Use of artificial intelligence for the detection of infection from upper gastrointestinal endoscopy images: an updated systematic review and meta-analysis.

Background: () infection is associated with various gastrointestinal diseases and may lead to gastric cancer. Currently, endoscopy is the gold standard modality used for diagnosing infection, but it lacks objective indicators and requires expert interpretation. In the past few years, the use of artificial intelligence (AI) for diagnosing gastrointestinal pathologies has increased tremendously and may improve the diagnostic accuracy of endoscopy for infection.

Is Automatic Tumor Segmentation on Whole-Body F-FDG PET Images a Clinical Reality?

The integration of automated whole-body tumor segmentation using F-FDG PET/CT images represents a pivotal shift in oncologic diagnostics, enhancing the precision and efficiency of tumor burden assessment. This editorial examines the transition toward automation, propelled by advancements in artificial intelligence, notably through deep learning techniques. We highlight the current availability of commercial tools and the academic efforts that have set the stage for these developments.

Extracting value from total-body PET/CT image data - the emerging role of artificial intelligence.

The evolution of Positron Emission Tomography (PET), culminating in the Total-Body PET (TB-PET) system, represents a paradigm shift in medical imaging. This paper explores the transformative role of Artificial Intelligence (AI) in enhancing clinical and research applications of TB-PET imaging. Clinically, TB-PET's superior sensitivity facilitates rapid imaging, low-dose imaging protocols, improved diagnostic capabilities and higher patient comfort.

Fully Automated, Fast Motion Correction of Dynamic Whole-Body and Total-Body PET/CT Imaging Studies.

We introduce the Fast Algorithm for Motion Correction (FALCON) software, which allows correction of both rigid and nonlinear motion artifacts in dynamic whole-body (WB) images, irrespective of the PET/CT system or the tracer. Motion was corrected using affine alignment followed by a diffeomorphic approach to account for nonrigid deformations. In both steps, images were registered using multiscale image alignment.

Conditional Generative Adversarial Networks Aided Motion Correction of Dynamic F-FDG PET Brain Studies.

This work set out to develop a motion-correction approach aided by conditional generative adversarial network (cGAN) methodology that allows reliable, data-driven determination of involuntary subject motion during dynamic F-FDG brain studies. Ten healthy volunteers (5 men/5 women; mean age ± SD, 27 ± 7 y; weight, 70 ± 10 kg) underwent a test-retest F-FDG PET/MRI examination of the brain ( = 20). The imaging protocol consisted of a 60-min PET list-mode acquisition contemporaneously acquired with MRI, including MR navigators and a 3-dimensional time-of-flight MR angiography sequence.

Potentials and caveats of AI in hybrid imaging.

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data.

Optimization for customized trajectories in cone beam computed tomography.

Purpose: We developed a target-based cone beam computed tomography (CBCT) imaging framework for optimizing an unconstrained three dimensional (3D) source-detector trajectory by incorporating prior image information. Our main aim is to enable a CBCT system to provide topical information about the target using a limited angle noncircular scan orbit with a minimal number of projections. Such a customized trajectory should include enough information to sufficiently reconstruct a particular volume of interest (VOI) under kinematic constraints, which may result from the patient size or additional surgical or radiation therapy-related equipment.

What scans we will read: imaging instrumentation trends in clinical oncology.

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non-invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.

Fully Integrated PET/MR Imaging for the Assessment of the Relationship Between Functional Connectivity and Glucose Metabolic Rate.

Unlabelled: In the past, determination of absolute values of cerebral metabolic rate of glucose (CMRGlc) in clinical routine was rarely carried out due to the invasive nature of arterial sampling. With the advent of combined PET/MR imaging technology, CMRGlc values can be obtained non-invasively, thereby providing the opportunity to take advantage of fully quantitative data in clinical routine. However, CMRGlc values display high physiological variability, presumably due to fluctuations in the intrinsic activity of the brain at rest.

Promise of Fully Integrated PET/MRI: Noninvasive Clinical Quantification of Cerebral Glucose Metabolism.

We describe a fully automated processing pipeline to support the noninvasive absolute quantification of the cerebral metabolic rate for glucose (CMRGlc) in a clinical setting. This pipeline takes advantage of "anatometabolic" information associated with fully integrated PET/MRI. Ten healthy volunteers (5 men and /5 women; 27 ± 7 y old; 70 ± 10 kg) underwent a test-retest F-FDG PET/MRI examination of the brain.

Population-based surveillance for severe rotavirus gastroenteritis in children in Karachi, Pakistan.

The incidence of rotavirus-associated severe diarrhoea and distribution of rotavirus genotypes in children less than five years of age was determined in two low-income communities in Karachi, Pakistan. Over a two-year period, 717 children met eligibility criteria for severe diarrhoea and stools were obtained from 575 (80%) with 97 (17%) being rotavirus positive. Adjusted annual rates of severe rotavirus diarrhoea in children less than five years and less than one year were respectively 5.