Development of a deep learning method to identify acute ischaemic stroke lesions on brain CT.

Background: CT is commonly used to image patients with ischaemic stroke but radiologist interpretation may be delayed. Machine learning techniques can provide rapid automated CT assessment but are usually developed from annotated images which necessarily limits the size and representation of development data sets. We aimed to develop a deep learning (DL) method using CT brain scans that were labelled but not annotated for the presence of ischaemic lesions.

Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images.

Segmentation masks of pathological areas are useful in many medical applications, such as brain tumour and stroke management. Moreover, healthy counterfactuals of diseased images can be used to enhance radiologists' training files and to improve the interpretability of segmentation models. In this work, we present a weakly supervised method to generate a healthy version of a diseased image and then use it to obtain a pixel-wise anomaly map.

Choroidalyzer: An Open-Source, End-to-End Pipeline for Choroidal Analysis in Optical Coherence Tomography.

Purpose: To develop Choroidalyzer, an open-source, end-to-end pipeline for segmenting the choroid region, vessels, and fovea, and deriving choroidal thickness, area, and vascular index.

Methods: We used 5600 OCT B-scans (233 subjects, six systemic disease cohorts, three device types, two manufacturers). To generate region and vessel ground-truths, we used state-of-the-art automatic methods following manual correction of inaccurate segmentations, with foveal positions manually annotated.

Prediction of retinopathy progression using deep learning on retinal images within the Scottish screening programme.

Background/aims: National guidelines of many countries set screening intervals for diabetic retinopathy (DR) based on grading of the last screening retinal images. We explore the potential of deep learning (DL) on images to predict progression to referable DR beyond DR grading, and the potential impact on assigned screening intervals, within the Scottish screening programme.

Methods: We consider 21 346 and 247 233 people with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), respectively, each contributing on average 4.

An Open-Source Deep Learning Algorithm for Efficient and Fully Automatic Analysis of the Choroid in Optical Coherence Tomography.

Purpose: To develop an open-source, fully automatic deep learning algorithm, DeepGPET, for choroid region segmentation in optical coherence tomography (OCT) data.

Methods: We used a dataset of 715 OCT B-scans (82 subjects, 115 eyes) from three clinical studies related to systemic disease. Ground-truth segmentations were generated using a clinically validated, semiautomatic choroid segmentation method, Gaussian Process Edge Tracing (GPET).

Deep learning detection of diabetic retinopathy in Scotland's diabetic eye screening programme.

Background/aims: Support vector machine-based automated grading (known as iGradingM) has been shown to be safe, cost-effective and robust in the diabetic retinopathy (DR) screening (DES) programme in Scotland. It triages screening episodes as gradable with no DR versus manual grading required. The study aim was to develop a deep learning-based autograder using images and gradings from DES and to compare its performance with that of iGradingM.

Can deep learning on retinal images augment known risk factors for cardiovascular disease prediction in diabetes? A prospective cohort study from the national screening programme in Scotland.

Aims: This study's objective was to evaluate whether deep learning (DL) on retinal photographs from a diabetic retinopathy screening programme improve prediction of incident cardiovascular disease (CVD).

Methods: DL models were trained to jointly predict future CVD risk and CVD risk factors and used to output a DL score. Poisson regression models including clinical risk factors with and without a DL score were fitted to study cohorts with 2,072 and 38,730 incident CVD events in type 1 (T1DM) and type 2 diabetes (T2DM) respectively.

Deep attention super-resolution of brain magnetic resonance images acquired under clinical protocols.

Vast quantities of Magnetic Resonance Images (MRI) are routinely acquired in clinical practice but, to speed up acquisition, these scans are typically of a quality that is sufficient for clinical diagnosis but sub-optimal for large-scale precision medicine, computational diagnostics, and large-scale neuroimaging collaborative research. Here, we present a critic-guided framework to upsample low-resolution (often 2D) MRI full scans to help overcome these limitations. We incorporate feature-importance and self-attention methods into our model to improve the interpretability of this study.

Accuracy of Automated Computer-Aided Diagnosis for Stroke Imaging: A Critical Evaluation of Current Evidence.

There is increasing interest in computer applications, using artificial intelligence methodologies, to perform health care tasks previously performed by humans, particularly in medical imaging for diagnosis. In stroke, there are now commercial artificial intelligence software for use with computed tomography or MR imaging to identify acute ischemic brain tissue pathology, arterial obstruction on computed tomography angiography or as hyperattenuated arteries on computed tomography, brain hemorrhage, or size of perfusion defects. A rapid, accurate diagnosis may aid treatment decisions for individual patients and could improve outcome if it leads to effective and safe treatment; or conversely, to disaster if a delayed or incorrect diagnosis results in inappropriate treatment.

Meta-Learning in Neural Networks: A Survey.

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Contrary to conventional approaches to AI where tasks are solved from scratch using a fixed learning algorithm, meta-learning aims to improve the learning algorithm itself, given the experience of multiple learning episodes. This paradigm provides an opportunity to tackle many conventional challenges of deep learning, including data and computation bottlenecks, as well as generalization.

Individualized prediction of psychosis in subjects with an at-risk mental state.

Early intervention strategies in psychosis would significantly benefit from the identification of reliable prognostic biomarkers. Pattern classification methods have shown the feasibility of an early diagnosis of psychosis onset both in clinical and familial high-risk populations. Here we were interested in replicating our previous classification findings using an independent cohort at clinical high risk for psychosis, drawn from the prospective FePsy (Fruherkennung von Psychosen) study.

Improved individualized prediction of schizophrenia in subjects at familial high risk, based on neuroanatomical data, schizotypal and neurocognitive features.

To date, there are no reliable markers for predicting onset of schizophrenia in individuals at high risk (HR). Substantial promise is, however, shown by a variety of pattern classification approaches to neuroimaging data. Here, we examined the predictive accuracy of support vector machine (SVM) in later diagnosing schizophrenia, at a single-subject level, using a cohort of HR individuals drawn from multiply affected families and a combination of neuroanatomical, schizotypal and neurocognitive variables.

Evaluation of a pre-surgical functional MRI workflow: From data acquisition to reporting.

Purpose: Present and assess clinical protocols and associated automated workflow for pre-surgical functional magnetic resonance imaging in brain tumor patients.

Methods: Protocols were validated using a single-subject reliability approach based on 10 healthy control subjects. Results from the automated workflow were evaluated in 9 patients with brain tumors, comparing fMRI results to direct electrical stimulation (DES) of the cortex.

The Supervised Hierarchical Dirichlet Process.

We propose the supervised hierarchical Dirichlet process (sHDP), a nonparametric generative model for the joint distribution of a group of observations and a response variable directly associated with that whole group. We compare the sHDP with another leading method for regression on grouped data, the supervised latent Dirichlet allocation (sLDA) model. We evaluate our method on two real-world classification problems and two real-world regression problems.

Brain white matter structure and information processing speed in healthy older age.

Cognitive decline, especially the slowing of information processing speed, is associated with normal ageing. This decline may be due to brain cortico-cortical disconnection caused by age-related white matter deterioration. We present results from a large, narrow age range cohort of generally healthy, community-dwelling subjects in their seventies who also had their cognitive ability tested in youth (age 11 years).

Reduced structural connectivity within a prefrontal-motor-subcortical network in amyotrophic lateral sclerosis.

Background: To investigate white matter structural connectivity changes associated with amyotrophic lateral sclerosis (ALS) using network analysis and compare the results with those obtained using standard voxel-based methods, specifically Tract-based Spatial Statistics (TBSS).

Methods: MRI data were acquired from 30 patients with ALS and 30 age-matched healthy controls. For each subject, 85 grey matter regions (network nodes) were identified from high resolution structural MRI, and network connections formed from the white matter tracts generated by diffusion MRI and probabilistic tractography.

Test-retest reliability of structural brain networks from diffusion MRI.

Structural brain networks constructed from diffusion MRI (dMRI) and tractography have been demonstrated in healthy volunteers and more recently in various disorders affecting brain connectivity. However, few studies have addressed the reproducibility of the resulting networks. We measured the test-retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.

A test-retest fMRI dataset for motor, language and spatial attention functions.

Background: Since its inception over twenty years ago, functional magnetic resonance imaging (fMRI) has been used in numerous studies probing neural underpinnings of human cognition. However, the between session variance of many tasks used in fMRI remains understudied. Such information is especially important in context of clinical applications.

Single subject fMRI test-retest reliability metrics and confounding factors.

While the fMRI test-retest reliability has been mainly investigated from the point of view of group level studies, here we present analyses and results for single-subject test-retest reliability. One important aspect of group level reliability is that not only does it depend on between-session variance (test-retest), but also on between-subject variance. This has partly led to a debate regarding which reliability metric to use and how different sources of noise contribute to between-session variance.

Adaptive thresholding for reliable topological inference in single subject fMRI analysis.

Single subject fMRI has proved to be a useful tool for mapping functional areas in clinical procedures such as tumor resection. Using fMRI data, clinicians assess the risk, plan and execute such procedures based on thresholded statistical maps. However, because current thresholding methods were developed mainly in the context of cognitive neuroscience group studies, most single subject fMRI maps are thresholded manually to satisfy specific criteria related to single subject analyzes.

Reproducibility of tract segmentation between sessions using an unsupervised modelling-based approach.

This work describes a reproducibility analysis of scalar water diffusion parameters, measured within white matter tracts segmented using a probabilistic shape modelling method. In common with previously reported neighbourhood tractography (NT) work, the technique optimises seed point placement for fibre tracking by matching the tracts generated using a number of candidate points against a reference tract, which is derived from a white matter atlas in the present study. No direct constraints are applied to the fibre tracking results.

Predicting ambulance diversion in an adult Emergency Department using a Gaussian process.

When the Emergency Department (ED) reaches a critical level of overcrowding, it diverts ambulances to other hospitals. We evaluated the accuracy of a Gaussian process for prediction of ambulance diversion using March 1, 2005 November 30, 2005 data. The area under the receiver operating curve (AUC) for 120 minutes in advance was 0.

Tract shape modelling provides evidence of topological change in corpus callosum genu during normal ageing.

Understanding how ageing affects brain structure is an important challenge for medical science. By allowing segmentation of fasciculi-of-interest from diffusion magnetic resonance imaging (dMRI) data, tractography provides a promising tool for assessing white matter connectivity in old age. However, the output from tractography algorithms is usually strongly dependent on the subjective location of user-specified seed points, with the result that it can be both difficult and time consuming to identify the same tract reliably in cross-sectional studies.