Large Language Models as Tools to Generate Radiology Board-Style Multiple-Choice Questions.

RATIONALE AND OBJECTIVES: To determine the potential of large language models (LLMs) to be used as tools by radiology educators to create radiology board-style multiple choice questions (MCQs), answers, and rationales. METHODS: Two LLMs (Llama 2 and GPT-4) were used to develop 104 MCQs based on the American Board of Radiology exam blueprint. Two board-certified radiologists assessed each MCQ using a 10-point Likert scale across five criteria-clarity, relevance, suitability for a board exam based on level of difficulty, quality of distractors, and adequacy of rationale. For comparison, MCQs from prior American College of Radiology (ACR) Diagnostic Radiology In-Training (DXIT) exams were also assessed using these criteria, with radiologists blinded to the question source. RESULTS: Mean scores (±standard deviation) for clarity, relevance, suitability, quality of distractors, and adequacy of rationale were 8.7 (±1.4), 9.2 (±1.3), 9.0 (±1.2), 8.4 (±1.9), and 7.2 (±2.2), respectively, for Llama 2; 9.9 (±0.4), 9.9 (±0.5), 9.9 (±0.4), 9.8 (±0.5), and 9.9 (±0.3), respectively, for GPT-4; and 9.9 (±0.3), 9.9 (±0.2), 9.9 (±0.2), 9.9 (±0.4), and 9.8 (±0.6), respectively, for ACR DXIT items (p < 0.001 for Llama 2 vs. ACR DXIT across all criteria; no statistically significant difference for GPT-4 vs. ACR DXIT). The accuracy of model-generated answers was 69% for Llama 2 and 100% for GPT-4. CONCLUSION: A state-of-the art LLM such as GPT-4 may be used to develop radiology board-style MCQs and rationales to enhance exam preparation materials and expand exam banks, and may allow radiology educators to further use MCQs as teaching and learning tools.

Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm.

OBJECTIVES: To develop and validate an open-source artificial intelligence (AI) algorithm to accurately detect contrast phases in abdominal CT scans. MATERIALS AND METHODS: Retrospective study aimed to develop an AI algorithm trained on 739 abdominal CT exams from 2016 to 2021, from 200 unique patients, covering 1545 axial series. We performed segmentation of five key anatomic structures-aorta, portal vein, inferior vena cava, renal parenchyma, and renal pelvis-using TotalSegmentator, a deep learning-based tool for multi-organ segmentation, and a rule-based approach to extract the renal pelvis. Radiomics features were extracted from the anatomical structures for use in a gradient-boosting classifier to identify four contrast phases: non-contrast, arterial, venous, and delayed. Internal and external validation was performed using the F1 score and other classification metrics, on the external dataset "VinDr-Multiphase CT". RESULTS: The training dataset consisted of 172 patients (mean age, 70 years ± 8, 22% women), and the internal test set included 28 patients (mean age, 68 years ± 8, 14% women). In internal validation, the classifier achieved an accuracy of 92.3%, with an average F1 score of 90.7%. During external validation, the algorithm maintained an accuracy of 90.1%, with an average F1 score of 82.6%. Shapley feature attribution analysis indicated that renal and vascular radiodensity values were the most important for phase classification. CONCLUSION: An open-source and interpretable AI algorithm accurately detects contrast phases in abdominal CT scans, with high accuracy and F1 scores in internal and external validation, confirming its generalization capability. CLINICAL RELEVANCE STATEMENT: Contrast phase detection in abdominal CT scans is a critical step for downstream AI applications, deploying algorithms in the clinical setting, and for quantifying imaging biomarkers, ultimately allowing for better diagnostics and increased access to diagnostic imaging. KEY POINTS: Digital Imaging and Communications in Medicine labels are inaccurate for determining the abdominal CT scan phase. AI provides great help in accurately discriminating the contrast phase. Accurate contrast phase determination aids downstream AI applications and biomarker quantification.

MSK-TIM: A Telerobotic Ultrasound System for Assessing the Musculoskeletal System.

The aim of this paper is to investigate technological advancements made to a robotic tele-ultrasound system for musculoskeletal imaging, the MSK-TIM (Musculoskeletal Telerobotic Imaging Machine). The hardware was enhanced with a force feedback sensor and a new controller was introduced. Software improvements were developed which allowed the operator to access ultrasound functions such as focus, depth, gain, zoom, color, and power Doppler controls. The device was equipped with Wi-Fi network capability which allowed the master and slave stations to be positioned in different locations. A trial assessing the system to scan the wrist was conducted with twelve participants, for a total of twenty-four arms. Both the participants and radiologist reported their experience. The images obtained were determined to be of satisfactory quality for diagnosis. The system improvements resulted in a better user and patient experience for the radiologist and participants. Latency with the VPN configuration was similar to the WLAN in our experiments. This research explores several technologies in medical telerobotics and provides insight into how they should be used in future. This study provides evidence to support larger-scale trials of the MSK-TIM for musculoskeletal imaging.

The STRAT-PARK cohort: A personalized initiative to stratify Parkinson's disease.

The STRAT-PARK initiative aims to provide a platform for stratifying Parkinson's disease (PD) into biological subtypes, using a bottom-up, multidisciplinary biomarker-based and data-driven approach. PD is a heterogeneous entity, exhibiting high interindividual clinicopathological variability. This diversity suggests that PD may encompass multiple distinct biological entities, each driven by different molecular mechanisms. Molecular stratification and identification of disease subtypes is therefore a key priority for understanding and treating PD. STRAT-PARK is a multi-center longitudinal cohort aiming to recruit a total of 2000 individuals with PD and neurologically healthy controls from Norway and Canada, for the purpose of identifying molecular disease subtypes. Clinical assessment is performed annually, whereas biosampling, imaging, and digital and neurophysiological phenotyping occur every second year. The unique feature of STRAT-PARK is the diversity of collected biological material, including muscle biopsies and platelets, tissues particularly useful for mitochondrial biomarker research. Recruitment rate is ∼150 participants per year. By March 2023, 252 participants were included, comprising 204 cases and 48 controls. STRAT-PARK is a powerful stratification initiative anticipated to become a global research resource, contributing to personalized care in PD.

Effects of speech rate modifications on phonatory acoustic outcomes in Parkinson's disease.

Speech rate reduction is a global speech therapy approach for speech deficits in Parkinson's disease (PD) that has the potential to result in changes across multiple speech subsystems. While the overall goal of rate reduction is usually improvements in speech intelligibility, not all people with PD benefit from this approach. Speech rate is often targeted as a means of improving articulatory precision, though less is known about rate-induced changes in other speech subsystems that could help or hinder communication. The purpose of this study was to quantify phonatory changes associated with speech rate modification across a broad range of speech rates from very slow to very fast in talkers with and without PD. Four speaker groups participated: younger and older healthy controls, and people with PD with and without deep brain stimulation of the subthalamic nucleus (STN-DBS). Talkers read aloud standardized sentences at 7 speech rates elicited using magnitude production: habitual, three slower rates, and three faster rates. Acoustic measures of speech intensity, cepstral peak prominence, and fundamental frequency were measured as a function of speech rate and group. Overall, slower rates of speech were associated with differential effects on phonation across the four groups. While all talkers spoke at a lower pitch in slow speech, younger talkers showed increases in speech intensity and cepstral peak prominence, while talkers with PD and STN-DBS showed the reverse pattern. Talkers with PD without STN-DBS and older healthy controls behaved in between these two extremes. At faster rates, all groups uniformly demonstrated increases in cepstral peak prominence. While speech rate reductions are intended to promote positive changes in articulation to compensate for speech deficits in dysarthria, the present results highlight that undesirable changes may be invoked across other subsystems, such as at the laryngeal level. In particular, talkers with STN-DBS, who often demonstrate speech deterioration following DBS surgery, demonstrated more phonatory detriments at slowed speech rates. Findings have implications for speech rate candidacy considerations and speech motor control processes in PD.

CAR and CSTR Cardiac Computed Tomography (CT) Practice Guidelines: Part 2-Non-Coronary Imaging.

The cardiac computed tomography (CT) practice guidelines provide an updated review of the technological improvements since the publication of the first Canadian Association of Radiologists (CAR) cardiac CT practice guidelines in 2009. An overview of the current evidence supporting the use of cardiac CT in the most common clinical scenarios, standards of practice to optimize patient preparation and safety as well as image quality are described. Coronary CT angiography (CCTA) is the focus of Part I. In Part II, an overview of cardiac CT for non-coronary indications that include valvular and pericardial imaging, tumour and mass evaluation, pulmonary vein imaging, and imaging of congenital heart disease for diagnosis and treatment monitoring are discussed. The guidelines are intended to be relevant for community hospitals and large academic centres with established cardiac CT imaging programs.

CAR and CSTR Cardiac Computed Tomography (CT) Practice Guidelines: Part 1 Coronary CT Angiography (CCTA).

Imaging the heart is one of the most technically challenging applications of Computed Tomography (CT) due to the presence of cardiac motion limiting optimal visualization of small structures such as the coronary arteries. Electrocardiographic gating during CT data acquisition facilitates motion free imaging of the coronary arteries. Since publishing the first version of the Canadian Association of Radiologists (CAR) cardiac CT guidelines, many technological advances in CT hardware and software have emerged necessitating an update. The goal of these cardiac CT practice guidelines is to present an overview of the current evidence supporting the use of cardiac CT in various clinical scenarios and to outline standards of practice for patient safety and quality of care when establishing a cardiac CT program in Canada.

SmartFSCV: An Artificial Intelligence Enabled Miniaturised FSCV Device Targeting Serotonin.

Goal: Dynamically monitoring serotonin in real-time within target brain regions would significantly improve the diagnostic and therapeutic approaches to a variety of neurological and psychiatric disorders. Current systems for measuring serotonin lack immediacy and portability and are bulky and expensive. Methods: We present a new miniaturised device, named SmartFSCV, designed to monitor dynamic changes of serotonin using fast-scan cyclic voltammetry (FSCV). This device outputs a precision voltage potential between -3 to +3 V, and measures current between -1.5 to +1.5 µA with nano-ampere accuracy. The device can output modifiable arbitrary waveforms for various measurements and uses an N-shaped waveform at a scan-rate of 1000 V/s for sensing serotonin. Results: Four experiments were conducted to validate SmartFSCV: static bench test, dynamic serotonin test and two artificial intelligence (AI) algorithm tests. Conclusions: These tests confirmed the ability of SmartFSCV to accurately sense and make informed decisions about the presence of serotonin using AI.

COVID-19 Severity and Mortality in Veterans with Chronic Lung Disease.

Rationale: Chronic lung diseases (CLDs) have been variably associated with a risk for more severe manifestations and death with coronavirus disease (COVID-19). Objectives: To determine the risk overall and by type of CLD for severity of COVID-19 outcomes in a U.S. national cohort. Methods: Using data from the Veterans Health Administration, we determined the risk associated with CLDs, including chronic obstructive pulmonary disease (COPD) (mild or severe), asthma (mild, active, or severe), idiopathic pulmonary fibrosis (IPF), sarcoidosis, and other interstitial lung diseases (ILDs) for outcomes among veterans with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-positive tests between March 1, 2020 and April 30, 2021. We used multinomial regression to estimate risk of four mutually exclusive COVID-19 outcomes within 30 days: outpatient management, hospitalization, hospitalization with indicators of critical illness, or death. We calculated the overall proportion with each outcome, the absolute risk difference, and risk ratios for each outcome between those with and without CLD. We also describe clinical and laboratory abnormalities by CLD in those hospitalized. Results: We included 208,283 veterans with COVID-19; 35,587 (17%) had CLD. Compared with no CLD, veterans with CLD were older and had more comorbidities. Hospitalized veterans with CLD were more likely to have low temperature, mean arterial pressure, oxygen saturation, and leukopenia and thrombocytopenia and were more likely to receive oxygen, mechanical ventilation, and vasopressors. Veterans with CLD were significantly less likely to have mild COVID-19 (-4.5%; adjusted risk ratio [aRR], 0.94; 95% confidence interval [CI], 0.94-0.95), and more likely to have a moderate (+2.5%; aRR, 1.21; 95% CI, 1.18-1.24), critical (+1.4%; aRR, 1.38; 95% CI, 1.32-1.45), or fatal (+0.7%; aRR, 1.15; 95% CI, 1.10-1.20) outcome. IPF was most strongly associated with COVID-19 severity, especially mortality (+3.2%; aRR, 1.69; 95% CI, 1.46-1.96), followed by other ILDs and COPD, whereas asthma was less likely to be associated with severity of COVID-19. In veterans younger than age 65 years, worse COVID-19 outcomes were generally more likely with IPF, sarcoidosis, and other ILDs. Conclusions: Veterans who had CLD, particularly IPF, other ILDs, and COPD, had an increased probability of more severe 30-day outcomes with COVID-19. These results provide insight into the absolute and relative risk of different CLDs with severity of COVID-19 outcomes and can help inform considerations of healthcare utilization and prognosis. Observational clinical epidemiology study registered with www.clinicaltrials.gov (NCT04628039).

A self-stiffening compliant intracortical microprobe.

Utilising a flexible intracortical microprobe to record/stimulate neurons minimises the incompatibility between the implanted microprobe and the brain, reducing tissue damage due to the brain micromotion. Applying bio-dissolvable coating materials temporarily makes a flexible microprobe stiff to tolerate the penetration force during insertion. However, the inability to adjust the dissolving time after the microprobe contact with the cerebrospinal fluid may lead to inaccuracy in the microprobe positioning. Furthermore, since the dissolving process is irreversible, any subsequent positioning error cannot be corrected by re-stiffening the microprobe. The purpose of this study is to propose an intracortical microprobe that incorporates two compressible structures to make the microprobe both adaptive to the brain during operation and stiff during insertion. Applying a compressive force by an inserter compresses the two compressible structures completely, resulting in increasing the equivalent elastic modulus. Thus, instant switching between stiff and soft modes can be accomplished as many times as necessary to ensure high-accuracy positioning while causing minimal tissue damage. The equivalent elastic modulus of the microprobe during operation is ≈ 23 kPa, which is ≈ 42% less than the existing counterpart, resulting in ≈ 46% less maximum strain generated on the surrounding tissue under brain longitudinal motion. The self-stiffening microprobe and surrounding neural tissue are simulated during insertion and operation to confirm the efficiency of the design. Two-photon polymerisation technology is utilised to 3D print the proposed microprobe, which is experimentally validated and inserted into a lamb's brain without buckling.

RadioGraphics Update: The 10 Pillars of Lung Cancer Screening-Rationale and Logistics of a Lung Cancer Screening Program.

Editor's Note.-RadioGraphics Update articles supplement or update information found in full-length articles previously published in RadioGraphics. These updates, written by at least one author of the previous article, provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes.

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.

Incorporating Exercise Training into Cardio-Oncology Care: Current Evidence and Opportunities: JACC: CardioOncology State-of-the-Art Review.

Cancer treatment-induced cardiotoxicities are an ongoing concern throughout the cancer care continuum from treatment initiation to survivorship. Several "standard-of-care" primary, secondary, and tertiary prevention strategies are available to prevent the development or further progression of cancer treatment-induced cardiotoxicities and their risk factors. Despite exercise's established benefits on the cardiovascular system, it has not been widely adopted as a nonpharmacologic cardioprotective strategy within cardio-oncology care. In this state-of-the-art review, the authors discuss cancer treatment-induced cardiotoxicities, review the existing evidence supporting the role of exercise in preventing and managing these sequelae in at-risk and affected individuals living after cancer diagnoses, and propose considerations for implementing exercise-based services in cardio-oncology practice.

Artificial Intelligence and Machine Learning in Lung Cancer Screening.

Recent advances in artificial intelligence and machine learning (AI/ML) hold substantial promise to address some of the current challenges in lung cancer screening and improve health equity. This article reviews the status and future directions of AI/ML tools in the lung cancer screening workflow, focusing on determining screening eligibility, radiation dose reduction and image denoising for low-dose chest computed tomography (CT), lung nodule detection, lung nodule classification, and determining optimal screening intervals. AI/ML tools can assess for chronic diseases on CT, which creates opportunities to improve population health through opportunistic screening.

Physical activity and physical fitness assessments in adolescents and young adults diagnosed with cancer: a scoping review.

PURPOSE: This scoping review describes the assessment methodologies for physical activity (PA) and physical fitness assessments used in studies focusing on adolescents and young adults (AYAs) diagnosed with cancer. METHODS: A search of the literature was conducted in PubMed, CINAHL, Web of Science, and Cochrane Library following the PRISMA-ScR statement. A total of 34 studies were included in this review. RESULTS: PA was primarily assessed via self-reported questionnaires (30/34) either completed in-person (n = 17) or online (n = 13) at different time points and different stages along the cancer trajectory (i.e., from diagnosis onward). A total of 9 studies conducted a physical fitness assessment. CONCLUSIONS: PA and physical fitness measurements are key when trying to describe outcomes, assess for associations, track changes, measure intervention adherence, and test intervention efficacy and effectiveness. Considerable heterogeneity across studies was reported limiting the generation of formal recommendations or guidance for researchers, healthcare providers, and policy makers.

Design and methods of a randomized trial testing "Advancing care for COPD in people living with HIV by implementing evidence-based management through proactive E-consults (ACHIEVE)".

Chronic obstructive pulmonary disease (COPD) is one of the most common comorbid diseases among aging people with HIV (PWH) and is often mismanaged. To address this gap, we are conducting the study, "Advancing care for COPD in people living with HIV by Implementing Evidence-based management through proactive E-consults (ACHIEVE)." This intervention optimizes COPD management by promoting effective, evidence-based care and de-implementing inappropriate therapies for COPD in PWH receiving care at Veteran Affairs (VA) medical centers. Study pulmonologists are proactively supporting ID providers managing a population of PWH who have COPD, offering real-time evidence-based recommendations tailored to each patient. We are leveraging VA clinical and informatics infrastructures to communicate recommendations between the study team and clinical providers through the electronic health record (EHR) as an E-consult. If effective, ACHIEVE could serve as a model of effective, efficient COPD management among PWH receiving care in VA. This paper outlines the rationale and methodology of the ACHIEVE trial, one of a series of studies funded by the National Heart, Lung, and Blood Institute (NHLBI) within the ImPlementation REsearCh to DEvelop Interventions for People Living with HIV (PRECluDE) consortium to study chronic disease comorbidities in HIV populations.

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..

Variation within major internal repeats of KSHV in vivo.

Kaposi's sarcoma-associated herpesvirus (KSHV) is the etiologic agent of Kaposi's sarcoma (KS), yet the viral genetic factors that lead to the development of KS in KSHV-infected individuals have not been fully elucidated. Nearly, all previous analyses of KSHV genomic evolution and diversity have excluded the three major internal repeat regions: the two origins of lytic replication, internal repeats 1 and 2 (IR1 and IR2), and the latency-associated nuclear antigen (LANA) repeat domain (LANAr). These regions encode protein domains that are essential to the KSHV infection cycle but have been rarely sequenced due to their extended repetitive nature and high guanine and cytosine (GC) content. The limited data available suggest that their sequences and repeat lengths are more heterogeneous across individuals than in the remainder of the KSHV genome. To assess their diversity, the full-length IR1, IR2, and LANAr sequences, tagged with unique molecular identifiers (UMIs), were obtained by Pacific Biosciences' single-molecule real-time sequencing (SMRT-UMI) from twenty-four tumors and six matching oral swabs from sixteen adults in Uganda with advanced KS. Intra-host single-nucleotide variation involved an average of 0.16 per cent of base positions in the repeat regions compared to a nearly identical average of 0.17 per cent of base positions in the remainder of the genome. Tandem repeat unit (TRU) counts varied by only one from the intra-host consensus in a majority of individuals. Including the TRU indels, the average intra-host pairwise identity was 98.3 per cent for IR1, 99.6 per cent for IR2 and 98.9 per cent for LANAr. More individuals had mismatches and variable TRU counts in IR1 (twelve/sixteen) than in IR2 (two/sixteen). There were no open reading frames in the Kaposin coding sequence inside IR2 in at least fifty-five of ninety-six sequences. In summary, the KSHV major internal repeats, like the rest of the genome in individuals with KS, have low diversity. IR1 was the most variable among the repeats, and no intact Kaposin reading frames were present in IR2 of the majority of genomes sampled.