B-mode EUS and contrast-enhanced harmonic EUS (CH-EUS) have been reported to be useful for the diagnosis of GB diseases because they can evaluate the thickening of the GB wall and protuberant lesions in detail. We also outline the current status of EUS-guided fine-needle aspiration (EUS-FNA) for GB lesions, as there have been scattered reports on EUS-FNA in recent years. Furthermore, artificial intelligence (AI) technologies, ranging from machine learning to deep learning, have become popular in healthcare for disease diagnosis, drug discovery, drug development, and patient risk identification. In this review, we outline the current status of AI in the diagnosis of GB.

Usefulness of contrast-enhanced ultrasonography for biliary tract disease

Conventional ultrasonography (US) for biliary tract disease shows high time and spatial resolution. In addition, it is simple and minimally invasive, and is selected as a first-choice examination procedure for biliary tract disease. Currently, contrast-enhanced US (CEUS), which facilitates the more accurate assessment of lesion blood flow in comparison with color and power Doppler US, is performed using a second-generation ultrasonic contrast agent. Such agents are stable and provide a timeline for CEUS diagnosis. Gallbladder lesions are classified into three types: gallbladder biliary lesion (GBL), gallbladder polypoid lesion (GPL), and gallbladder wall thickening (GWT). Bile duct lesions can also be classified into three types: bile duct biliary lesion (BBL), bile duct polypoid lesion (BDPL), and bile duct wall thickening (BDWT). CEUS facilitates the differentiation of GBL/BBL from tumorous lesions based on the presence or absence of blood vessels. In the case of GPL, it is important to identify a vascular stalk attached to the lesion. In the case of GWT, the presence or absence of a non-contrast-enhanced area, the Rokitansky-Aschoff sinus, and continuity of a contrast-enhanced gallbladder wall layer are important for differentiation from gallbladder cancer. In the case of BDWT, it is useful to evaluate the contour of the contrast-enhanced medial layer of the bile duct wall for differentiating IgG4-related sclerosing cholangitis from primary sclerosing cholangitis. CEUS for ampullary carcinoma accurately reflects histopathological findings of the lesion. Evaluating blood flow in the lesion, continuity of the gallbladder wall, and contour of the bile duct wall via CEUS provides useful information for the diagnosis of biliary tract disease.

Detection of Gallbladder Disease Types Using Deep Learning: An Informative Medical Method

Nowadays, despite all the conducted research and the provided efforts in advancing the healthcare sector, there is a strong need to rapidly and efficiently diagnose various diseases. The complexity of some disease mechanisms on one side and the dramatic life-saving potential on the other side raise big challenges for the development of tools for the early detection and diagnosis of diseases. Deep learning (DL), an area of artificial intelligence (AI), can be an informative medical tomography method that can aid in the early diagnosis of gallbladder (GB) disease based on ultrasound images (UI). Many researchers considered the classification of only one disease of the GB. In this work, we successfully managed to apply a deep neural network (DNN)-based classification model to a rich built database in order to detect nine diseases at once and to determine the type of disease using UI. In the first step, we built a balanced database composed of 10,692 UI of the GB organ from 1782 patients. These images were carefully collected from three hospitals over roughly three years and then classified by professionals. In the second step, we preprocessed and enhanced the dataset images in order to achieve the segmentation step. Finally, we applied and then compared four DNN models to analyze and classify these images in order to detect nine GB disease types. All the models produced good results in detecting GB diseases; the best was the MobileNet model, with an accuracy of 98.35%.

The Role of Endoscopic Ultrasound in the Diagnosis of Gallbladder Lesions

Gallbladder (GB) diseases represent various lesions including gallstones, cholesterol polyps, adenomyomatosis, and GB carcinoma. This review aims to summarize the role of endoscopic ultrasound (EUS) in the diagnosis of GB lesions. EUS provides high-resolution images that can improve the diagnosis of GB polypoid lesions, GB wall thickness, and GB carcinoma staging. Contrast-enhancing agents may be useful for the differential diagnosis of GB lesions, but the evidence of their effectiveness is still limited. Thus, further studies are required in this area to establish its usefulness. EUS combined with fine-needle aspiration has played an increasing role in providing a histological diagnosis of GB tumors in addition to GB wall thickness.