Anatomy-centred deep learning improves generalisability and progression prediction in radiographic sacroiliitis detection.

Purpose: To examine whether incorporating anatomy-centred deep learning can improve generalisability and enable prediction of disease progression.

Methods: This retrospective multicentre study included conventional pelvic radiographs of four different patient cohorts focusing on axial spondyloarthritis collected at university and community hospitals. The first cohort, which consisted of 1483 radiographs, was split into training (n=1261) and validation (n=222) sets.

Artificial intelligence to analyze magnetic resonance imaging in rheumatology.

Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable.

Dual center validation of deep learning for automated multi-label segmentation of thoracic anatomy in bedside chest radiographs.

Background And Objectives: Bedside chest radiographs (CXRs) are challenging to interpret but important for monitoring cardiothoracic disease and invasive therapy devices in critical care and emergency medicine. Taking surrounding anatomy into account is likely to improve the diagnostic accuracy of artificial intelligence and bring its performance closer to that of a radiologist. Therefore, we aimed to develop a deep convolutional neural network for efficient automatic anatomy segmentation of bedside CXRs.

Successful treatment of severe COVID-19 pneumonia, a case series with simultaneous interleukin-1 and interleukin-6 blockade with 1-month follow-up.

Interleukin (IL)-6 and IL-1 blockade showed beneficial results in patients with severe COVID-19 pneumonia and evidence of cytokine release at the early disease stage. Here, we report outcomes of open-label therapy with a combination of blocking IL-6 with tocilizumab 8 mg/kg up to 800 mg and IL-1 receptor antagonist anakinra 100-300 mg over 3-5 days. Thirty-one adult patients with severe COVID-19 pneumonia and signs of cytokine release, mean age 54 (30-79) years, 5 female, 26 male, and mean symptom duration 6 (3-10) days were treated.

Deep Learning Detects Changes Indicative of Axial Spondyloarthritis at MRI of Sacroiliac Joints.

Background MRI is frequently used for early diagnosis of axial spondyloarthritis (axSpA). However, evaluation is time-consuming and requires profound expertise because noninflammatory degenerative changes can mimic axSpA, and early signs may therefore be missed. Deep neural networks could function as assistance for axSpA detection.

Do submillisievert-chest CT protocols impact diagnostic quality in suspected COVID-19 patients?

Background: During the ongoing global SARS-CoV-2 pandemic, there is a high demand for quick and reliable methods for early identification of infected patients. Due to its widespread availability, chest-CT is commonly used to detect early pulmonary manifestations and for follow-ups.

Purpose: This study aims to analyze image quality and reproducibility of readings of scans using low-dose chest CT protocols in patients suspected of SARS-CoV-2 infection.

Evaluation of potential tissue heating during percutaneous drill-assisted bone sampling in an in vivo porcine study.

Background: Minimally invasive, battery-powered drilling systems have become the preferred tool for obtaining representative samples from bone lesions. However, the heat generated during battery-powered bone drilling for bone biopsies has not yet been sufficiently investigated. Thermal necrosis can occur if the bone temperature exceeds a critical threshold for a certain period of time.

Computed Tomography Imaging in Simulated Ongoing Cardiopulmonary Resuscitation: No Need to Switch Off the Chest Compression Device during Image Acquisition.

Computed tomography (CT) represents the current standard for imaging of patients with acute life-threatening diseases. As some patients present with circulatory arrest, they require cardiopulmonary resuscitation. Automated chest compression devices are used to continue resuscitation during CT examinations, but tend to cause motion artifacts degrading diagnostic evaluation of the chest.

Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance.

Background: Radiographs of the sacroiliac joints are commonly used for the diagnosis and classification of axial spondyloarthritis. The aim of this study was to develop and validate an artificial neural network for the detection of definite radiographic sacroiliitis as a manifestation of axial spondyloarthritis (axSpA).

Methods: Conventional radiographs of the sacroiliac joints obtained in two independent studies of patients with axSpA were used.

Deep-Learning-Based Diagnosis of Bedside Chest X-ray in Intensive Care and Emergency Medicine.

Objectives: Validation of deep learning models should separately consider bedside chest radiographs (CXRs) as they are the most challenging to interpret, while at the same time the resulting diagnoses are important for managing critically ill patients. Therefore, we aimed to develop and evaluate deep learning models for the identification of clinically relevant abnormalities in bedside CXRs, using reference standards established by computed tomography (CT) and multiple radiologists.

Materials And Methods: In this retrospective study, a dataset consisting of 18,361 bedside CXRs of patients treated at a level 1 medical center between January 2009 and March 2019 was used.

Diagnosing axial spondyloarthritis: estimation of the disease probability in patients with a priori different likelihoods of the diagnosis.

Objective: To evaluate the diagnostic value of SpA parameters and their combination for the diagnosis of axial SpA in patients with an a priori different probability of the diagnosis.

Methods: A total of 361 patients with chronic back pain and suspicion of axial SpA (181 referred by primary care physicians or orthopaedists, 180 recruited via an online screening tool) received a structured rheumatologic examination, which resulted into a diagnosis or exclusion of axial SpA. The prevalence of axial SpA indicating the pre-test probability was 40% in the physician-referred subgroup and 20% in the online screening subgroup.

Improving CT accuracy in the diagnosis of COVID-19 in a hospital setting.

Objective: This study aimed to improve the accuracy of CT for detection of COVID-19-associated pneumonia and to identify patient subgroups who might benefit most from CT imaging.

Methods: A total of 269 patients who underwent CT for suspected COVID-19 were included in this retrospective analysis. COVID-19 was confirmed by reverse-transcription-polymerase-chain-reaction.

Is lung density associated with severity of COVID-19?

Purpose: Emphysema and chronic obstructive lung disease were previously identified as major risk factors for severe disease progression in COVID-19. Computed tomography (CT)-based lung-density analysis offers a fast, reliable, and quantitative assessment of lung density. Therefore, we aimed to assess the benefit of CT-based lung density measurements to predict possible severe disease progression in COVID-19.

Comparing different deep learning architectures for classification of chest radiographs.

Chest radiographs are among the most frequently acquired images in radiology and are often the subject of computer vision research. However, most of the models used to classify chest radiographs are derived from openly available deep neural networks, trained on large image datasets. These datasets differ from chest radiographs in that they are mostly color images and have substantially more labels.

Highly accurate classification of chest radiographic reports using a deep learning natural language model pre-trained on 3.8 million text reports.

Motivation: The development of deep, bidirectional transformers such as Bidirectional Encoder Representations from Transformers (BERT) led to an outperformance of several Natural Language Processing (NLP) benchmarks. Especially in radiology, large amounts of free-text data are generated in daily clinical workflow. These report texts could be of particular use for the generation of labels in machine learning, especially for image classification.

The role of visceral adiposity in the severity of COVID-19: Highlights from a unicenter cross-sectional pilot study in Germany.

Background And Aims: Overall obesity has recently been established as an independent risk factor for critical illness in patients with coronavirus disease 2019 (COVID-19). The role of fat distribution and especially that of visceral fat, which is often associated with metabolic syndrome, remains unclear. Therefore, this study aims at investigating the association between fat distribution and COVID-19 severity.

Periportal fields cause stronger cooling effects than veins in hepatic microwave ablation: an in vivo porcine study.

Background: Vascular cooling effects are a well-known source for tumor recurrence in thermal in situ ablation techniques for hepatic malignancies. Microwave ablation (MWA) is an ablation technique to be considered in the treatment of malignant liver tumors. The impact of vascular cooling in MWA is still controversial.

Immediate post-interventional contrast-enhanced computed tomography overestimates hepatic microwave ablation - an animal study.

Contrast-enhanced computed tomography (CECT) is used to monitor technical success immediately after hepatic microwave ablation (MWA). However, it remains unclear, if CECT shows the exact extend of the thermal destruction zone, or if tissue changes such as peri-lesionary edema are depicted as well. The objective of this study was to correlate immediate post-interventional CECT with histological and macroscopic findings in hepatic MWA in porcine liver .

CT-based quantification of short-term tissue shrinkage following hepatic microwave ablation in an in vivo porcine liver model.

Background: Microwave ablation (MWA) is a minimally invasive treatment option for solid tumors and belongs to the local ablative therapeutic techniques, based on thermal tissue coagulation. So far there are mainly ex vivo studies that describe tissue shrinkage during MWA.

Purpose: To characterize short-term volume changes of the ablated zone following hepatic MWA in an in vivo porcine liver model using contrast-enhanced computer tomography (CECT).

Effect of Different Iodine Concentrations on Patient-Reported Discomfort in Contrast-Enhanced Computed Tomography: A Prospective Comparative Trial.

Purpose:  Reducing contrast media injection speed while maintaining iodine flux is a promising workaround to lower or avoid contrast media-related discomfort during CT examinations. This approach demands contrast media with a higher concentration to guarantee excellent image quality. It remains unclear whether these concentration changes affect the patient's experience.