Altered movement strategy of chronic ankle instability individuals with postural instability classified based on Nyquist and Bode analyses

BackgroundThe aim of the current study was to assess movement strategies during a single leg balance in chronic ankle instability individuals with unstable postural control strategy identified by Nyquist and Bode analyses in conjunction with sample entropy.MethodsThirty-three participants with self-reported chronic ankle instability and 22 healthy controls performed single-leg eyes closed static balance trials. The sagittal and frontal plane kinematics in the lower extremity and trunk as well as center of pressure trajectories were recorded during three, 20-second trials. The Nyquist and Bode stability analyses, which classify center of pressure waveforms as stable based on the resulting gain and phase margins, were performed to identify the presence of postural control deficits. Sample entropy was implemented to analyze movement strategies during the task.FindingsBased on the Nyquist and Bode stability analyses, we included 19 out of 33 chronic ankle instability participants with unstable postural control strategy and 16 out of 22 controls with stable postural control strategy in the final analyses. Chronic ankle instability participants demonstrated a significantly lower sample entropy value in sagittal and frontal plane trunk kinematics and sagittal plane hip kinematics compared to the controls. No between-group differences existed in other kinematic measures.InterpretationThe lower sample entropy values in participants with chronic ankle instability indicates that those with postural control deficits may increase reliance on the trunk and hip joint contributions to the maintenance of postural control, reflecting changes in the sensorimotor constraints on movement patterns during the task.     

A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images

BACKGROUND AND PURPOSE:Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.MATERIALS AND METHODS:Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20).RESULTS:StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.CONCLUSIONS:A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.

White matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter.¹ Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer’s disease and vascular dementia, and increased risk of stroke.^2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research.^4,5 Although the extent of WMHs can be visually scored,⁶ the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. Manually segmenting WMHs is tedious, prone to inter- and intraobserver variability, and is, in most cases, impractical. Thus, there is an increased interest in developing fast, accurate, and reliable computer-aided automated techniques for WMH segmentation.Convolutional neural network (CNN)-based approaches have been successful in several semantic segmentation tasks in medical imaging.⁷ Recent works have proposed using deep learning–based methods for segmenting WMHs using 2D-FLAIR images.^8-11 More recently, a WMH segmentation challenge¹² was also organized (http://wmh.isi.uu.nl/) to facilitate comparison of automated segmentation of WMHs of presumed vascular origin in 2D multislice T2-FLAIR images. Architectures that used an ensemble of separately trained CNNs showed promising results in this challenge, with 3 of the top 5 winners using ensemble-based techniques.¹²Conventional 2D-FLAIR images are typically acquired with thick slices (3–4 mm) and possible slice gaps. Partial volume effects from a thick slice are likely to affect the detection of smaller lesions, both in-plane and out-of-plane. 3D-FLAIR images, with isotropic resolution, have been shown to achieve higher resolution and contrast-to-noise ratio¹³ and have shown promising results in MS lesion detection using 3D CNNs.¹⁴ Additionally, the isotropic resolution enables viewing and evaluation of the images in multiple planes. This multiplanar reformatting of 3D-FLAIR without the use of interpolating kernels is only possible due to the isotropic nature of the acquisition. Network architectures that use information from the 3 orthogonal views have been explored in recent works for CNN-based segmentation of 3D MR imaging data.¹⁵ The use of data from multiple planes allows more spatial context during training without the computational burden associated with full 3D training.¹⁶ The use of 3 orthogonal views simultaneously mirrors how humans approach this segmentation task.Ensembles of CNNs have been shown to average away the variances in the solution and the choice of model- and configuration-specific behaviors of CNNs.¹⁷ Traditionally, the solutions from these separately trained CNNs are combined by averaging or using a majority consensus. In this work, we propose the use of a stacked generalization framework (StackGen-Net) for combining multiplanar lesion information from 3D CNN ensembles to improve the detection of WMH lesions in 3D-FLAIR. A stacked generalization¹⁸ framework learns to combine solutions from individual CNNs in the ensemble. We systematically evaluated the performance of this framework and compared it with traditional ensemble techniques, such as averaging or majority voting, and state-of-the-art deep learning techniques.     

Repeat resection in recurrent glioblastoma (3rGBM) Trial: A randomized care trial

BackgroundThe prognosis for patients with recurrent glioblastoma (GBM) is dismal, and the question of repeat surgery at time of recurrence is common. Re-operation in the management of these patients remains controversial, as there is no randomized evidence of benefit. An all-inclusive pragmatic care trial is needed to evaluate the role of repeat resection.Methods3rGBM is a multicenter, pragmatic, prospective, parallel-group randomized care trial, with 1:1 allocation to repeat resection or standard care with no repeat resection. To test the hypothesis that repeat resection can improve overall survival by at least 3 months (from 6 to 9 months), 250 adult patients with prior resection of pathology-proven glioblastoma for whom the attending surgeon believes repeat resection may improve quality survival will be enrolled. A surrogate measure of quality of life, the number of days outside of hospital/nursing/palliative care facility, will also be compared. Centers are invited to participate without financial compensation and without contracts. Clinicians may apply to local authorities to approve an investigator-led in-house trial, using a common protocol, web-based randomization platform, and simple standardized case report forms.DiscussionThe 3rGBM trial is a modern transparent care research framework with no additional risks, tests, or visits other than what patients would encounter in normal care. The burden of proof remains on repeat surgical management of recurrent GBM, because this management has yet to be shown beneficial. The trial is designed to help patients and surgeons manage the uncertainty regarding optimal care.Clinical Trial Registrationhttp://www.clinicaltrials.gov. Unique identifier: NCT04838782.     

Peer outreach point-of-care testing as a bridge to hepatitis C care for people who inject drugs in Toronto,Canada

BackgroundPeople who inject drugs have high rates of hepatitis C (HCV) and yet many remain undiagnosed and untreated. HCV treatment guidelines and elimination strategies recommend task-shifting to expand where, and by whom, HCV testing and care is delivered.MethodsA randomized controlled trial design was used to evaluate if point-of-care (POC) HCV antibody testing by peer outreach workers outside of health and social service spaces would improve engagement in HCV care. People with a lifetime history of injection drug use without prior knowledge of HCV antibody status were randomized to receive HCV outreach plus either POC or referral to community-based HCV program for testing as usual. The study was co-designed by people with lived experience of HCV.Results920 people were approached to participate over 14 weeks. After refusals, withdrawals and removal of duplicates, there were 380 study participants. Outreach took place primarily in public spaces (66%) such as parks, coffee shops and apartment lobbies. Participants reported very high rates of poverty, housing instability and recent injection drug use. Despite being at high risk for HCV, 61% had no history or knowledge of past HCV testing (n = 230). Of those who received a POC test 77/195 (39%) were positive for HCV antibodies. There was no change in rates of engagement in HCV care among those who received the POC (n = 6; 3%) compared to those who did not (n = 5; 3%).ConclusionPeer outreach workers were able to efficiently reach a marginalized group of individuals who had a high HCV antibody prevalence and low rates of prior HCV testing. This improved participants’ knowledge of their HCV antibody status, but that knowledge in itself did not lead to any change in participant's subsequent engagement in HCV care. Future work is required to evaluate strategies such as incentives or peer navigators to improve linkage to HCV care after diagnosis.     

Diabetic macular ischaemia- a new therapeutic target?

Diabetic macular ischaemia (DMI) is traditionally defined and graded based on the angiographic evidence of an enlarged and irregular foveal avascular zone. However, these anatomical changes are not surrogate markers for visual impairment. We postulate that there are vascular phenotypes of DMI based on the relative perfusion deficits of various retinal capillary plexuses and choriocapillaris. This review highlights several mechanistic pathways, including the role of hypoxia and the complex relation between neurons, glia, and microvasculature. The current animal models are reviewed, with shortcomings noted. Therefore, utilising the advancing technology of optical coherence tomography angiography (OCTA) to identify the reversible DMI phenotypes may be the key to successful therapeutic interventions for DMI. However, there is a need to standardise the nomenclature of OCTA perfusion status. Visual acuity is not an ideal endpoint for DMI clinical trials. New trial endpoints that represent disease progression need to be developed before irreversible vision loss in patients with DMI. Natural history studies are required to determine the course of each vascular and neuronal parameter to define the DMI phenotypes. These DMI phenotypes may also partly explain the development and recurrence of diabetic macular oedema. It is also currently unclear where and how DMI fits into the diabetic retinopathy severity scales, further highlighting the need to better define the progression of diabetic retinopathy and DMI based on both multimodal imaging and visual function. Finally, we discuss a complete set of proposed therapeutic pathways for DMI, including cell-based therapies that may provide restorative potential.