Applications and potential of machine learning augmented chest X-ray interpretation in cardiology.

The chest X-ray (CXR) has a wide range of clinical indications in the field of cardiology, from the assessment of acute pathology to disease surveillance and screening. Despite many technological advancements, CXR interpretation error rates have remained constant for decades. The application of machine learning has the potential to substantially improve clinical workflow efficiency, pathology detection accuracy, error rates and clinical decision making in cardiology.

Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy.

Objectives: Non-contrast computed tomography of the brain (NCCTB) is commonly used to detect intracranial pathology but is subject to interpretation errors. Machine learning can augment clinical decision-making and improve NCCTB scan interpretation. This retrospective detection accuracy study assessed the performance of radiologists assisted by a deep learning model and compared the standalone performance of the model with that of unassisted radiologists.

ADMANI: Annotated Digital Mammograms and Associated Non-Image Datasets.

Mammography, Screening, Convolutional Neural Network (CNN) Published under a CC BY 4.0 license. See also the commentary by Cadrin-Chênevert in this issue.

Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review.

Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning systems to assist clinicians and improve interpretation accuracy. An understanding of the capabilities and limitations of modern machine learning systems is necessary for clinicians as these tools begin to permeate practice. This systematic review aimed to provide an overview of machine learning applications designed to facilitate CXR interpretation.

Charting the potential of brain computed tomography deep learning systems.

Brain computed tomography (CTB) scans are widely used to evaluate intracranial pathology. The implementation and adoption of CTB has led to clinical improvements. However, interpretation errors occur and may have substantial morbidity and mortality implications for patients.

Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography.

Objectives: To evaluate the ability of a commercially available comprehensive chest radiography deep convolutional neural network (DCNN) to detect simple and tension pneumothorax, as stratified by the following subgroups: the presence of an intercostal drain; rib, clavicular, scapular or humeral fractures or rib resections; subcutaneous emphysema and erect versus non-erect positioning. The hypothesis was that performance would not differ significantly in each of these subgroups when compared with the overall test dataset.

Design: A retrospective case-control study was undertaken.

Distal Medium Vessel Occlusions Can Be Accurately and Rapidly Detected Using Maps.

Background And Purpose: Distal medium vessel occlusions (DMVOs) are increasingly considered for endovascular thrombectomy but are difficult to detect on computed tomography angiography (CTA). We aimed to determine whether time-to-maximum of tissue residue function (Tmax) maps, derived from CT perfusion, can be used as a triage screening tool to accurately and rapidly identify patients with DMVOs.

Methods: Consecutive code stroke patients who underwent multimodal CT were screened retrospectively.

Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study.

Background: Chest x-rays are widely used in clinical practice; however, interpretation can be hindered by human error and a lack of experienced thoracic radiologists. Deep learning has the potential to improve the accuracy of chest x-ray interpretation. We therefore aimed to assess the accuracy of radiologists with and without the assistance of a deep-learning model.

Evaluation of deep learning-based artificial intelligence techniques for breast cancer detection on mammograms: Results from a retrospective study using a BreastScreen Victoria dataset.

Introduction: This study aims to evaluate deep learning (DL)-based artificial intelligence (AI) techniques for detecting the presence of breast cancer on a digital mammogram image.

Methods: We evaluated several DL-based AI techniques that employ different approaches and backbone DL models and tested the effect on performance of using different data-processing strategies on a set of digital mammographic images with annotations of pathologically proven breast cancer.

Results: Our evaluation uses the area under curve (AUC) and accuracy (ACC) for performance measurement.

Incidental detection of prostate cancer with computed tomography scans.

Prostate cancer (PCa) is the second most frequent type of cancer found in men worldwide, with around one in nine men being diagnosed with PCa within their lifetime. PCa often shows no symptoms in its early stages and its diagnosis techniques are either invasive, resource intensive, or has low efficacy, making widespread early detection onerous. Inspired by the recent success of deep convolutional neural networks (CNN) in computer aided detection (CADe), we propose a new CNN based framework for incidental detection of clinically significant prostate cancer (csPCa) in patients who had a CT scan of the abdomen/pelvis for other reasons.

Sequences show rapid motor transfer and spatial translation in the oculomotor system.

Every day we perform learnt sequences of actions that seem to happen almost without awareness. It has been argued that for learning such sequences parallel learning networks exist - one using spatial coordinates and one using motor coordinates - with sequence acquisition involving a progressive shift from the former to the latter as a sequence is rehearsed. When sequences are interrupted by an out-of-sequence target, there is a delay in the response to the target, and so here we transiently interrupt oculomotor sequences to probe the influence of oculomotor rehearsal and spatial coordinates in sequence acquisition.

Target direction rather than position determines oculomotor expectation in repeating sequences.

Saccadic latencies to targets appearing to the left and right of fixation in a repeating sequence are significantly increased when a target is presented out of sequence. Is this because the target is in the wrong position, the wrong direction, or both? To find out, we arranged for targets in a horizontal plane occasionally to appear with an unexpected eccentricity, though in the correct direction. This had no significant effect on latency, unlike what is observed when targets appeared in the unexpected direction.

White matter changes in patients with Parkinson's disease carrying small CGG expansion FMR1 alleles: a pilot study.

Background/aims: Alleles of the FMR1 gene containing small expansions of the CGG-trinucleotide repeat comprise premutation and grey-zone alleles. Premutation alleles may cause late-onset Fragile X-associated tremor/ataxia syndrome attributed to the neurotoxic effect of elevated FMR1 transcripts. Our earlier data suggested that both grey-zone and low-end premutation alleles might also play a significant role in the acquisition of the parkinsonian phenotype due to mitochondrial dysfunction caused by elevated FMR1 mRNA toxicity.

A state space based approach in non-linear hemodynamic response modeling with fMRI data.

In this paper we use the modified and integrated version of the balloon model in the analysis of fMRI data. We propose a new state space model realization for this balloon model and represent it with the standard A,B,C and D matrices widely used in system theory. A second order Padé approximation with equal numerator and denominator degree is used for the time delay approximation in the modeling of the cerebral blood flow.

Maximum likelihood estimation for Rician distributed data in analytical q-ball imaging.

Analytical q-ball imaging is widely used for reconstruction of orientation distribution function (ODF) using diffusion weighted MRI data. Estimating the spherical harmonic coefficients is a critical step in this method. Least squares (LS) is widely used for this purpose assuming the noise to be additive Gaussian.

Robust ODF smoothing for accurate estimation of fiber orientation.

Q-ball imaging was presented as a model free, linear and multimodal diffusion sensitive approach to reconstruct diffusion orientation distribution function (ODF) using diffusion weighted MRI data. The ODFs are widely used to estimate the fiber orientations. However, the smoothness constraint was proposed to achieve a balance between the angular resolution and noise stability for ODF constructs.

Comparison of MRI sequences for evaluation of multiple sclerosis of the cervical spinal cord at 3 T.

Purpose: Debate remains regarding the utility of the traditional STIR (short inversion time recovery) sequence in aiding MRI diagnosis of spinal cord lesions in patients with multiple sclerosis (MS) and this sequence is not included in the current imaging guidelines. A recent study proposed a T1 weighted STIR as a superior alternative to the traditional STIR and T2 fast spin echo (FSE). Thus, the aim of this study was to compare the sensitivity of T2, standard STIR and T1 weighted STIR sequences in the evaluation of MS plaques on our 3 T system.

Association between radioiodine therapy for Graves' hyperthyroidism and thyroid-associated ophthalmopathy.

To investigate the role of radioactive iodine (RAI) in the onset and progression of thyroid-associated ophthalmopathy (TAO). Forty-six Graves' disease patients with mild or no ophthalmopathy were prospectively treated with carbimazole (CBZ) (n = 22) or RAI (n = 24). Treatment effects were evaluated clinically over 12 months, and with orbital MRI-measured extra-ocular muscle (EOM) volumes at baseline and at 6 months.

Magnetic resonance imaging study in older fragile X premutation male carriers.

Some carriers of a "premutation" allele of the FMR1 gene develop late-onset tremor/ataxia. We conducted a magnetic resonance imaging volumetric study in an unselected sample of eight older male premutation carriers. Volumetric measures, including total brain volume, and the volumes of cerebrum, cerebellum, and cerebral cortex all were significantly reduced in premutation carriers compared with similar data from 21 age-matched normal controls.

An unusual case of hypertensive encephalopathy.

This case report describes a 59-year-old male who presented with headaches, seizures and hypertension followed by coma. Initial magnetic resonance imaging showed T2 hyperintensities typical of Hypertensive Encephalopathy (HE), the follow up scans showed diffusion-weighted imaging (DWI) hyperintensities which is a rare finding in HE. DWI hyperintensities are typically suggestive of areas of cytotoxic damage, and the presence of these changes makes this case unusual, since the pathogenesis of HE is usually due to vasogenic oedema rather than cytotoxic damage of the brain tissue.

Head position modulates activity in the human parietal eye fields.

For us to interact with our environment we need to know where objects are around us, relative to our body. In monkeys, a body-centered map of visual space is known to exist within the parietal eye fields. This map is formed by the modulation of retinal responses by gain fields to gaze position.