Pure Vision Transformer (CT-ViT) with Noise2Neighbors Interpolation for Low-Dose CT Image Denoising.

Convolutional neural networks (CNN) have been used for a wide variety of deep learning applications, especially in computer vision. For medical image processing, researchers have identified certain challenges associated with CNNs. These challenges encompass the generation of less informative features, limitations in capturing both high and low-frequency information within feature maps, and the computational cost incurred when enhancing receptive fields by deepening the network.

Neonatal Catheter and Tube Placement and Radiographic Assessment Statistics in Relation to Important Anatomic Landmarks.

Objective: Neonatal catheters and tubes are commonly used for monitoring and support for intensive care and must be correctly positioned to avoid complications. Position assessment is routinely done by radiography. The objective of this study is to characterize neonatal catheter and tube placement in terms of the proportion of those devices that are malpositioned.

Deep learning-based age estimation from chest CT scans.

Purpose: Medical imaging can be used to estimate a patient's biological age, which may provide complementary information to clinicians compared to chronological age. In this study, we aimed to develop a method to estimate a patient's age based on their chest CT scan. Additionally, we investigated whether chest CT estimated age is a more accurate predictor of lung cancer risk compared to chronological age.

Classification of endoscopic image and video frames using distance metric-based learning with interpolated latent features.

Unlabelled: Conventional Endoscopy (CE) and Wireless Capsule Endoscopy (WCE) are well known tools for diagnosing gastrointestinal (GI) tract related disorders. Defining the anatomical location within the GI tract helps clinicians determine appropriate treatment options, which can reduce the need for repetitive endoscopy. Limited research addresses the localization of the anatomical location of WCE and CE images using classification, mainly due to the difficulty in collecting annotated data.

Reductions in Bone Mineral Density Are Apparent Early in Children With Prevalent Osteonecrosis Lesions Following Leukemia Therapy.

Osteonecrosis (ON) is a serious complication of childhood acute lymphoblastic leukemia. We determined the prevalence of osteonecrotic lesions in our patient population by a one-time multisite magnetic resonance imaging (MRI) more than 1 year following leukemia therapy. MRI findings were evaluated in relationship to clinical factors (including longitudinal changes in bone mineral density [BMD]).

Applications of deep learning to reduce the need for iodinated contrast media for CT imaging: a systematic review.

Purpose: The usage of iodinated contrast media (ICM) can improve the sensitivity and specificity of computed tomography (CT) for many clinical indications. However, the adverse effects of ICM administration can include renal injury, life-threatening allergic-like reactions, and environmental contamination. Deep learning (DL) models can generate full-dose ICM CT images from non-contrast or low-dose ICM administration or generate non-contrast CT from full-dose ICM CT.

A pictorial review of the radiographic skeletal findings in Morquio syndrome (mucopolysaccharidosis type IV).

Morquio syndrome, also known as Morquio-Brailsford syndrome or mucopolysaccharidosis type IV (MPS IV), is a subgroup of mucopolysaccharidosis. It is an autosomal recessive lysosomal storage disorder. Two subtypes of Morquio syndrome have been identified.

Dilated Convolution ResNet with Boosting Attention Modules and Combined Loss Functions for LDCT Image Denoising.

With the increasing concern regarding the radiation exposure of patients undergoing computed tomography (CT) scans, researchers have been using deep learning techniques to improve the quality of denoised low-dose CT (LDCT) images. In this paper, a cascaded dilated residual network (ResNet) with integrated attention modules, specifically spatial- and channel- attention modules, is proposed. This experiment demonstrated how these attention modules improved the denoised CT image by testing a simple ResNet with and without the modules.

Gradient-based Optimization Algorithm for Hybrid Loss Function in Low-dose CT Denoising.

Deep learning techniques have emerged in de-noising low-dose computed tomography (CT) images to avoid the potential health risks of high ionizing radiation dose on patients. Although these post-processing methods display high quality denoised images, the denoising performance still has the potential to improve. The primary purpose of this work was to determine and analyze the most effective and efficient hybrid loss function in deep learning (DL)-based denoising network.

Clinical Impact and Generalizability of a Computer-Assisted Diagnostic Tool to Risk-Stratify Lung Nodules With CT.

Objective: To evaluate whether an imaging classifier for radiology practice can improve lung nodule classification and follow-up.

Methods: A machine learning classifier was developed and trained using imaging data from the National Lung Screening Trial (NSLT) to produce a malignancy risk score (malignancy Similarity Index [mSI]) for individual lung nodules. In addition to NLST cohorts, external cohorts were developed from a tertiary referral lung cancer screening program data set and an external nonscreening data set of all nodules detected on CT.

Economic Evaluation of Telerobotic Ultrasound Technology to Remotely Provide Ultrasound Services in Rural and Remote Communities.

Introduction: Telerobotic ultrasound technology allows radiologists and sonographers to remotely provide ultrasound services in underserved areas. This study aimed to compare costs associated with using telerobotic ultrasound to provide ultrasound services in rural and remote communities to costs associated with alternate models.

Methods: A cost-minimization approach was used to compare four ultrasound service delivery models: telerobotic ultrasound (Model 1), telerobotic ultrasound and an itinerant sonographer (Model 2), itinerant sonographer without telerobotic ultrasound (Model 3), and travel to another community for all exams (Model 4).

Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review.

Most artificial intelligence (AI) studies have focused primarily on adult imaging, with less attention to the unique aspects of pediatric imaging. The objectives of this study were to (1) identify all publicly available pediatric datasets and determine their potential utility and limitations for pediatric AI studies and (2) systematically review the literature to assess the current state of AI in pediatric chest radiograph interpretation. We searched PubMed, Web of Science and Embase to retrieve all studies from 1990 to 2021 that assessed AI for pediatric chest radiograph interpretation and abstracted the datasets used to train and test AI algorithms, approaches and performance metrics.

Program Report: -Helping Patients and Families Living With Kidney Disease in Northern Saskatchewan.

Purpose Of The Program: , the Cree word for , is an initiative to close gaps in kidney health care for First Nations and Métis patients, their families, and communities in northern Saskatchewan. emerged from a collaboration between the Kidney Health Community Program and First Nations and Métis Health Services to find ways to deliver better care and education to First Nations and Métis people living with kidney disease while acknowledging and the

Sources Of Information: This article describes how traditional Indigenous protocols and storytelling were woven into the events, gathering of patient and family voices in writing and video format, and how this work led to a collaborative co-designed process that incorporates the into kidney care and the benefits we have seen so far. The teachings of the 4 Rs-respect, reciprocity, responsibility, and relevance, were critical to ensuring that reports and learning were shared with participants and the communities represented in this initiative.

A Telerobotic Ultrasound Clinic Model of Ultrasound Service Delivery to Improve Access to Imaging in Rural and Remote Communities.

Objective: Patients living in many rural and remote areas do not have readily available access to ultrasound services because of a lack of sonographers and radiologists in these communities. The objective of this study was to determine the feasibility of using telerobotic ultrasound to establish a service delivery model to remotely provide access to diagnostic ultrasound in rural and remote communities.

Methods: Telerobotic ultrasound clinics were developed in three remote communities more than 500 km away from our academic medical center.

Low Dose CT Image Denoising Using Boosting Attention Fusion GAN with Perceptual Loss.

Image denoising of Low-dose computed tomography (LDCT) images has continues to receive attention in the research community due to ongoing concerns about high-dose radiation exposure of patients for diagnosis. The use of low radiation CT image, however, could lead to inaccurate diagnosis due to the presence of noise. Deep learning techniques are being integrated into denoising methods to address this problem.

Cascaded Learning with Generative Adversarial Networks for Low Dose CT Denoising.

CT machines can be tuned in order to reduce the radiation dose used for imaging, yet reducing the radiation dose results in noisy images which are not suitable in clinical practice. In order for low dose CT to be used effectively in practice this issue must be addressed. Generative Adversarial Networks (GAN) have been used widely in computer vision research and have proven themselves as a powerful tool for producing images with high perceptual quality.

COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life.

In the early months of the COVID-19 pandemic with no designated cure or vaccine, the only way to break the infection chain is self-isolation and maintaining the physical distancing. In this article, we present a potential application of the Internet of Things (IoT) in healthcare and physical distance monitoring for pandemic situations. The proposed framework consists of three parts: a lightweight and low-cost IoT node, a smartphone application (app), and fog-based Machine Learning (ML) tools for data analysis and diagnosis.

Magnetic resonance imaging in boys with severe hemophilia A: Serial and end-of-study findings from the Canadian Hemophilia Primary Prophylaxis Study.

Background: This study examined the structural outcomes for joints of boys with severe hemophilia A receiving frequency/dose-escalated primary prophylaxis using magnetic resonance imaging (MRI), and the importance of interval MRI changes.

Methods: Forty-six subjects (27 with interval studies) were evaluated by radiographs (X-rays) and mid- and end-of-study MRIs (using the International Prophylaxis Study Group scale), as part of the Canadian Hemophilia Prophylaxis Study. The primary outcome was the presence of MRI osteochondral findings.

Sociodemographic and Geographic Factors Associated With Non-Obstetrical Ultrasound Imaging Utilization: A Population-Based Study.

Objective: Ultrasound is one of the most commonly used imaging modalities, though some populations face barriers in accessing ultrasound services, potentially resulting in disparities in utilization. The objective of this study was to assess the association between sociodemographic and geographic factors and non-obstetrical ultrasound utilization in the province of Saskatchewan, Canada.

Methods: All non-obstetrical ultrasound exams performed from 2014 to 2018 in Saskatchewan, Canada were retrospectively identified from province-wide databases.

Sociodemographic and Geographic Disparities in Obstetrical Ultrasound Imaging Utilization: A Population-based Study.

Rationale And Objectives: Obstetrical ultrasound imaging is an important part of prenatal care, though not all patients have readily available access to ultrasound services. This study aimed to assess the association between sociodemographic and geographic factors and (1) having a second trimester complete obstetrical ultrasound and (2) overall obstetrical ultrasound utilization.

Methods: All pregnancies and obstetrical ultrasound exams billed from 2014-2018 in Saskatchewan, Canada were identified from province-wide databases.

Access to ultrasound imaging: A qualitative study in two northern, remote, Indigenous communities in Canada.

Ultrasound imaging is an essential component of healthcare services. This study sought to explore perceptions of access, and factors which shape access, to ultrasound imaging in two northern, remote, Indigenous communities in Canada. Using interpretive description as a methodological approach and a multi-dimensional conceptualisation of access to care as a theoretical framework, 15 semi-structured interviews were conducted in the northern Canadian communities of Stony Rapids and Black Lake, Saskatchewan.

Musculoskeletal ultrasound in hemophilia: Results and recommendations from a global survey and consensus meeting.

Introduction: For persons with hemophilia, optimization of joint outcomes is an important unmet need. The aim of this initiative was to determine use of ultrasound in evaluating arthropathy in persons with hemophilia, and to move toward consensus among hemophilia care providers regarding the preferred ultrasound protocols for global adaptation.

Methods: A global survey of hemophilia treatment centers was conducted that focused on understanding how and why ultrasound was being used and endeavored to move toward consensus definitions of both point-of-care musculoskeletal ultrasound (POC-MSKUS) and full diagnostic ultrasound, terminology to describe structures being assessed by ultrasound, and how these assessments should be interpreted.