Combining CEUS and CT/MRI LI-RADS major imaging features: diagnostic accuracy for classification of indeterminate liver observations in patients at risk for HCC.

Purpose: To determine the diagnostic accuracy of combining CEUS and CT/MRI LI-RADS major imaging features for the improved categorization of liver observations indeterminate on both CT/MRI and CEUS.

Materials And Methods: A retrospective analysis using a database from a prospective study conducted at 11 centers in North America and Europe from 2018 to 2022 included a total of 109 participants at risk for HCC who had liver observations with indeterminate characterization (LR3, LR-4, and LR-M) on both CEUS and CT/MRI. The individual CEUS and CT/MRI LI-RADS major features were extracted from the original study and analyzed in various combinations.

Characterizing Sentinel Lymph Node Status in Breast Cancer Patients Using a Deep-Learning Model Compared With Radiologists' Analysis of Grayscale Ultrasound and Lymphosonography.

The objective of the study was to use a deep learning model to differentiate between benign and malignant sentinel lymph nodes (SLNs) in patients with breast cancer compared to radiologists' assessments.Seventy-nine women with breast cancer were enrolled and underwent lymphosonography and contrast-enhanced ultrasound (CEUS) examination after subcutaneous injection of ultrasound contrast agent around their tumor to identify SLNs. Google AutoML was used to develop image classification model.

Evaluation of the Contrast-Enhanced Ultrasound Nonradiation Treatment Response Assessment LI-RADS v2024 Using Data From a Multi-Center Transarterial Chemoembolization Study.

Rationale And Objective: Hepatocellular carcinoma (HCC) locoregional treatment response is commonly evaluated using the Modified Response Evaluation Criteria in Solid Tumors and the American College of Radiology (ACR) Liver Reporting and Data System (LI-RADS) Treatment Response Assessment (TRA) for MRI/CT. This study aims to evaluate the diagnostic performance of the new ACR contrast-enhanced ultrasound (CEUS) Nonradiation TRA LI-RADS v2024 in HCC treated with transarterial chemoembolization (TACE).

Materials And Methods: This retrospective observational study included 87 patients treated with TACE from a previously reported cohort.

A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey.

In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications approved by the FDA being in imaging and radiology in 2023. The surge in AI model development to tackle clinical challenges underscores the necessity for preparing high-quality medical imaging data. Proper data preparation is crucial as it fosters the creation of standardized and reproducible AI models while minimizing biases.

Reproducibility of Deep Learning Algorithms Developed for Medical Imaging Analysis: A Systematic Review.

Since 2000, there have been more than 8000 publications on radiology artificial intelligence (AI). AI breakthroughs allow complex tasks to be automated and even performed beyond human capabilities. However, the lack of details on the methods and algorithm code undercuts its scientific value.

Comparison of Magnetic Resonance-Based Elastography and Ultrasound Shear Wave Elastography in Patients With Suspicion of Nonalcoholic Fatty Liver Disease.

This study investigated the correlation between magnetic resonance elastography (MRE) and shear wave ultrasound elastography (SWE) in patients with clinically diagnosed or suspected nonalcoholic fatty liver disease (NAFLD). Subjects with or at risk of NAFLD identified by magnetic resonance imaging (MRI) proton density fat fraction (PDFF) were prospectively enrolled. For each patient, 6 valid 2-dimensional SWE measurements were acquired using a Logiq E10 scanner (GE HealthCare, Waukesha, WI).

Ultrasound-Based Machine Learning Approach for Detection of Nonalcoholic Fatty Liver Disease.

Objectives: Current diagnosis of nonalcoholic fatty liver disease (NAFLD) relies on biopsy or MR-based fat quantification. This prospective study explored the use of ultrasound with artificial intelligence for the detection of NAFLD.

Methods: One hundred and twenty subjects with clinical suspicion of NAFLD and 10 healthy volunteers consented to participate in this institutional review board-approved study.

Comparison of Gray-scale Inversion to Improve Detection of Pulmonary Nodules on Chest X-rays Between Radiologists and a Deep Convolutional Neural Network.

Detection of pulmonary nodules on chest x-rays is an important task for radiologists. Previous studies have shown improved detection rates using gray-scale inversion. The purpose of our study was to compare the efficacy of gray-scale inversion in improving the detection of pulmonary nodules on chest x-rays for radiologists and machine learning models (ML).

Tumoral oxygenation and biodistribution of Lonidamine oxygen microbubbles following localized ultrasound-triggered delivery.

Prior work has shown that microbubble-assisted delivery of oxygen improves tumor oxygenation and radiosensitivity, albeit over a limited duration. Lonidamine (LND) has been investigated because of its ability to stimulate glycolysis, lactate production, inhibit mitochondrial respiration, and inhibit oxygen consumption rates in tumors but suffers from poor bioavailability. The goal of this work was to characterize LND-loaded oxygen microbubbles and assess their ability to oxygenate a human head and neck squamous cell carcinoma (HNSCC) tumor model, while also assessing LND biodistribution.

Assessment of Axillary Lymph Nodes for Metastasis on Ultrasound Using Artificial Intelligence.

The purpose of this study was to evaluate an artificial intelligence (AI) system for the classification of axillary lymph nodes on ultrasound compared to radiologists. Ultrasound images of 317 axillary lymph nodes from patients referred for ultrasound guided fine needle aspiration or core needle biopsy and corresponding pathology findings were collected. Lymph nodes were classified into benign and malignant groups with histopathological result serving as the reference.

Incorporation of a Machine Learning Algorithm With Object Detection Within the Thyroid Imaging Reporting and Data System Improves the Diagnosis of Genetic Risk.

Background: The role of next generation sequencing (NGS) for identifying high risk mutations in thyroid nodules following fine needle aspiration (FNA) biopsy continues to grow. However, ultrasound diagnosis even using the American College of Radiology's Thyroid Imaging Reporting and Data System (TI-RADS) has limited ability to stratify genetic risk. The purpose of this study was to incorporate an artificial intelligence (AI) algorithm of thyroid ultrasound with object detection within the TI-RADS scoring system to improve prediction of genetic risk in these nodules.

Aloe Vera-Derived Gel-Blended PHBV Nanofibrous Scaffold for Bone Tissue Engineering.

Today, composite scaffolds fabricated by natural and synthetic polymers have attracted a lot of attention among researchers in the field of tissue engineering, and given their combined properties that can play a very useful role in repairing damaged tissues. In the current study, aloe vera-derived gel-blended poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) nanofibrous scaffold was fabricated by electrospinning, and then, PHBV and PHBV gel fabricated scaffolds characterized by scanning electron microscope, protein adsorption, cell attachment, tensile and cell's viability tests. After that, osteogenic supportive property of the scaffolds was studied by culturing of human-induced pluripotent stem cells on the scaffolds under osteogenic medium and evaluating of the common bone-related markers.

Micro-RNA-incorporated electrospun nanofibers improve osteogenic differentiation of human-induced pluripotent stem cells.

Smart scaffolds have a great role in the damaged tissue reconstruction. The aim of this study was developing a scaffold that in addition to its fiber's topography has also content of micro-RNAs (miRNAs), which play a regulatory role during osteogenesis. In this study, we inserted two important miRNAs, including miR-22 and miR-126 in the electrospun polycaprolactone (PCL) nanofibers and after scaffold characterization, osteoinductivity of the fabricated nanofibers was investigated by evaluating of the osteogenic differentiation potential of induced pluripotent stem cells (iPSCs) when grown on miRNAs-incorporated PCL nanofibers (PCL-miR) and empty PCL.

Transgluteal ultrasonography in spica cast in postreduction assessment of developmental dysplasia of the hip.

Purpose: The position of the femoral head in spica cast after the reduction of developmental dysplasia of the hip (DDH) should be examined and followed up closely and regularly. The study aimed to use the transgluteal ultrasonography approach for this purpose and compare its accuracy with the results of CT scan, which is the most commonly used modality.

Methods: Twenty-three patients with an average age of 20-21 months were examined for 1 year after the reduction of DDH, both closed and open.

CAD system based on B-mode and color Doppler sonographic features may predict if a thyroid nodule is hot or cold.

Objectives: The aim of this study was to evaluate if the analysis of sonographic parameters could predict if a thyroid nodule was hot or cold.

Methods: Overall, 102 thyroid nodules, including 51 hyperfunctioning (hot) and 51 hypofunctioning (cold) nodules, were evaluated in this study. Twelve sonographic features (i.