An improved 3D shape context registration method for non-rigid surface registration

3D shape context is a method to define matching points between similar shapes as a pre-processing step to non-rigid registration. The main limitation of the approach is point mismatching, which includes long geodesic distance mismatch and neighbors crossing mismatch. In this paper, we propose a topological structure verification method to correct the long geodesic distance mismatch and a correspondence field smoothing method to correct the neighbors crossing mismatch. A robust 3D shape context model is proposed and further combined with thin-plate spline model for non-rigid surface registration. The method was tested on phantoms and rat hind limb skeletons from micro CT images. The results from experiments on mouse hind limb skeletons indicate that the approach is robust

Cross-sectional and Longitudinal Analysis of the Relationship Between A beta Deposition, Cortical Thickness, and Memory in Cognitively Unimpaired Individuals and in Alzheimer Disease

IMPORTANCE beta-amyloid (A beta) deposition is one of the hallmarks of Alzheimer disease. A beta deposition accelerates gray matter atrophy at early stages of the disease even before objective cognitive impairment is manifested. Identification of at-risk individuals at the presymptomatic stage has become a major research interest because it will allow early therapeutic interventions before irreversible synaptic and neuronal loss occur. We aimed to further characterize the cross-sectional and longitudinal relationship between A beta deposition, gray matter atrophy, and cognitive impairment

Polyunsaturated fatty acids for the primary and secondary prevention of cardiovascular disease

Background: Evidence on the health effects of total polyunsaturated fatty acids (PUFA) is equivocal. Fish oils are rich in omega-3 PUFA and plant oils in omega-6 PUFA. Evidence suggests increasing PUFA-rich foods, supplements or supplemented foods can reduce serum cholesterol, but may increase body weight, so overall cardiovascular effects are unclear. Objectives: To assess effects of increasing PUFA intake on cardiovascular disease (CVD) and all-cause mortality in adults. Search method: We searched CENTRAL, MEDLINE and Embase to April 2017 and ClinicalTrials.com and World Health Organization International Clinical Trials Registry Platform to September 2016, without language restrictions. We checked trials included in relevant systematic reviews. Selection criteria: We included randomised controlled trials (RCTs) comparing higher with lower PUFA intakes in adults with or without CVD that assessed effects over ≥12 months. We included full-text, abstracts, trials registry entries and unpublished data. Outcomes were all-cause mortality, CVD mortality and events, risk factors (blood lipids, adiposity, blood pressure), and adverse events. We excluded trials where we could not separate effects of PUFA intake from other dietary, lifestyle or medication interventions. Data collection and analysis: Two authors independently screened titles/abstracts, assessed trials for inclusion, extracted data, and assessed risk of bias. We wrote to authors of included studies for further data. Meta-analyses used random-effects analysis, sensitivity analyses included fixed-effects and limiting to low summary risk of bias. We assessed GRADE quality of evidence. Main result: We included 49 RCTs randomising 24,272 participants, with duration of one to eight years. Twelve included trials were at low summary risk of bias, 33 recruited participants without cardiovascular disease. Baseline PUFA intake was unclear in most trials, but 3.9% to 8% of total energy intake where reported. Most trials gave supplemental capsules, but eight gave dietary advice, eight gave supplemental foods such as nuts or margarine, and three used a combination of methods to increase PUFA. Increasing PUFA intake probably has little or no effect on all-cause mortality (risk 3.4% vs 3.3% in primary prevention, 11.7% vs 11.5% in secondary prevention, risk ratio (RR) 0.98, 95% confidence interval (CI) 0.89 to 1.07, 24 trials in 19290 participants), but probably reduces risk of CVD events from 5.8% to 4.9% in primary prevention, 23.3% to 20.8% in secondary prevention (RR 0.89, 95% CI 0.79 to 1.01, 20 trials in 17,073 participants), both moderate quality evidence. Increasing PUFA may reduce risk of CHD events from 13.4% to 7.1% primary prevention, 14.3% to 13.7% secondary prevention (RR 0.87, 95% CI 0.72 to 1.06, 15 trials, 10,076 participants), CHD death (5.2% to 4.4% primary prevention, 6.8% to 6.1% secondary prevention, RR 0.91, 95% CI 0.78 to 1.06, 9 trials, 8810 participants) and may slightly reduce stroke risk (2.1% to 1.5% primary prevention, RR 0.91, 95% CI 0.58 to 1.44, 11 trials, 14,742 participants), but has little or no effect on cardiovascular mortality (RR 1.02, 95% CI 0.82 to 1.26, I2 31%, 16 trials, 15,107 participants) all low quality evidence. Effects of increasing PUFA on major adverse cardiac and cerebrovascular events and atrial fibrillation are unclear as evidence is of very low quality. Event outcomes were all downgraded for indirectness, as most events occurred in men in westernised countries. Increasing PUFA intake reduces total cholesterol (MD -0.12 mmol/L, 95% CI -0.23 to -0.02, I2 79%, 8072 participants, 26 trials) and probably decreases triglycerides (TG, MD -0.12 mmol/L, 95% CI -0.20 to -0.04, I2 50%, 3905 participants, 20 trials), but has little or no effect on HDL (MD -0.01 mmol/L, 95% CI -0.02 to 0.01, I2 0%, 4674 participants, 18 trials) and LDL (MD -0.01 mmol/L, 95% CI -0.09 to 0.06, I2 44%, 3362 participants, 15 trials). Increasing PUFA probably causes slight weight gain (MD 0.76 kg, 95% CI 0.34 to 1.19, I2 59%, 7100 participants, 12 trials). Effects of increasing PUFA on serious adverse events such as pulmonary embolism and bleeding are unclear as the evidence is of very low quality. Authors' conclusions: Increasing PUFA intake probably reduces risk of CVD events, may reduce risk of CHD events and CHD mortality,and may slightly reduce stroke risk, but has little or no effect on all-cause or CVD mortality. The mechanism may be via lipid reduction, but increasing PUFA probably slightly increases weight

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Reduction in saturated fat intake for cardiovascular disease

BACKGROUND: Reducing saturated fat reduces serum cholesterol, but effects on other intermediate outcomes may be less clear. Additionally, it is unclear whether the energy from saturated fats eliminated from the diet are more helpfully replaced by polyunsaturated fats, monounsaturated fats, carbohydrate or protein. OBJECTIVES: To assess the effect of reducing saturated fat intake and replacing it with carbohydrate (CHO), polyunsaturated (PUFA), monounsaturated fat (MUFA) and/or protein on mortality and cardiovascular morbidity, using all available randomised clinical trials. SEARCH METHODS: We updated our searches of the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE (Ovid) and Embase (Ovid) on 15 October 2019, and searched Clinicaltrials.gov and WHO International Clinical Trials Registry Platform (ICTRP) on 17 October 2019. SELECTION CRITERIA: Included trials fulfilled the following criteria: 1) randomised; 2) intention to reduce saturated fat intake OR intention to alter dietary fats and achieving a reduction in saturated fat; 3) compared with higher saturated fat intake or usual diet; 4) not multifactorial; 5) in adult humans with or without cardiovascular disease (but not acutely ill, pregnant or breastfeeding); 6) intervention duration at least 24 months; 7) mortality or cardiovascular morbidity data available. DATA COLLECTION AND ANALYSIS: Two review authors independently assessed inclusion, extracted study data and assessed risk of bias. We performed random-effects meta-analyses, meta-regression, subgrouping, sensitivity analyses, funnel plots and GRADE assessment. MAIN RESULTS: We included 15 randomised controlled trials (RCTs) (16 comparisons, ~59,000 participants), that used a variety of interventions from providing all food to advice on reducing saturated fat. The included long-term trials suggested that reducing dietary saturated fat reduced the risk of combined cardiovascular events by 21% (risk ratio (RR) 0.79; 95% confidence interval (CI) 0.66 to 0.93, 11 trials, 53,300 participants of whom 8% had a cardiovascular event, I² = 65%, GRADE moderate-quality evidence). Meta-regression suggested that greater reductions in saturated fat (reflected in greater reductions in serum cholesterol) resulted in greater reductions in risk of CVD events, explaining most heterogeneity between trials. The number needed to treat for an additional beneficial outcome (NNTB) was 56 in primary prevention trials, so 56 people need to reduce their saturated fat intake for ~four years for one person to avoid experiencing a CVD event. In secondary prevention trials, the NNTB was 32. Subgrouping did not suggest significant differences between replacement of saturated fat calories with polyunsaturated fat or carbohydrate, and data on replacement with monounsaturated fat and protein was very limited. We found little or no effect of reducing saturated fat on all-cause mortality (RR 0.96; 95% CI 0.90 to 1.03; 11 trials, 55,858 participants) or cardiovascular mortality (RR 0.95; 95% CI 0.80 to 1.12, 10 trials, 53,421 participants), both with GRADE moderate-quality evidence. There was little or no effect of reducing saturated fats on non-fatal myocardial infarction (RR 0.97, 95% CI 0.87 to 1.07) or CHD mortality (RR 0.97, 95% CI 0.82 to 1.16, both low-quality evidence), but effects on total (fatal or non-fatal) myocardial infarction, stroke and CHD events (fatal or non-fatal) were all unclear as the evidence was of very low quality. There was little or no effect on cancer mortality, cancer diagnoses, diabetes diagnosis, HDL cholesterol, serum triglycerides or blood pressure, and small reductions in weight, serum total cholesterol, LDL cholesterol and BMI. There was no evidence of harmful effects of reducing saturated fat intakes. AUTHORS' CONCLUSIONS: The findings of this updated review suggest that reducing saturated fat intake for at least two years causes a potentially important reduction in combined cardiovascular events. Replacing the energy from saturated fat with polyunsaturated fat or carbohydrate appear to be useful strategies, while effects of replacement with monounsaturated fat are unclear. The reduction in combined cardiovascular events resulting from reducing saturated fat did not alter by study duration, sex or baseline level of cardiovascular risk, but greater reduction in saturated fat caused greater reductions in cardiovascular events

A Comparison Study of Ellipsoid Fitting for Pose Normalization of Hippocampal Shapes

Pose normalization is an important step to establish shape correspondence for group comparison of anatomical structures. The most basic and widely used way is ellipsoid fitting, which provides three principal axes for shape alignment, and is often solved by least square fitting. In this paper, it is recognized that the deformation caused by neuro-degenerative diseases is usually locally irregular, behaving like the outliers to the majority of the anatomical surfaces. Therefore we hypothesize that the distance function in L1-norm may perform better than that in L2-norm for hippocampal surface fitting, and thus conduct a study to compare the influence of different distance functions. In particular, we show how to perform ellipsoid fitting via L1-norm based algebraic and geometric distances, and experimentally compare their performance together with the conventional L2-norm based distance functions. Our study demonstrates that L1-norm approach fits the majority of the surface, while L2-norm approach tends to fit the irregularity

Apparent fibre density: A new measure for high angular resolution diffusion-weighted image analysis

Apparent Fibre Density is a new measure that is based on information provided by Fibre Orientation Distributions. Voxel wise comparisons of Apparent Fibre Density can be made over all orientations permitting differences to be attributed to a single fibre within voxels with multiple fibre populations

The FA connectome: A quantitative strategy for studying neurological disease processes

Structural connectivity indices derived using diffusion based HARDI or q-ball imaging in conjunction with functional parcellation of the cortex from high resolution MRI, has provided insight into the anatomical conformation of many of the important neural networks in the living brain. We are developing the concept of the FA connectome, i.e. combining a measure of fractional anisotropy, a quantitative diffusivity metric that reflects the integrity of WM pathways, with the connectivity matrix. When applied to study Amyotrophic Lateral Sclerosis, this technique shows identifies a number of key corticomotor pathways with reduced mean FA compared to control participants

MR-less surface-based amyloid estimation by subject-specific atlas selection and Bayesian fusion

For clinical evaluation, assessing amyloid deposition with PiB-PET is desirable without requiring MR acquisition and associated fusion/segmentation techniques. A useful clinical tool is to estimate PiB-PET against the brain surface, which is however challenging using PET alone because of the lack of structural information. We propose a method to generate such estimate, where multiple atlases are selected and combined with local weights in a Bayesian framework. Qualitative and quantitative comparison with and without MRI are presented. Using PET only, the average error on the brain surface was around 13% compared to MRI-dependant method