In recent years, deep convolutional neural networks (CNNs) have shown record-shattering performance in a variety of computer vision problems, such as visual object recognition, detection and segmentation. These methods have also been utilised in medical image analysis domain for lesion segmentation, anatomical segmentation and classification. We present an extensive literature review of CNN techniques applied in brain magnetic resonance imaging (MRI) analysis, focusing on the architectures, pre-processing, data-preparation and post-processing strategies available in these works. The aim of this study is three-fold. Our primary goal is to report how different CNN architectures have evolved, discuss state-of-the-art strategies, condense their results obtained using public datasets and examine their pros and cons. Second, this paper is intended to be a detailed reference of the research activity in deep CNN for brain MRI analysis. Finally, we present a perspective on the future of CNNs in which we hint some of the research directions in subsequent years.

Purpose To assess accuracy and adherence of visual field (VF) home-monitoring in a pilot sample of glaucoma patients. Design Prospective longitudinal feasibility and reliability study. Methods Twenty adults (median 71 years) with an established diagnosis of glaucoma were issued a tablet-perimeter (Eyecatcher), and were asked to perform one VF home-assessment per eye, per month, for 6 months (12 tests total). Before and after home-monitoring, two VF assessments were performed in-clinic using Standard Automated Perimetry (SAP; 4 tests total, per eye). Results All 20 participants could perform monthly home-monitoring, though one participant stopped after 4 months (Adherence: 98%). There was good concordance between VFs measured at home and in the clinic ( r = 0.94, P < 0.001). In 21 of 236 tests (9%) Mean Deviation deviated by more than ±3 dB from the median. Many of these anomalous tests could be identified by applying machine learning techniques to recordings from the tablets’ front-facing camera (Area Under the ROC Curve = 0.78). Adding home-monitoring data to 2 SAP tests made 6 months apart reduced measurement error (between-test measurement variability) in 97% of eyes, with mean absolute error more than halving in 90% of eyes. Median test duration was 4.5 mins ( Quartiles : 3.9 – 5.2 mins). Substantial variations in ambient illumination had no observable effect on VF measurements ( r = 0.07, P = 0.320). Conclusions Home-monitoring of VFs is viable for some patients, and may provide clinically useful data.

Eye movements are altered by visual field loss, and these changes are related to changes in clinical measures. Eye movements recorded while passively viewing images could potentially be used as biomarkers for visual field damage.

Objective:Satisfaction is becoming a popular health-care quality indicator as it reflects the reality of service or care provided. The aim of this study was to assess the level of patients' expectation toward and satisfaction from pharmacy service provided and to identify associated factor that might affect their expectation and satisfaction.Methods:A cross-sectional study was conducted on 287 patients, who were served in five pharmacies of Gondar University Hospital in May 2015. Data regarding socio-demographic characteristics and parameters that measure patients' expectation and satisfaction were collected through interview using the Amharic version of the questionnaire. Data were entered into SPSS version 21, and descriptive statistics, cross-tabs, and binary logistic regressions were utilized. P < 0.05 was used to declare association.Findings:Among 287 respondents involved in the study, 149 (51.9%) claimed to be satisfied with the pharmacy service and setting. Two hundred and twenty-nine (79.4%) respondents have high expectation toward gaining good services. Even though significant association was observed between the pharmacy type and patients level of satisfaction, sociodemographic characteristics of a patient were not found to predict the level of satisfaction. There is a higher level of expectation among study participants who earn higher income per month (>(2000 Ethiopian birr [ETB]) than those who get less income (<1000 ETB).Conclusion:Although patients have a higher level of expectation toward pharmacy services, their satisfaction from the service was found to be low.

ObjectivesTo explore the acceptability of home visual field (VF) testing using Eyecatcher among people with glaucoma participating in a 6-month home monitoring pilot study.DesignQualitative study using face-to-face semistructured interviews. Transcripts were analysed using thematic analysis.SettingParticipants were recruited in the UK through an advertisement in the International Glaucoma Association (now Glaucoma UK) newsletter.ParticipantsTwenty adults (10 women; median age: 71 years) with a diagnosis of glaucoma were recruited (including open angle and normal tension glaucoma; mean deviation=2.5 to −29.9 dB).ResultsAll participants could successfully perform VF testing at home. Interview data were coded into four overarching themes regarding experiences of undertaking VF home monitoring and attitudes towards its wider implementation in healthcare: (1) comparisons between Eyecatcher and Humphrey Field Analyser (HFA); (2) capability using Eyecatcher; (3) practicalities for effective wider scale implementation; (4) motivations for home monitoring.ConclusionsParticipants identified a broad range of benefits to VF home monitoring and discussed areas for service improvement. Eyecatcher was compared positively with conventional VF testing using HFA. Home monitoring may be acceptable to at least a subset of people with glaucoma.

Glaucoma is a leading cause of irreversible sight-loss and has been shown to affect natural eyemovements. These changes may provide a cheap and easy-to-obtain biomarker for improving disease detection. Here, we investigated whether these changes are large enough to be clinically useful. We used a gaze-contingent simulated visual field (VF) loss paradigm, in which participants experienced a variable magnitude of simulated VF loss based on longitudinal data from a real glaucoma patient (thereby controlling for other variables, such as age and general health). Fifty-five young participants with healthy vision were asked to view two short videos and three pictures, either with: (1) no VF loss, (2) moderate VF loss, or (3) advanced VF loss. Eye-movements were recorded using a remote eye tracker. Key eye-movement parameters were computed, including saccade amplitude, the spread of saccade endpoints (bivariate contour ellipse area), location of saccade landing positions, and similarity of fixations locations among participants (quantified using kernel density estimation). The simulated VF loss caused some statistically significant effects in the eye movement parameters. Yet, these effects were not capable of consistently identifying simulated VF loss, despite it being of a magnitude likely easily detectable by standard automated perimetry. Glaucoma is a chronic eye disease affecting 1 in 28 people aged 40-80 years 1. It is characterised by progressive, irreversible visual field (VF) loss. Early detection is therefore crucial 2. Currently, however, as many as 50% of cases remain undiagnosed 3-5 , or are diagnosed late: after substantial vision loss has already occurred 6. At present, successful detection of glaucoma requires a detailed assessment by a specialist clinician, including measurements of intraocular pressure (IOP), VF loss by standard automated perimetry, and inspection of the optic nerve head. Unfortunately, many adults do not attend routine eye-checks due to associated costs (real and perceived), lack of awareness, aversion to the methods used, and lack of understanding about their purpose 7,8. One way to improve glaucoma detection would be to perform proactive screening. However, this is not economical using traditional technologies, owing to the high cost of the personnel and equipment involved 9. What is needed, therefore, is an inexpensive, automated screening tool to identify high-risk individuals. Modern eye tracking technologies may be able to provide such a solution. Previous studies have shown that natural eye movements are altered in instances of glaucomatous VF loss. For example, our research has demonstrated that glaucoma patients exhibit differences in saccade frequency and gaze spread when free-viewing images, relative to normally sighted controls 10. More recently, we found similar differences in eye-movements between the two eyes of glaucoma patients with asymmetric VF loss, when free-viewing pictures monocularly 11. Several other studies have also likewise confirmed a link between simulated ...

Eye movements of glaucoma patients have been shown to differ from age-similar control groups when performing everyday tasks, such as reading (Burton et al., 2012; Smith et al., 2014) [1], [2], visual search (Smith et al., 2012) [3], face recognition (Glen et al., 2013) [4], driving, and viewing static images (Smith et al., 2012) [5]. Described here is the dataset from a recent publication in which we compared the eye-movements of 44 glaucoma patients and 32 age-similar controls, while they watched a series of short video clips taken from television programs (Crabb et al., 2018) [6]. Gaze was recorded at 1000 Hz using a remote eye-tracker. We also provide demographic information and results from a clinical examination of vision for each participant.

To explore the feasibility of using various easy-to-obtain biomarkers to monitor non-compliance (measurement error) during visual field assessments. Methods: Forty-two healthy adults (42 eyes) and seven glaucoma patients (14 eyes) underwent two same-day visual field assessments. An ordinary webcam was used to compute seven potential biomarkers of task compliance, based primarily on eye gaze, head pose, and facial expression. We quantified the association between each biomarker and measurement error, as defined by (1) test-retest differences in overall test scores (mean sensitivity), and (2) failures to respond to visible stimuli on individual trials (stimuli −3 dB or more brighter than threshold). Results: In healthy eyes, three of the seven biomarkers were significantly associated with overall (test-retest) measurement error (P = 0.003-0.007), and at least two others exhibited possible trends (P = 0.052-0.060). The weighted linear sum of all seven biomarkers was associated with overall measurement error, in both healthy eyes (r = 0.51, P < 0.001) and patients (r = 0.65, P < 0.001). Five biomarkers were each associated with failures to respond to visible stimuli on individual trials (all P < 0.001). Conclusions: Inexpensive, autonomous measures of task compliance are associated with measurement error in visual field assessments, in terms of both the overall reliability of a test and failures to respond on particular trials ("lapses"). This could be helpful for identifying low-quality assessments and for improving assessment techniques (e.g., by discounting suspect responses or by automatically triggering comfort breaks or encouragement). Translational Relevance: This study explores a potential way of improving the reliability of visual field assessments, a crucial but notoriously unreliable clinical measure.