Anatomy and Physiology of Artificial Intelligence in PET Imaging

The influence of artificial intelligence (AI) within the field of nuclear medicine has been rapidly growing. Many researchers and clinicians are seeking to apply AI within PET, and clinicians will soon find themselves engaging with AI-based applications all along the chain of molecular imaging, from image reconstruction to enhanced reporting. This expanding presence of AI in PET imaging will result in greater demand for educational resources for those unfamiliar with AI. The objective of this article to is provide an illustrated guide to the core principles of modern AI, with specific focus on aspects that are most likely to be encountered in PET imaging. We describe convolutional neural networks, algorithm training, and explain the components of the commonly used U-Net for segmentation and image synthesis

ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax

Radiology narrative reports often describe characteristics of a patient's disease, including its location, size, and shape. Motivated by the recent success of multimodal learning, we hypothesized that this descriptive text could guide medical image analysis algorithms. We proposed a novel vision-language model, ConTEXTual Net, for the task of pneumothorax segmentation on chest radiographs. ConTEXTual Net utilizes language features extracted from corresponding free-form radiology reports using a pre-trained language model. Cross-attention modules are designed to combine the intermediate output of each vision encoder layer and the text embeddings generated by the language model. ConTEXTual Net was trained on the CANDID-PTX dataset consisting of 3,196 positive cases of pneumothorax with segmentation annotations from 6 different physicians as well as clinical radiology reports. Using cross-validation, ConTEXTual Net achieved a Dice score of 0.716$\pm$0.016, which was similar to the degree of inter-reader variability (0.712$\pm$0.044) computed on a subset of the data. It outperformed both vision-only models (ResNet50 U-Net: 0.677$\pm$0.015 and GLoRIA: 0.686$\pm$0.014) and a competing vision-language model (LAVT: 0.706$\pm$0.009). Ablation studies confirmed that it was the text information that led to the performance gains. Additionally, we show that certain augmentation methods degraded ConTEXTual Net's segmentation performance by breaking the image-text concordance. We also evaluated the effects of using different language models and activation functions in the cross-attention module, highlighting the efficacy of our chosen architectural design

Identification of an elaborate complex mediating postsynaptic inhibition

Inhibitory synapses dampen neuronal activity through postsynaptic hyperpolarization. The composition of the inhibitory postsynapse and the mechanistic basis of its regulation, however, remains poorly understood. We used an in vivo chemico-genetic proximity-labeling approach to discover inhibitory postsynaptic proteins. Quantitative mass spectrometry not only recapitulated known inhibitory postsynaptic proteins, but also revealed a large network of new proteins, many of which are either implicated in neurodevelopmental disorders or are of unknown function. CRISPR-depletion of one of these previously uncharacterized proteins, InSyn1, led to decreased postsynaptic inhibitory sites, reduced frequency of miniature inhibitory currents, and increased excitability in the hippocampus. Our findings uncover a rich and functionally diverse assemblage of previously unknown proteins that regulate postsynaptic inhibition and might contribute to developmental brain disorders

Issues and Challenges in Applications of Artificial Intelligence to Nuclear Medicine -- The Bethesda Report (AI Summit 2022)

The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community members and stakeholders from academia, healthcare, industry, patient representatives, and government (NIH, FDA), and considered various key themes to envision and facilitate a bright future for routine, trustworthy use of AI in nuclear medicine. In what follows, essential issues, challenges, controversies and findings emphasized in the meeting are summarized

Soil methane sink capacity response to a long-term wildfire chronosequence in Northern Sweden

Boreal forests occupy nearly one fifth of the terrestrial land surface and are recognised as globally important regulators of carbon (C) cycling and greenhouse gas emissions. Carbon sequestration processes in these forests include assimilation of CO2 into biomass and subsequently into soil organic matter, and soil microbial oxidation of methane (CH4). In this study we explored how ecosystem retrogression, which drives vegetation change, regulates the important process of soil CH4 oxidation in boreal forests. We measured soil CH4 oxidation processes on a group of 30 forested islands in northern Sweden differing greatly in fire history, and collectively representing a retrogressive chronosequence, spanning 5000 years. Across these islands the build-up of soil organic matter was observed to increase with time since fire disturbance, with a significant correlation between greater humus depth and increased net soil CH4 oxidation rates. We suggest that this increase in net CH4 oxidation rates, in the absence of disturbance, results as deeper humus stores accumulate and provide niches for methanotrophs to thrive. By using this gradient we have discovered important regulatory controls on the stability of soil CH4 oxidation processes that could not have not been explored through shorter-term experiments. Our findings indicate that in the absence of human interventions such as fire suppression, and with increased wildfire frequency, the globally important boreal CH4 sink could be diminished

Magnitude, precision, and realism of depth perception in stereoscopic vision

Our perception of depth is substantially enhanced by the fact that we have binocular vision. This provides us with more precise and accurate estimates of depth and an improved qualitative appreciation of the three-dimensional (3D) shapes and positions of objects. We assessed the link between these quantitative and qualitative aspects of 3D vision. Specifically, we wished to determine whether the realism of apparent depth from binocular cues is associated with the magnitude or precision of perceived depth and the degree of binocular fusion. We presented participants with stereograms containing randomly positioned circles and measured how the magnitude, realism, and precision of depth perception varied with the size of the disparities presented. We found that as the size of the disparity increased, the magnitude of perceived depth increased, while the precision with which observers could make depth discrimination judgments decreased. Beyond an initial increase, depth realism decreased with increasing disparity magnitude. This decrease occurred well below the disparity limit required to ensure comfortable viewing

From Data to Software to Science with the Rubin Observatory LSST

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collaboratively, and making them broadly available, can enable more inclusive and equitable collaboration on LSST science. To facilitate such opportunities, a community workshop entitled "From Data to Software to Science with the Rubin Observatory LSST" was organized by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) and partners, and held at the Flatiron Institute in New York, March 28-30th 2022. The workshop included over 50 in-person attendees invited from over 300 applications. It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection functions, (iv) frameworks for scalable time-series analyses, (v) services for image access and reprocessing at scale, (vi) object image access (cutouts) and analysis at scale, and (vii) scalable job execution systems. This white paper summarizes the discussions of this workshop. It considers the motivating science use cases, identified cross-cutting algorithms, software, and services, their high-level technical specifications, and the principles of inclusive collaborations needed to develop them. We provide it as a useful roadmap of needs, as well as to spur action and collaboration between groups and individuals looking to develop reusable software for early LSST science.Comment: White paper from "From Data to Software to Science with the Rubin Observatory LSST" worksho

Movement Patterns and Muscular Function Before and After Onset of Sports-Related Groin Pain: A Systematic Review with Meta-analysis

BACKGROUND: Sports-related groin pain (SRGP) is a common entity in rotational sports such as football, rugby and hockey, accounting for 12-18 % of injuries each year, with high recurrence rates and often prolonged time away from sport. OBJECTIVE: This systematic review synthesises movement and muscle function findings to better understand deficits and guide rehabilitation. STUDY SELECTION: Prospective and retrospective cross-sectional studies investigating muscle strength, flexibility, cross-sectional area, electromyographic activation onset and magnitude in patients with SRGP were included. SEARCH METHODS: Four databases (MEDLINE, Web of Knowledge, EBSCOhost and EMBASE) were searched in June 2014. Studies were critiqued using a modified version of the Downs and Black Quality Index, and a meta-analysis was performed. RESULTS: Seventeen studies (14 high quality, 3 low quality; 8 prospective and 9 retrospective) were identified. Prospective findings: moderate evidence indicated decreased hip abduction flexibility as a risk factor for SRGP. Limited or very limited evidence suggested that decreased hip adduction strength during isokinetic testing at ~119°/s was a risk factor for SRGP, but no associations were found at ~30°/s or ~210°/s, or with peak torque angle. Decreased hip abductor strength in angular velocity in ~30°/s but not in ~119°/s and ~210°/s was found as a risk factor for SRGP. No relationships were found with hip internal or external rotation range of movement, nor isokinetic knee extension strength. Decreased isokinetic knee flexion strength also was a potential risk factor for SRGP, at a speed ~60°/s. Retrospective findings: there was strong evidence of decreased hip adductor muscle strength during a squeeze test at 45°, and decreased total hip external rotation range of movement (sum of both legs) being associated with SRGP. There was strong evidence of no relationship to abductor muscle strength nor unilateral hip internal and external rotation range of movement. Moderate evidence suggested that increased abduction flexibility and no change in total hip internal rotation range of movement (sum of both legs) were retrospectively associated with SRGP. Limited or very limited evidence (significant findings only) indicated decreased hip adductor muscle strength during 0° and 30° squeeze tests and during an eccentric hip adduction test, but a decrease in the isometric adductors-to-abductors strength ratio at speed 120°/s; decreased abductors-to-adductors activation ratio in the early phase in the moving leg as well as in all three phases in the weight-bearing leg during standing hip flexion; and increased hip flexors strength during isokinetic and decrease in transversus abdominis muscle resting thickness associated with SRGP. CONCLUSIONS: There were a number of significant movement and muscle function associations observed in athletes both prior to and following the onset of SRGP. The strength of findings was hampered by the lack of consistent terminology and diagnostic criteria, with there being clear guides for future research. Nonetheless, these findings should be considered in rehabilitation and prevention planning

Right Iliac Fossa Pain Treatment (RIFT) Study: protocol for an international, multicentre, prospective observational study

Introduction Patients presenting with right iliac fossa (RIF) pain are a common challenge for acute general surgical services. Given the range of potential pathologies, RIF pain creates diagnostic uncertainty and there is subsequent variation in investigation and management. Appendicitis is a diagnosis which must be considered in all patients with RIF pain; however, over a fifth of patients undergoing appendicectomy, in the UK, have been proven to have a histologically normal appendix (negative appendicectomy). The primary aim of this study is to determine the contemporary negative appendicectomy rate. The study’s secondary aims are to determine the rate of laparoscopy for appendicitis and to validate the Appendicitis Inflammatory Response (AIR) and Alvarado prediction scores. Methods and analysis This multicentre, international prospective observational study will include all patients referred to surgical specialists with either RIF pain or suspected appendicitis. Consecutive patients presenting within 2-week long data collection periods will be included. Centres will be invited to participate in up to four data collection periods between February and August 2017. Data will be captured using a secure online data management system. A centre survey will profile local policy and service delivery for management of RIF pain. Ethics and dissemination Research ethics are not required for this study in the UK, as determined using the National Research Ethics Service decision tool. This study will be registered as a clinical audit in participating UK centres. National leads in countries outside the UK will oversee appropriate registration and study approval, which may include completing full ethical review. The study will be disseminated by trainee-led research collaboratives and through social media. Peer-reviewed publications will be published under corporate authorship including ‘RIFT Study Group’ and ‘West Midlands Research Collaborative’

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease