Angle-Closure Detection in Anterior Segment OCT based on Multi-Level Deep Network

Irreversible visual impairment is often caused by primary angle-closure glaucoma, which could be detected via Anterior Segment Optical Coherence Tomography (AS-OCT). In this paper, an automated system based on deep learning is presented for angle-closure detection in AS-OCT images. Our system learns a discriminative representation from training data that captures subtle visual cues not modeled by handcrafted features. A Multi-Level Deep Network (MLDN) is proposed to formulate this learning, which utilizes three particular AS-OCT regions based on clinical priors: the global anterior segment structure, local iris region, and anterior chamber angle (ACA) patch. In our method, a sliding window based detector is designed to localize the ACA region, which addresses ACA detection as a regression task. Then, three parallel sub-networks are applied to extract AS-OCT representations for the global image and at clinically-relevant local regions. Finally, the extracted deep features of these sub-networks are concatenated into one fully connected layer to predict the angle-closure detection result. In the experiments, our system is shown to surpass previous detection methods and other deep learning systems on two clinical AS-OCT datasets.Comment: 9 pages, accepted by IEEE Transactions on Cybernetic

VANT-GAN: adversarial learning for discrepancy-based visual attribution in medical imaging

Visual attribution (VA) in relation to medical images is an essential aspect of modern automation-assisted diagnosis. Since it is generally not straightforward to obtain pixel-level ground-truth labelling of medical images, classification-based interpretation approaches have become the de facto standard for automated diagnosis, in which the ability of classifiers to make categorical predictions based on class-salient regions is harnessed within the learning algorithm. Such regions, however, typically constitute only a small subset of the full range of features of potential medical interest. They may hence not be useful for VA of medical images where capturing all of the disease evidence is a critical requirement. This hence motivates the proposal of a novel strategy for visual attribution that is not reliant on image classification. We instead obtain normal counterparts of abnormal images and find discrepancy maps between the two. To perform the abnormal-to-normal mapping in unsupervised way, we employ a Cycle-Consistency Generative Adversarial Network, thereby formulating visual attribution in terms of a discrepancy map that, when subtracted from the abnormal image, makes it indistinguishable from the counterpart normal image. Experiments are performed on three datasets including a synthetic, Alzheimer’s disease Neuro imaging Initiative and, BraTS dataset. We outperform baseline and related methods in both experiments

Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework

Malaria is a potentially fatal plasmodium parasite injected by female anopheles mosquitoes that infect red blood cells and millions worldwide yearly. However, specialists' manual screening in clinical practice is laborious and prone to error. Therefore, a novel Deep Boosted and Ensemble Learning (DBEL) framework, comprising the stacking of new Boosted-BR-STM convolutional neural networks (CNN) and the ensemble ML classifiers, is developed to screen malaria parasite images. The proposed Boosted-BR-STM is based on a new dilated-convolutional block-based split transform merge (STM) and feature-map Squeezing-Boosting (SB) ideas. Moreover, the new STM block uses regional and boundary operations to learn the malaria parasite's homogeneity, heterogeneity, and boundary with patterns. Furthermore, the diverse boosted channels are attained by employing Transfer Learning-based new feature-map SB in STM blocks at the abstract, medium, and conclusion levels to learn minute intensity and texture variation of the parasitic pattern. The proposed DBEL framework implicates the stacking of prominent and diverse boosted channels and provides the generated discriminative features of the developed Boosted-BR-STM to the ensemble of ML classifiers. The proposed framework improves the discrimination ability and generalization of ensemble learning. Moreover, the deep feature spaces of the developed Boosted-BR-STM and customized CNNs are fed into ML classifiers for comparative analysis. The proposed DBEL framework outperforms the existing techniques on the NIH malaria dataset that are enhanced using discrete wavelet transform to enrich feature space. The proposed DBEL framework achieved Accuracy (98.50%), Sensitivity (0.9920), F-score (0.9850), and AUC (0.997), which suggest it to be utilized for malaria parasite screening.Comment: 26 pages, 10 figures, 9 Table

Malarial retinopathy and neurovascular injury in paediatric cerebral malaria

Background Diseases of the brain are difficult to study because this organ is relatively inaccessible. Only one part of the central nervous system is available to direct, non-invasive observation – the retina. The concept of the retina as a window to the brain has created much interest in the retina as a source of potential markers of brain disease. Paediatric cerebral malaria is a severe neurological complication of infection with the parasite Plasmodium falciparum, which is responsible for death and disability in a significant number of children in sub-Saharan Africa. As with many neurological diseases, the precise mechanisms by which this infection causes damage to the brain remain unclear, and this hampers efforts to develop effective treatments. It may be that studying the retina in paediatric cerebral malaria could both illuminate pathogenesis specific to this disease, and also provide an illustration of how to approach retinal biomarkers in a new, and potentially more effective way. Methods I approached the aim of developing retinal features as markers of brain disease in paediatric cerebral malaria via several objectives. I made use of an existing clinical study to collect new retinal data from ophthalmoscopic examinations and fundus fluorescein angiograms from patients over three successive malaria seasons in Malawi, and added these to historical data obtained previously at the same site. I devised a new method for grading retinal images. I reviewed the biological plausibility of associations between retina and brain in cerebral malaria, and then considered analytical methods to interpret my retinal data effectively. Finally I estimated associations between retinal features, outcomes, and a radiological measure of brain swelling using combinations of regression models. Results My review of retinal and cerebral histopathology, vascular anatomy and physiology indicated that certain retinal and brain regions may be similarly prone to damage from sequestration as a result of interactions between aberrant rheology and microvascular geometry, such as branching patterns and arteriole to venule ratios. My review of evaluations of analogy and surrogacy suggested that biological similarities between retina and brain could be used to justify statistical evaluation of the amount of information the subject and object of the inference share about a common outcome, as used to assess surrogate end points for clinical trials. This kind of approach is able to address questions about whether a particular retinal feature is effectively equivalent to an analogous disease manifestation in the brain. I report analyses on three overlapping groups of subjects, all of whom had retinopathy positive cerebral malaria: children with admission ophthalmoscopy (n=817), children with admission fluorescein angiography (n=260), and children with admission angiography and MRI of the brain (n=134). Several retinal features are associated with death and longer time to recover consciousness in paediatric cerebral malaria. Broadly speaking, these features appear to reflect two processes: neurovascular sequestration (e.g. orange vessel discolouration and death), and neurovascular leakage (e.g. >5 sites of punctate leak and death). Respective adjusted odds ratios and 95% confidence intervals for these particular associations are: 2.88 (1.64-5.05); and 6.90 (1.52-31.3). Other related processes may also be important, such as ischaemia, which can be extensive. Associations between retina and brain are less clear, in part because of selection bias in the samples. Conclusions Neurovascular leak is important in fatal paediatric cerebral malaria, suggesting that fatal brain swelling may occur primarily as a result of vasogenic oedema. Other processes are also likely to be involved, particularly neurovascular sequestration, which is visible on retinal imaging as orange vessels or intravascular filling defects. Sequestration may plausibly cause leak through direct damage to tight junctions and by increasing transmural pressure secondary to venous congestion. Several types of retinal leakage are seen and some of these may represent re-perfusion rather than acute injury. Future work to investigate temporal changes in retinal signs may find clearer associations with radiological and clinical outcomes. The steps taken to evaluate retinal markers in cerebral malaria illustrate a more rigorous approach to retinal biomarkers in general, which can be applied to other neurological disease

A Comparative Study of the Pathology and Pathophysiology of Severe Malaria in a Non-Human Primate Model

With the rise in plasmodial drug resistance throughout the world, a dwindling arsenal of anti-malarials and a predilection for use of less than ideal murine models, the need for effective, applicable and established animal models through which to understand severe malarial disease and test potential treatments is becoming increasingly crucial. Plasmodium coatneyi was discovered in the Philippines in the early 1960s in Rhesus macaque monkeys and has been evaluated sporadically as a potential platform for comparative studies of Plasmodium falciparum induced severe and cerebral malaria in humans. Using 30 years worth of archival samples at the Armed Forces Research Institute of Medical Sciences in Bangkok, Thailand, compounded with data and material from a series of prospective experimental studies, the work in this thesis sought to fully characterize and describe the pathology and pathobiology of the P. coatneyi/Rhesus macaque model. This included a thorough examination of the parasite host interaction, clinical symptomology, hematology and clinical pathology, gross pathology, histopathology, immunohistochemistry, cerebrospinal fluid cytokine profiles, transmission and scanning electron microscopy and atomic force microscopy of the disease in 45 adult rhesus macaques. The study further compared P. coatneyi with existing P. falciparum infected human material maintained at the National University of Singapore’s Vivax laboratory and Oxford University’s Ultrastructural Morphology Group, Nuffield Division of Clinical Laboratory Sciences. The study culminated in a successful clinical drug trial which evaluated the efficacy of varying doses of methylene blue as a radical cure for malaria in this non-human primate model. In many ways, the model mirrors P. falciparum, however the benefit of this evaluation is that it highlights not only the similarities but also the divergences. The successful completion of this description of the pathology and pathobiology of the P. coatneyi/Rhesus macaque model of severe and cerebral malaria adds a complex but effective animal model to the armamentarium in the ongoing war against malaria

The Role of PfEMP1 Expression and Immunity in Ugandian Children with Severe Malaria

Indiana University-Purdue University Indianapolis (IUPUI)Severe malaria, primarily caused by Plasmodium falciparum infection, is among the leading causes of childhood mortality globally. A key virulence factor and source of antigenic variation and immune evasion during infection is P. falciparum erythrocyte membrane protein 1 (PfEMP1). Encoded for by approximately 60 var genes, this complex protein mediates cytoadherence of infected erythrocytes to the host endothelium and is a prominent immune target for the anti-malarial immune response in children. During severe malaria, specific domains of PfEMP1 that bind to endothelial protein C receptor (EPCR) and intercellular adhesion molecule-1 (ICAM-1) on host endothelial cells, are more prevalently expressed. The interaction of these proteins and infected erythrocytes mediates the sequestration of infected erythrocytes and plays a role in severe malaria pathogenesis. Antibodies to these domains develop over time with exposure to the parasite and are thought to contribute to immunity against severe malaria in children. In this study, whole blood samples from children with different forms of severe malaria, enrolled in two observational prospective cohort studies were used to quantify the expression of PfEMP1 domains using RT-qPCR and to measure the antibody response to PfEMP1 domains via a bead-based multiplex immunoassay. Using these samples, we demonstrated that although the expression of var transcripts encoding PfEMP1 domains was generally similar across children with different forms of severe malaria, the expression of variants encoding specific EPCR-binding domains was associated with thrombocytopenia and severe anemia. The antibody response to PfEMP1 domains in children with severe malaria was highest in children with SMA and children with asymptomatic parasitemia, but not associated with decreased risk of additional malaria episodes. Overall, the results of this study suggest that PfEMP1 is acting similarly across different forms of severe malaria but that it can be related to pathogenesis and severe malaria immunity

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations