1,186 research outputs found
Federated learning for medical imaging radiology
Federated learning (FL) is gaining wide acceptance across the medical AI domains. FL promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability of machine learning models across multiple institutions. However, the research on FL for medical imaging AI is still in its early stages. This paper presents a review of recent research to outline the difference between state-of-the-art [SOTA] (published literature) and state-of-the-practice [SOTP] (applied research in realistic clinical environments). Furthermore, the review outlines the future research directions considering various factors such as data, learning models, system design, governance, and human-in-loop to translate the SOTA into SOTP and effectively collaborate across multiple institutions
AI chatbots not yet ready for clinical use
As large language models (LLMs) expand and become more advanced, so do the natural language processing capabilities of conversational AI, or “chatbots”. OpenAI's recent release, ChatGPT, uses a transformer-based model to enable human-like text generation and question-answering on general domain knowledge, while a healthcare-specific Large Language Model (LLM) such as GatorTron has focused on the real-world healthcare domain knowledge. As LLMs advance to achieve near human-level performances on medical question and answering benchmarks, it is probable that Conversational AI will soon be developed for use in healthcare. In this article we discuss the potential and compare the performance of two different approaches to generative pretrained transformers—ChatGPT, the most widely used general conversational LLM, and Foresight, a GPT (generative pretrained transformer) based model focused on modelling patients and disorders. The comparison is conducted on the task of forecasting relevant diagnoses based on clinical vignettes. We also discuss important considerations and limitations of transformer-based chatbots for clinical use
SIC~POVMs and Clifford groups in prime dimensions
We show that in prime dimensions not equal to three, each group covariant
symmetric informationally complete positive operator valued measure (SIC~POVM)
is covariant with respect to a unique Heisenberg--Weyl (HW) group. Moreover,
the symmetry group of the SIC~POVM is a subgroup of the Clifford group. Hence,
two SIC~POVMs covariant with respect to the HW group are unitarily or
antiunitarily equivalent if and only if they are on the same orbit of the
extended Clifford group. In dimension three, each group covariant SIC~POVM may
be covariant with respect to three or nine HW groups, and the symmetry group of
the SIC~POVM is a subgroup of at least one of the Clifford groups of these HW
groups respectively. There may exist two or three orbits of equivalent
SIC~POVMs for each group covariant SIC~POVM, depending on the order of its
symmetry group. We then establish a complete equivalence relation among group
covariant SIC~POVMs in dimension three, and classify inequivalent ones
according to the geometric phases associated with fiducial vectors. Finally, we
uncover additional SIC~POVMs by regrouping of the fiducial vectors from
different SIC~POVMs which may or may not be on the same orbit of the extended
Clifford group.Comment: 30 pages, 1 figure, section 4 revised and extended, published in J.
Phys. A: Math. Theor. 43, 305305 (2010
Enter evaluation of mitral inflow velocity profile: optimal through plane location for mitral inflow assessment with cardiac magnetic resonance
Diastology is usually assessed using transthoracic echocardiography (TTE).
Velocity‐encoded phase‐contrast imaging permits evaluation with cardiac magnetic resonance
(CMR). Heterogeneous contour locations have been used to measure mitral (MV) inflow
velocities and the optimal contour location is uncertain. We evaluated CMR MV inflow
velocities against TTE to identify the optimal location
Two-dimensional Dirac fermions in a topological insulator: transport in the quantum limit
Pulsed magnetic fields of up to 55T are used to investigate the transport
properties of the topological insulator Bi_2Se_3 in the extreme quantum limit.
For samples with a bulk carrier density of n = 2.9\times10^16cm^-3, the lowest
Landau level of the bulk 3D Fermi surface is reached by a field of 4T. For
fields well beyond this limit, Shubnikov-de Haas oscillations arising from
quantization of the 2D surface state are observed, with the \nu =1 Landau level
attained by a field of 35T. These measurements reveal the presence of
additional oscillations which occur at fields corresponding to simple rational
fractions of the integer Landau indices.Comment: 5 pages, 4 figure
Latent Transformer Models for out-of-distribution detection
Any clinically-deployed image-processing pipeline must be robust to the full range of inputs it may be presented with. One popular approach to this challenge is to develop predictive models that can provide a measure of their uncertainty. Another approach is to use generative modelling to quantify the likelihood of inputs. Inputs with a low enough likelihood are deemed to be out-of-distribution and are not presented to the downstream predictive model. In this work, we evaluate several approaches to segmentation with uncertainty for the task of segmenting bleeds in 3D CT of the head. We show that these models can fail catastrophically when operating in the far out-of-distribution domain, often providing predictions that are both highly confident and wrong. We propose to instead perform out-of-distribution detection using the Latent Transformer Model: a VQ-GAN is used to provide a highly compressed latent representation of the input volume, and a transformer is then used to estimate the likelihood of this compressed representation of the input. We demonstrate this approach can identify images that are both far- and near- out-of-distribution, as well as provide spatial maps that highlight the regions considered to be out-of-distribution. Furthermore, we find a strong relationship between an image's likelihood and the quality of a model's segmentation on it, demonstrating that this approach is viable for filtering out unsuitable images
A new approach to assessing the health benefit from obesity interventions in children and adolescents: the assessing cost-effectiveness in obesity project
OBJECTIVE: To report on a new modelling approach developed for the assessing cost-effectiveness in obesity (ACE-Obesity) project and the likely population health benefit and strength of evidence for 13 potential obesity prevention interventions in children and adolescents in Australia. METHODS: We used the best available evidence, including evidence from non-traditional epidemiological study designs, to determine the health benefits as body mass index (BMI) units saved and disability-adjusted life years (DALYs) saved. We developed new methods to model the impact of behaviours on BMI post-intervention where this was not measured and the impacts on DALYs over the child\u27s lifetime (on the assumption that changes in BMI were maintained into adulthood). A working group of stakeholders provided input into decisions on the selection of interventions, the assumptions for modelling and the strength of the evidence. RESULTS: The likely health benefit varied considerably, as did the strength of the evidence from which that health benefit was calculated. The greatest health benefit is likely to be achieved by the \u27Reduction of TV advertising of high fat and/or high sugar foods and drinks to children\u27, \u27Laparoscopic adjustable gastric banding\u27 and the \u27multi-faceted school-based programme with an active physical education component\u27 interventions. CONCLUSIONS: The use of consistent methods and common health outcome measures enables valid comparison of the potential impact of interventions, but comparisons must take into account the strength of the evidence used. Other considerations, including cost-effectiveness and acceptability to stakeholders, will be presented in future ACE-Obesity papers. Information gaps identified include the need for new and more effective initiatives for the prevention of overweight and obesity and for better evaluations of public health interventions
Genome-wide association study identifies a variant in HDAC9 associated with large vessel ischemic stroke
Genetic factors have been implicated in stroke risk but few replicated associations have been reported. We conducted a genome-wide association study (GWAS) in ischemic stroke and its subtypes in 3,548 cases and 5,972 controls, all of European ancestry. Replication of potential
signals was performed in 5,859 cases and 6,281 controls. We replicated reported associations between variants close to PITX2 and ZFHX3 with cardioembolic stroke, and a 9p21 locus with large vessel stroke. We identified a novel association for a SNP within the histone deacetylase 9(HDAC9) gene on chromosome 7p21.1 which was associated with large vessel stroke including additional replication in a further 735 cases and 28583 controls (rs11984041, combined P =
1.87×10−11, OR=1.42 (95% CI) 1.28-1.57). All four loci exhibit evidence for heterogeneity of effect across the stroke subtypes, with some, and possibly all, affecting risk for only one subtype. This suggests differing genetic architectures for different stroke subtypes
De novo mutations in SMCHD1 cause Bosma arhinia microphthalmia syndrome and abrogate nasal development
Bosma arhinia microphthalmia syndrome (BAMS) is an extremely rare and striking condition characterized by complete absence of the nose with or without ocular defects. We report here that missense mutations in the epigenetic regulator SMCHD1 mapping to the extended ATPase domain of the encoded protein cause BAMS in all 14 cases studied. All mutations were de novo where parental DNA was available. Biochemical tests and in vivo assays in Xenopus laevis embryos suggest that these mutations may behave as gain-of-function alleles. This finding is in contrast to the loss-of-function mutations in SMCHD1 that have been associated with facioscapulohumeral muscular dystrophy (FSHD) type 2. Our results establish SMCHD1 as a key player in nasal development and provide biochemical insight into its enzymatic function that may be exploited for development of therapeutics for FSHD
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