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. Transformers have emerged as an approach aiming to address and overcome these specific limitations of CNNs in the context of medical image analysis. Preservation of all spatial details of medical images is necessary to ensure accurate patient diagnosis. Hence, this research introduced the use of a pure Vision Transformer (ViT) for a denoising artificial neural network for medical image processing specifically for low-dose computed tomography (LDCT) image denoising. The proposed model follows a U-Net framework that contains ViT modules with the integration of Noise2Neighbor (N2N) interpolation operation. Five different datasets containing LDCT and normal-dose CT (NDCT) image pairs were used to carry out this experiment. To test the efficacy of the proposed model, this experiment includes comparisons between the quantitative and visual results among CNN-based (BM3D, RED-CNN, DRL-E-MP), hybrid CNN-ViT-based (TED-Net), and the proposed pure ViT-based denoising model. The findings of this study showed that there is about 15-20% increase in SSIM and PSNR when using self-attention transformers than using the typical pure CNN. Visual results also showed improvements especially when it comes to showing fine structural details of CT images.

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. METHODS: To develop our age prediction model, we utilized composite CT images and Inception-ResNet-v2. The model was trained, validated, and tested on 13,824 chest CT scans from the National Lung Screening Trial, with 91% for training, 5% for validation, and 4% for testing. Additionally, we independently tested the model on 1849 CT scans collected locally. To assess chest CT estimated age as a risk factor for lung cancer, we computed the relative lung cancer risk between two groups. Group 1 consisted of individuals assigned a CT age older than their chronological age, while Group 2 comprised those assigned a CT age younger than their chronological age. RESULTS: Our analysis revealed a mean absolute error of 1.84 years and a Pearson's correlation coefficient of 0.97 for our local data when comparing chronological age with the estimated CT age. The model showed the most activation in the area associated with the lungs during age estimation. The relative risk for lung cancer was 1.82 (95% confidence interval, 1.65-2.02) for individuals assigned a CT age older than their chronological age compared to those assigned a CT age younger than their chronological age. CONCLUSION: Findings suggest that chest CT age captures some aspects of biological aging and may be a more accurate predictor of lung cancer risk than chronological age. Future studies with larger and more diverse patients are required for the generalization of the interpretations.

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. STUDY DESIGN: Using an institutional dataset of 723 chest/abdominal radiographs of neonatal intensive care unit (ICU) patients (all within 60 days of birth), we assessed the proportion of catheters that are malpositioned. Many radiographs contained multiple catheter types. Umbilical venous catheters (UVCs; 448 radiographs), umbilical arterial catheters (UACs; 259 radiographs), endotracheal tubes (ETTs; 451 radiographs), and nasogastric tubes (NGTs; 603 radiographs) were included in our analysis. RESULTS: UVCs were malpositioned in 90% of radiographs, while UACs were malpositioned in 36%, ETTs in 30%, and NGTs in just 5%. The most common locations in which UVCs were malpositioned were in the right atrium (31%) and umbilical vein (21%), and for UACs the most common malpositioned tip location was the aortic arch (8%). For the remaining tubes, 5% of ETTs were found to be in the right main bronchus and 4% of NGTs were found in the esophagus. CONCLUSION: A substantial proportion of catheters and tubes are malpositioned, suggesting that optimizing methods of catheter placement and assessment ought to be areas of focus for future work. KEY POINTS: · Neonatal catheters are frequently malpositioned.. · Most umbilical venous catheters need readjustment.. · X-ray and ultrasound are important for assessment.. · Catheter tips should be assessed in all X-rays..

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]). Eighty-six children enrolled in the Steroid Associated Osteoporosis in the Pediatric Population (STOPP) study were evaluated for ON at 3.1 ± 1.3 years following therapy. Thirty children had a total of 150 confirmed ON lesions (35%). Lumbar spine (LS) BMD Z-scores (mean ± SD) were low at diagnosis and similar between patients with and without ON (-1.09 ± 1.53 versus -1.27 ± 1.25, p = 0.549). LS BMD Z-scores declined from baseline to 12 months in children with ON (-0.31 ± 1.02) but not in those without (0.13 ± 0.82, p = 0.035); the hip BMD Z-scores from baseline to 24 months declined in both groups, but to a greater extent in those with ON (-1.77 ± 1.22) compared to those without (-1.03 ± 1.07, p = 0.045). At the time of the MRI, mean total hip and total body (TB) BMD Z-scores were lower in children with ON (hip -0.98 ± 0.95 versus -0.28 ± 1.06, p = 0.010; TB -1.36 ± 1.10 versus -0.48 ± 1.50, p = 0.018). Pain occurred in 11/30 (37%) with ON versus 20/56 (36%) without, p = 0.841. In multivariable models, older age at diagnosis (odds ratio [OR] 1.57; 95% confidence interval [CI], 1.15-2.13; p = 0.004), and hip BMD Z-score at MRI (OR 2.23; 95% CI, 1.02-4.87; p = 0.046) were independently associated with ON. Overall, one-third of children demonstrated ON after leukemia therapy. Those with ON had greater reductions in spine and hip BMD Z-scores in the first 1 and 2 years of therapy, respectively. Older age and lower hip BMD Z-scores at MRI were significantly associated with prevalent, off-therapy ON. These data assist in identifying children at risk of ON. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

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

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. In this study, we present a few-shot learning method based on distance metric learning which combines transfer-learning and manifold mixup schemes to localize and classify endoscopic images and video frames. The proposed method allows us to develop a pipeline for endoscopy video sequence localization that can be trained with only a few samples. The use of manifold mixup improves learning by increasing the number of training epochs while reducing overfitting and providing more accurate decision boundaries. A dataset is collected from 10 different anatomical positions of the human GI tract. Two models were trained using only 78 CE and 27 WCE annotated frames to predict the location of 25,700 and 1825 video frames from CE and WCE respectively. We performed subjective evaluation using nine gastroenterologists to validate the need of having such an automated system to localize endoscopic images and video frames. Our method achieved higher accuracy and a higher F1-score when compared with the scores from subjective evaluation. In addition, the results show improved performance with less cross-entropy loss when compared with several existing methods trained on the same datasets. This indicates that the proposed method has the potential to be used in endoscopy image classification. Supplementary Information: The online version contains supplementary material available at 10.1007/s11042-023-14982-1.

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. Eliminating the need for both contrast-enhanced and non-enhanced imaging or reducing the amount of required contrast while maintaining diagnostic capability may reduce overall patient risk, improve efficiency and minimize costs. We reviewed the current capabilities of DL to reduce the need for contrast administration in CT. METHODS: We conducted a systematic review of articles utilizing DL to reduce the amount of ICM required in CT, searching MEDLINE, Embase, Compendex, Inspec, and Scopus to identify papers published from 2016 to 2022. We classified the articles based on the DL model and ICM reduction. RESULTS: Eighteen papers met the inclusion criteria for analysis. Of these, ten generated synthetic full-dose (100%) ICM from real non-contrast CT, while four augmented low-dose to full-dose ICM CT. Three used DL to create synthetic non-contrast CT from real 100% ICM CT, while one paper used DL to translate the 100% ICM to non-contrast CT and vice versa. DL models commonly used generative adversarial networks trained and tested by paired contrast-enhanced and non-contrast or low ICM CTs. Image quality metrics such as peak signal-to-noise ratio and structural similarity index were frequently used for comparing synthetic versus real CT image quality. CONCLUSION: DL-generated contrast-enhanced or non-contrast CT may assist in diagnosis and radiation therapy planning; however, further work to optimize protocols to reduce or eliminate ICM for specific pathology is still needed along with a dedicated assessment of the clinical utility of these synthetic images.

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. In MPS IVA, a deficiency in N-acetylgalactosamine-6-sulfate sulfatase interrupts the normal metabolic pathway of degrading glycosaminoglycans. Accumulated undigested glycosaminoglycans in the tissue and bones result in complications leading to severe skeletal deformity. In MPS IVB, a deficiency in beta-galactosidase results in a milder phenotype than in MPS IVA. Morquio syndrome presents a variety of clinical manifestations in a spectrum of mild to severe. It classically has been considered a skeletal dysplasia with significant skeletal involvement. However, the extraskeletal features can also provide valuable information to guide further work-up to assess the possibility of the disorder. Although the disease involves almost all parts of the body, it most commonly affects the axial skeleton, specifically the vertebrae. The characteristic radiologic findings in MPS IV, such as paddle-shaped ribs, odontoid hypoplasia, vertebral deformity, metaphyseal and epiphyseal bone dysplasia, and steep acetabula, are encompassed in the term "dysostosis multiplex," which is a common feature among other types of MPS and storage disorders. Myelopathy due to spinal cord compression and respiratory airway obstruction are the most critical complications related to mortality and morbidity. The variety of clinical features, as well as overlapping of radiological findings with other disorders, make diagnosis challenging, and delays in diagnosis and treatment may lead to critical complications. Timely imaging and radiologic expertise are important components for diagnosis. Gene therapies may provide robust treatment, particularly if genetic variations can be screened in utero.

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. Performance of the mSI combined with Lung-RADS was compared with Lung-RADS alone and the Mayo and Brock risk calculators. RESULTS: We analyzed 963 subjects and 1,331 nodules across these cohorts. The mSI was comparable in accuracy (area under the curve = 0.89) to existing clinical risk models (area under the curve = 0.86-0.88) and independently predictive in the NLST cohort of 704 nodules. When compared with Lung-RADS, the mSI significantly increased sensitivity across all cohorts (25%-117%), with significant increases in specificity in the screening cohorts (17%-33%). When used in conjunction with Lung-RADS, use of mSI would result in earlier diagnoses and reduced follow-up across cohorts, including the potential for early diagnosis in 42% of malignant NLST nodules from prior-year CT scans. CONCLUSION: A computer-assisted diagnosis software improved risk classification from chest CTs of screening and incidentally detected lung nodules compared with Lung-RADS. mSI added predictive value independent of existing radiological and clinical variables. These results suggest the generalizability and potential clinical impact of a tool that is straightforward to implement in practice.

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). In Models 1-3, travel was assumed when exams could not be successfully performed telerobotically or by an itinerant sonographer. A publicly funded healthcare payer perspective was used for the reference case and a societal perspective was used for a secondary non-reference case. Costs were based on the literature and experience using telerobotic ultrasound in Saskatchewan, Canada. Costs were expressed in 2020 Canadian dollars. RESULTS: Average cost per ultrasound exam was $342, $323, $368, and $478 for Models 1, 2, 3, and 4, respectively, from a publicly funded healthcare payer perspective, and $461, $355, $447, and $849, respectively, from a societal perspective. In one-way sensitivity analyses, Model 2 was the lowest cost from a payer perspective for communities with population >2075 people, distance >350 km from the nearest ultrasound facility, or >47% of the population eligible for publicly funded medical transportation. CONCLUSION: Health systems may wish to consider solutions such as telerobotic ultrasound and itinerant sonographers to reduce healthcare costs and improve access to ultrasound in rural and remote communities.

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. Objective functions in deep learning algorithms are the main keys for optimizing the parameters of a network and can affect the quality of the denoised image significantly. Hence, this work examined the various combinations of the most common objective functions in CT denoising networks, namely L1 loss, per-pixel loss, perceptual loss, and structural dissimilarity loss. Further, a hyperparameter learning algorithm was also introduced to find the best scalable factors of the loss functions in each hybrid loss function combination. For simplic-ity, RED-CNN was used in this study to easily demonstrate the performance of the losses during the denoising process. Based on this experiment, the balance between these loss function via the gradient-based optimization algorithm could help in the generalizability prediction of designing future CT denoising networks.

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. Further, an investigation regarding the effectiveness of per-pixel loss, perceptual loss via VGG16-Net, and structural dissimilarity loss functions is also covered through an ablation experiment. By knowing how these loss functions affect the output denoised images, a combination of the these loss function is then proposed which aims to prevent edge over-smoothing, enhance textural details and finally, preserve structural details on the denoised images. Finally, a bench testing was also done by comparing the visual and quantitative results of the proposed model with the state-of-the-art models such as block matching 3D (BM3D), patch-GAN and dilated convolution with edge detection layer (DRL-E-MP) for accuracy.

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. Of 29 publicly available chest radiograph datasets, 2 datasets included solely pediatric chest radiographs, and 7 datasets included pediatric and adult patients. We identified 55 articles that implemented an AI model to interpret pediatric chest radiographs or pediatric and adult chest radiographs. Classification of chest radiographs as pneumonia was the most common application of AI, evaluated in 65% of the studies. Although many studies report high diagnostic accuracy, most algorithms were not validated on external datasets. Most AI studies for pediatric chest radiograph interpretation have focused on a limited number of diseases, and progress is hindered by a lack of large-scale pediatric chest radiograph datasets.

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

PURPOSE OF THE PROGRAM: Nîsohkamâtowak, the Cree word for Helping Each Other, is an initiative to close gaps in kidney health care for First Nations and Métis patients, their families, and communities in northern Saskatchewan. Nîsohkamâtowak 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 Truth and Reconciliation and the Calls to Action. SOURCES OF INFORMATION: This article describes how traditional Indigenous protocols and storytelling were woven into the Nîsohkamâtowak 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 Truth and Reconciliation: Calls to Action 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 Nîsohkamâtowak reports and learning were shared with participants and the communities represented in this initiative. METHODS: Group discussions and sharing circles were facilitated in several locations throughout northern and central Saskatchewan. Main topics of discussion were traditional medicines, residential schools impact, community and peer supports for kidney disease patients, and cultural safety education for health care providers. KEY FINDINGS: The general themes selected for improvement were education, support within the local community, traditional practices and cultural competency, and delivery of services. To address these gaps in kidney care, the following objectives were co-created with First Nations and Métis patients, families, and communities for Kidney Health to provide culturally appropriate education and resources, to ensure appropriate follow-up support to include strengthening connections to communities and other health authorities, to incorporate traditional practices into program design, and to ensure appropriate service delivery across the spectrum of care with a focus on screening and referral, which is strongly linked to coordination of care with local health centers. IMPLICATIONS: As a result of this work, the Kidney Health Community Program restructured the delivery of services and continues to work with Nîsohkamâtowak advisors on safety initiatives and chronic kidney disease awareness, prevention, and management in their respective communities. The Truth and Reconciliation and Calls to Action are honored to close the gaps in kidney care. LIMITATIONS: Nîsohkamâtowak is a local Kidney Health initiative that has the good fortune of having dedicated funding and staff to carry out this work. The findings may be unique to the First Nations and Métis communities and people who shared their stories. Truth and Reconciliation is an ongoing commitment that must be nurtured. Although not part of this publication, the effects of COVID-19 have made it difficult to further advance the Calls to Action, with more limited staff resources and the inability to meet in person as in the past.

OBJECTIFS DU PROGRAMME: Nîsohkamâtowak, un terme cri signifiant « s'aider les uns les autres ¼, est une initiative qui vise à combler les lacunes dans les soins de santé rénaux pour les patients des Premières Nations et Métis, leurs familles et leurs collectivités du nord de la Saskatchewan. Nîsohkamâtowak est née d'une collaboration entre le Kidney Health Community Program et First Nations and Métis Health Services pour trouver des moyens d'offrir de meilleurs soins et une meilleure éducation aux membres des Premières Nations et aux Métis qui vivent avec une néphropathie, tout en reconnaissant les appels à l'action de la Commission de vérité et réconciliation du Canada. SOURCES: Cet article décrit comment les protocoles et récits autochtones traditionnels ont été intégrés aux événements de Nîsohkamâtowak, comment les voix des patients et des familles ont été recueillies par écrit et sous forme de vidéos, et comment ces travaux ont mené à un processus collaboratif de conception conjointe qui intègre les recommandations de Vérité et réconciliation : Appels à l'action dans les soins rénaux et qui a mené aux avantages constatés jusqu'à présent. L'enseignement des quatre grands principes ­ respect, réciprocité, responsabilité et pertinence ­ était essentiel pour assurer que les rapports et les apprentissages de Nîsohkamâtowak soient partagés avec les participants et les communautés représentées par cette initiative. PRINCIPAUX RÉSULTATS: Les thèmes généraux suivants ont été retenus pour amélioration : l'éducation, le soutien dans la communauté locale, les pratiques traditionnelles et les compétences culturelles, et la prestation des services. Pour combler ces lacunes dans les soins rénaux, les objectifs suivants liés à la santé rénale ont été développés conjointement avec les patients, les familles et les communautés des Premières Nations et des Métis: offrir une éducation et des ressources adaptées à la culture; assurer un soutien de suivi approprié, notamment en renforçant les liens avec les communautés et avec les autres autorités sanitaires; intégrer les pratiques traditionnelles dans la conception des programmes; et garantir la prestation appropriée des services dans tout l'éventail des soins, en mettant l'accent sur le dépistage et l'aiguillage, qui sont fortement liés à la coordination des soins avec les centres de santé locaux. RÉSULTATS: À la suite de ces travaux, le Kidney Health Community Program a restructuré la prestation des services et continue à ce jour de travailler avec les conseillers de Nîsohkamâtowak sur des initiatives de sécurité, ainsi que sur la sensibilisation, la prévention et la prise en charge de l'insuffisance rénale chronique dans leurs collectivités respectives. La Commission de vérité et réconciliation du Canada : appel à l'action est honorée de combler les lacunes dans les soins rénaux. LIMITES: Nîsohkamâtowak est une initiative locale de santé rénale qui bénéficie d'un financement et d'un personnel dédiés pour accomplir ce travail. Les résultats pourraient être propres aux communautés des Premières Nations et des Métis et aux personnes qui ont partagé leurs histoires. La Commission de vérité et réconciliation est un engagement permanent qui doit être soutenu. Bien que cela ne soit pas souligné dans cette publication, la pandémie de COVID-19 a rendu difficile l'avancement des appels à l'action en raison des ressources humaines plus limitées et de l'incapacité de se rencontrer en personne comme auparavant.

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. Sonographers remotely performed all ultrasound examinations using telerobotic ultrasound systems, and examinations were subsequently interpreted by radiologists at an academic medical center. Diagnostic performance was assessed by each interpreting radiologist using a standardized reporting form. Patient experience was assessed through quantitative and qualitative analysis of survey responses. Operational challenges and solutions were identified. RESULTS: Eighty-seven telerobotic ultrasound examinations were remotely performed and included in this study, with the most frequent examination types being abdominal (n = 35), first-trimester obstetrical (n = 26), and second-trimester complete obstetrical (n = 12). Across all examination types, 70% of telerobotic ultrasound examinations were sufficient for diagnosis, minimizing travel or reducing wait times for these patients. Ninety-five percent of patients would be willing to have another telerobotic ultrasound examination in the future. Operational challenges were related to technical infrastructure, human resources, and coordination between clinic sites. CONCLUSION: Telerobotic ultrasound can provide access to diagnostic ultrasound services to underserved rural and remote communities without regular ultrasound services, thereby reducing disparities in access to care and improving health equity.

Telerobotic ultrasound to provide obstetrical ultrasound services remotely during the COVID-19 pandemic.

INTRODUCTION: Obstetrical ultrasound imaging is critical in identifying at-risk pregnancies and informing clinical management. The coronavirus disease 2019 (COVID-19) pandemic has exacerbated challenges in accessing obstetrical ultrasound for patients in underserved rural and remote communities where this service is not available. This prospective descriptive study describes our experience of providing obstetrical ultrasound services remotely using a telerobotic ultrasound system in a northern Canadian community isolated due to a COVID-19 outbreak. METHODS: A telerobotic ultrasound system was used to perform obstetrical ultrasound exams remotely in La Loche, Canada, a remote community without regular access to obstetrical ultrasound. Using a telerobotic ultrasound system, a sonographer 605 km away remotely controlled an ultrasound probe and ultrasound settings. Twenty-one exams were performed in a five-week period during a COVID-19 outbreak in the community, including limited first-, second- and third-trimester exams (n = 11) and complete second-trimester exams (n = 10). Participants were invited to complete a survey at the end of the telerobotic ultrasound exam describing their experiences with telerobotic ultrasound. Radiologists subsequently interpreted all exams and determined the adequacy of the images for diagnosis. RESULTS: Of 11 limited obstetrical exams, radiologists indicated images were adequate in nine (81%) cases, adequate with some reservations in one (9%) case and inadequate in one (9%) case. Of 10 second-trimester complete obstetrical exams, radiologists indicated images were adequate in two (20%) cases, adequate with some reservations in three (30%) cases and inadequate in five (50%) cases. Second-trimester complete obstetrical exams were limited due to a combination of body habitus, foetal lie and telerobotic technology. DISCUSSION: A telerobotic ultrasound system may be used to answer focused clinical questions such as foetal viability, dating and foetal presentation in a timely manner while minimising patient travel to larger centres and potential exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), during the COVID-19 pandemic.

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. Univariate and multivariate Poisson regression analyses were performed to assess the association between ultrasound utilization and sex, age, First Nations status, Charlson Comorbidity Index, urban vs. rural residence, geographic remoteness, and neighborhood income. RESULTS: A total of 1,324,846 individuals (5,857,044 person-years) were included in the analysis. Female sex (adjusted incidence rate ratio [aIRR], 2.20; 95% confidence interval [CI], 2.19-2.22), age (aIRR, 4.97; 95% CI, 4.90-5.05 for ≥57 years vs. <11 years), comorbidities (aIRR, 4.36 for Charlson Comorbidity Index >10 vs. 0; 95% CI, 3.78-5.03), and higher neighborhood income (aIRR, 1.04; 95% CI, 1.02-1.05 for highest vs. lowest quintile) were associated with higher rates of ultrasound utilization. Individuals who were status First Nations (aIRR, 0.91; 95% CI, 0.90-0.92) or resided in geographically remote areas (aIRR, 0.87 for most vs. least remote; 95% CI, 0.83-0.91) had lower rates of ultrasound utilization. Individuals who lived in a rural area also had lower rates of ultrasound utilization (aIRR, 0.93; 95% CI, 0.92-0.94). CONCLUSION: Substantial disparities exist in non-obstetrical ultrasound utilization among individuals in low-income neighborhoods, status First Nations individuals, and individuals in rural and remote communities.

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. Generalized estimating equation (GEE) models with binomial and Poisson distributions were used to identify factors associated with having a second trimester ultrasound and overall obstetrical ultrasound utilization, respectively. RESULTS: 80,536 pregnancies from 57,881 individuals were included. Of 57,186 pregnancies carried to ≥23 weeks, a second trimester ultrasound was performed in 50,180 (87.7%). Patients living in rural areas (adjusted odds ratio [aOR], 0.70; 95% confidence interval [CI], 0.63-0.77; p <0.0001), remote areas (aOR, 0.35 for greatest vs. least remoteness level; 95% CI, 0.32-0.39; p <0.0001), and status First Nations individuals (aOR, 0.50; 95% CI, 0.46-0.53; p <0.0001) were less likely to have a second trimester ultrasound. Patients living in higher income neighbourhoods (aOR, 1.86 for highest vs. lowest quintile; 95% CI, 1.62-2.13; p <0.0001) were more likely to have a second trimester ultrasound. GEE Poisson regression analysis demonstrated these same factors, except rural residence, were associated with overall obstetrical ultrasound utilization. CONCLUSION: Substantial disparities in obstetrical ultrasound utilization exist among patients in remote geographic areas, Indigenous peoples, and patients in low income neighbourhoods. Addressing barriers which these demographic groups face in accessing ultrasound imaging is critical to ensure health equity.