Off-label in-silico flow diverter performance assessment in posterior communicating artery aneurysms.

Background: The posterior communicating artery (PComA) is among the most common intracranial aneurysm locations, but flow diverter (FD) treatment with the widely used pipeline embolization device (PED) remains an off-label treatment that is not well understood. PComA aneurysm flow diversion is complicated by the presence of fetal posterior circulation (FPC), which has an estimated prevalence of 4-29% and is more common in people of black (11.5%) than white (4.

Joint shape/texture representation learning for cardiovascular disease diagnosis from magnetic resonance imaging.

Aims: Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide. Cardiac image and mesh are two primary modalities to present the shape and structure of the heart and have been demonstrated to be efficient in CVD prediction and diagnosis. However, previous research has been generally focussed on a single modality (image or mesh), and few of them have tried to jointly consider the image and mesh representations of heart.

Multi-centre benchmarking of deep learning models for COVID-19 detection in chest x-rays.

Introduction: This study is a retrospective evaluation of the performance of deep learning models that were developed for the detection of COVID-19 from chest x-rays, undertaken with the goal of assessing the suitability of such systems as clinical decision support tools.

Methods: Models were trained on the National COVID-19 Chest Imaging Database (NCCID), a UK-wide multi-centre dataset from 26 different NHS hospitals and evaluated on independent multi-national clinical datasets. The evaluation considers clinical and technical contributors to model error and potential model bias.

Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology.

Recent genome-wide association studies have successfully identified associations between genetic variants and simple cardiac morphological parameters derived from cardiac magnetic resonance images. However, the emergence of large databases, including genetic data linked to cardiac magnetic resonance facilitates the investigation of more nuanced patterns of cardiac shape variability than those studied so far. Here we propose a framework for gene discovery coined unsupervised phenotype ensembles.

A Generative Shape Compositional Framework to Synthesize Populations of Virtual Chimeras.

Generating virtual organ populations that capture sufficient variability while remaining plausible is essential to conduct in silico trials (ISTs) of medical devices. However, not all anatomical shapes of interest are always available for each individual in a population. The imaging examinations and modalities used can vary between subjects depending on their individualized clinical pathways.

Retinal imaging for the assessment of stroke risk: a systematic review.

Background: Stroke is a leading cause of morbidity and mortality. Retinal imaging allows non-invasive assessment of the microvasculature. Consequently, retinal imaging is a technology which is garnering increasing attention as a means of assessing cardiovascular health and stroke risk.

Beyond images: an integrative multi-modal approach to chest x-ray report generation.

Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images. Most existing methods focus solely on the image data, disregarding the other patient information accessible to radiologists. In this paper, we present a novel multi-modal deep neural network framework for generating chest x-rays reports by integrating structured patient data, such as vital signs and symptoms, alongside unstructured clinical notes.

Bronchoscopic lung volume reduction in emphysema: a review.

Purpose Of Review: Chronic obstructive pulmonary disease (COPD) poses a substantial burden on the healthcare system and is currently considered the sixth leading cause of death in the United States. Emphysema, as evidenced by severe air-trapping in patients with COPD, leads to significant dyspnea and morbidity. Lung volume reduction via surgery or minimally invasive endobronchial interventions are currently available, which improve lung function and quality of life.

Artificial intelligence in ovarian cancer histopathology: a systematic review.

This study evaluates the quality of published research using artificial intelligence (AI) for ovarian cancer diagnosis or prognosis using histopathology data. A systematic search of PubMed, Scopus, Web of Science, Cochrane CENTRAL, and WHO-ICTRP was conducted up to May 19, 2023. Inclusion criteria required that AI was used for prognostic or diagnostic inferences in human ovarian cancer histopathology images.

The role of bronchoscopy in the multidisciplinary approach to benign tracheal stenosis.

Benign tracheal stenosis can cause dyspnea, wheezing, and cough mimicking other obstructive lung diseases which often leads to a delay in the diagnosis. Risk factors and etiologies for tracheal strictures include autoimmune diseases, infection, gastro-esophageal reflux disease (GERD), radiation injury and iatrogenic factors such as post-intubation and post-tracheostomy. Once suspected, tracheal strictures are diagnosed by performing a thorough evaluation involving clinical exam, laboratory workup, pulmonary function test, chest imaging and bronchoscopy.

Hemodynamics of thrombus formation in intracranial aneurysms: An observational study.

How prevalent is spontaneous thrombosis in a population containing all sizes of intracranial aneurysms? How can we calibrate computational models of thrombosis based on published data? How does spontaneous thrombosis differ in normo- and hypertensive subjects? We address the first question through a thorough analysis of published datasets that provide spontaneous thrombosis rates across different aneurysm characteristics. This analysis provides data for a subgroup of the general population of aneurysms, namely, those of large and giant size (>10 mm). Based on these observed spontaneous thrombosis rates, our computational modeling platform enables the first observational study of spontaneous thrombosis prevalence across a broader set of aneurysm phenotypes.

Mapping of QTLs for morphophysiological and yield traits under water-deficit stress and well-watered conditions in maize.

Maize productivity is significantly impacted by drought; therefore, improvement of drought tolerance is a critical goal in maize breeding. To achieve this, a better understanding of the genetic basis of drought tolerance is necessary. Our study aimed to identify genomic regions associated with drought tolerance-related traits by phenotyping a mapping population of recombinant inbred lines (RILs) for two seasons under well-watered (WW) and water-deficit (WD) conditions.

Virtual high-resolution MR angiography from non-angiographic multi-contrast MRIs: synthetic vascular model populations for in-silico trials.

Despite success on multi-contrast MR image synthesis, generating specific modalities remains challenging. Those include Magnetic Resonance Angiography (MRA) that highlights details of vascular anatomy using specialised imaging sequences for emphasising inflow effect. This work proposes an end-to-end generative adversarial network that can synthesise anatomically plausible, high-resolution 3D MRA images using commonly acquired multi-contrast MR images (e.

Intraoperative cell-salvaged autologous blood transfusion is safe in metastatic spine tumour surgery: early outcomes of prospective clinical study.

Purpose: Allogeneic blood transfusion (ABT) is current standard of blood replenishment despite known complications. Salvaged blood transfusion (SBT) addresses majority of such complications. Surgeons remain reluctant to employ SBT in metastatic spine tumour surgery (MSTS), despite ample laboratory evidence.

RecON: Online learning for sensorless freehand 3D ultrasound reconstruction.

Sensorless freehand 3D ultrasound (US) reconstruction based on deep networks shows promising advantages, such as large field of view, relatively high resolution, low cost, and ease of use. However, existing methods mainly consider vanilla scan strategies with limited inter-frame variations. These methods thus are degraded on complex but routine scan sequences in clinics.

Advanced Imaging for Robotic Bronchoscopy: A Review.

Recent advances in navigational platforms have led bronchoscopists to make major strides in diagnostic interventions for pulmonary parenchymal lesions. Over the last decade, multiple platforms including electromagnetic navigation and robotic bronchoscopy have allowed bronchoscopists to safely navigate farther into the lung parenchyma with increased stability and accuracy. Limitations persist, even with these newer technologies, in achieving a similar or higher diagnostic yield when compared to the transthoracic computed tomography (CT) guided needle approach.

An Automated Method for Artifical Intelligence Assisted Diagnosis of Active Aortitis Using Radiomic Analysis of FDG PET-CT Images.

The aim of this study was to develop and validate an automated pipeline that could assist the diagnosis of active aortitis using radiomic imaging biomarkers derived from [18F]-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography (FDG PET-CT) images. The aorta was automatically segmented by convolutional neural network (CNN) on FDG PET-CT of aortitis and control patients. The FDG PET-CT dataset was split into training (43 aortitis:21 control), test (12 aortitis:5 control) and validation (24 aortitis:14 control) cohorts.

Post hypoxic myoclonus: A tale of two minds.

Post hypoxic myoclonus (PHM) is considered a poor prognostic sign and may influence decisions regarding withdrawal of treatment. PHM is generally categorized in literature as either acute or chronic (also commonly referred to as Lance-Adams Syndrome) based on the onset of myoclonus. However, it may be more accurate to differentiate between the various presentations of PHM based on the clinical characteristics and electroencephalogram (EEG) findings for prognostication.