Usefulness of newer testing modalities for the accurate diagnosis of culture-negative endocarditis.

A woman in her 80s with a history of congestive heart failure, atrial arrhythmia treated with atrioventricular nodal ablation and permanent pacemaker (PPM) placement, mitral valve disease status post-repair and colon cancer status post-treatment was admitted for further evaluation of severe dyspnea on exertion. Imaging revealed vegetation on both the prosthetic mitral valve and the PPM lead. Blood cultures were collected without growth, so a cell-free DNA Karius test was performed, which can detect over 1000 pathogens and has a sensitivity between 87% and 93%. Testing returned positive results for Streptococcus bovis subspecies pasteurianus Given its association with colorectal cancer, abdominal imaging and an endoscopic biopsy were performed, showing recurrent colonic malignancy. The patient underwent a right colon resection prior to cardiac intervention. This report describes the clinical application of the novel cell-free DNA Karius test, which led to the diagnosis of recurrent colon cancer associated with S. pasteurianus endocarditis.

Coccidioidomycosis (Valley Fever), Soil Moisture, and El Nino Southern Oscillation in California and Arizona.

The soil-borne fungal disease coccidioidomycosis (Valley fever) is prevalent across the southwestern United States (US). Previous studies have suggested that the occurrence of this infection is associated with anomalously wet or dry soil moisture states described by the "grow and blow" hypothesis. The growth of coccidioidomycosis is favored by moist conditions both at the surface and in the root zone. A statistical analysis identified two areas in Arizona and central California, with a moderate-to-high number of coccidioidomycosis cases. A Wavelet Transform Coherence (WTC) analysis between El Nino Southern Oscillation (ENSO), coccidioidomycosis cases, surface soil moisture (SSM; 0 to 5 cm) from European Space Agency-Climate Change Initiative (ESA-CCI), and shallow root zone soil moisture (RZSM; 0 to 40 cm depth) from Soil MERGE (SMERGE) was executed for twenty-four CA and AZ counties. In AZ, only SSM was modulated by ENSO. When case values were adjusted for overreporting between 2009 to 2012, a moderate but significant connection between ENSO and cases was observed at a short periodicity (2.1 years). In central CA, SSM, RZSM, and cases all had a significant link to ENSO at longer periodicities (5-to-7 years). This study provides an example of how oceanic-atmospheric teleconnections can impact human health.

Global challenges need attention now: educating humanity for wellness and sustainability.

This Editorial sets the stage for 18 papers on the theme, Contemplative inquiry, wellbeing and science education. The special issue consists of a diverse set of papers that complement one another while each contributes in unique ways that will stimulate reflexive practices among the science education community as they ponder how they can contribute to the resolution of global challenges that define our lifetimes. In this Editorial we address priorities for improved science literacy for a worldwide community so that moment-to-moment practices can reduce the world's carbon footprint and reverse global warming and related challenges such as species extinction. We posit that to meet particulars of global challenges facing humanity, science educators need to expand their roles and definitions of science education. Accordingly, in an era of COVID-19, there is an imperative to expand and deepen functional literacy in science and in particular wellness for everyone as part of a necessary education on the birth through death continuum. Our advocacy for the use of authentic inquiry affords learning from research and ensuring that all research participants have opportunities to learn from ongoing research and one another. We expect that all participants will benefit equitably from being involved in research. Participants are educated about what is learned from research in which they are involved, and how that improves their practices. We anticipate that much of what is learned from such research will be enacted in everyday life, thereby affording those with whom they interact to learn by being-with them. Because of the complex, chaotic and dynamic nature of today's world and its associated lifestyles, we address contemplative activities we regard as highly appropriate for doing science in a manner that is restorative and nurturing for ourselves and for the world. Specifically, we examine breathing meditation, meditating to increase blood oxygenation, walking meditation, loving kindness meditation, soft touch energy work and mindfulness. Each of these contemplative activities relates to knowing thyself better and promoting and sustaining wellness and wellbeing. We address self-help in relation to wellness because so many people follow one or two pathways when they become sick, i.e., they rest until their health improves and/or they go to the doctor who may diagnose/prescribe pharmaceuticals or changes in aspects of lifestyle (e.g., diet, more sleep). We provide examples of self-help practices that allow individuals to be autonomous and proactive in avoiding sickness, and directly addressing symptoms if and when necessary. Finally, we examine the potential of using a dialectical approach to teaching, learning and future rethinking of science education and science educators. Grand challenges of the moment demand no less than ongoing research with transformations that catalyze improvements now. We do not have a luxury of waiting for the right time, and the right time to enact sustainable lifestyles is now.

Retina image analysis and ocular telehealth: the Oak Ridge National Laboratory-Hamilton Eye Institute case study.

Automated retina image analysis has reached a high level of maturity in recent years, and thus the question of how validation is performed in these systems is beginning to grow in importance. One application of retina image analysis is in telemedicine, where an automated system could enable the automated detection of diabetic retinopathy and other eye diseases as a low-cost method for broad-based screening. In this work, we discuss our experiences in developing a telemedical network for retina image analysis, including our progression from a manual diagnosis network to a more fully automated one. We pay special attention to how validations of our algorithm steps are performed, both using data from the telemedicine network and other public databases.

Primary cardiac angiosarcoma: A diagnostic challenge in a young man with recurrent pericardial effusions.

Cardiac angiosarcomas are rare, rapidly progressive tumours that often present as diagnostic dilemmas resulting in delayed diagnosis. They should be considered in patients with recurrent pericardial effusions.A 33-year-old man presented for evaluation of a recurrent pericardial effusion. Infectious and rheumatological workups were negative. Pericardial fluid cytology and pericardial biopsy were unremarkable. Imaging, including echocardiogram and magnetic resonance imaging, were nondiagnostic.While awaiting surgical intervention, the patient developed respiratory failure requiring urgent intubation. Intraoperatively, he experienced significant hemorrhage from the myocardium. Hemostasis could not be achieved and the patient expired. Pathology reports revealed metastatic angiosarcoma.The present case illustrates a rare case of primary cardiac angiosarcoma posing a diagnostic dilemma in a young man. The authors present the challenges in diagnosis, and review the most current diagnostic and therapeutic strategies in the care of patients with this condition.

Exudate-based diabetic macular edema detection in fundus images using publicly available datasets.

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME through the presence of exudation. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publicly available datasets are employed to evaluate our algorithm. We are able to achieve diagnosis performance comparable to retina experts on the MESSIDOR (an independently labelled dataset with 1200 images) with cross-dataset testing (e.g., the classifier was trained on an independent dataset and tested on MESSIDOR). Our algorithm obtained an AUC between 0.88 and 0.94 depending on the dataset/features used. Additionally, it does not need ground truth at lesion level to reject false positives and is computationally efficient, as it generates a diagnosis on an average of 4.4s (9.3s, considering the optic nerve localisation) per image on an 2.6 GHz platform with an unoptimised Matlab implementation.

Detecting flash artifacts in fundus imagery.

In a telemedicine environment for retinopathy screening, a quality check is needed on initial input images to ensure sufficient clarity for proper diagnosis. This is true whether the system uses human screeners or automated software for diagnosis. We present a method for the detection of flash artifacts found in retina images. We have collected a set of retina fundus imagery from February 2009 to August 2011 from several clinics in the mid-South region of the USA as part of a telemedical project. These images have been screened with a quality check that sometimes omits specific flash artifacts, which can be detrimental for automated detection of retina anomalies. A multi-step method for detecting flash artifacts in the center area of the retina was created by combining characteristic colorimetric information and morphological pattern matching. The flash detection was tested on a dataset of 5218 images representative of the population. The system achieved a sensitivity of 96.54% and specificity of 70.16% for the detection of the flash artifacts. The flash artifact detection can serve as a useful tool in quality screening of retina images in a telemedicine network. The detection can be expected to improve automated detection by either providing special handling for these images in combination with a flash mitigation or removal method.

A health insurance portability and accountability act-compliant ocular telehealth network for the remote diagnosis and management of diabetic retinopathy.

In this article, we present the design and implementation of a regional ocular telehealth network for remote assessment and management of diabetic retinopathy (DR), including the design requirements, network topology, protocol design, system work flow, graphics user interfaces, and performance evaluation. The Telemedical Retinal Image Analysis and Diagnosis Network is a computer-aided, image analysis telehealth paradigm for the diagnosis of DR and other retinal diseases using fundus images acquired from primary care end users delivering care to underserved patient populations in the mid-South and southeastern United States.

Textureless macula swelling detection with multiple retinal fundus images.

Retinal fundus images acquired with nonmydriatic digital fundus cameras are versatile tools for the diagnosis of various retinal diseases. Because of the ease of use of newer camera models and their relatively low cost, these cameras can be employed by operators with limited training for telemedicine or point-of-care (PoC) applications. We propose a novel technique that uses uncalibrated multiple-view fundus images to analyze the swelling of the macula. This innovation enables the detection and quantitative measurement of swollen areas by remote ophthalmologists. This capability is not available with a single image and prone to error with stereo fundus cameras. We also present automatic algorithms to measure features from the reconstructed image, which are useful in PoC automated diagnosis of early macular edema, e.g., before the appearance of exudation. The technique presented is divided into three parts: first, a preprocessing technique simultaneously enhances the dark microstructures of the macula and equalizes the image; second, all available views are registered using nonmorphological sparse features; finally, a dense pyramidal optical flow is calculated for all the images and statistically combined to build a naive height map of the macula. Results are presented on three sets of synthetic images and two sets of real-world images. These preliminary tests show the ability to infer a minimum swelling of 300 µm and to correlate the reconstruction with the swollen location.

Stable angina pectoris: what does the current clinical evidence tell us?

Coronary artery disease is the leading cause of death in the United States. The combination of noninvasive cardiovascular testing and invasive cardiac procedures accounts for a substantial portion of the yearly healthcare expenditure in the United States. Although the diagnosis of ischemically-driven chest pain may appear to be simple and straightforward, it often takes an astute clinician to confirm that clinically significant coronary artery blockage is the cause of a patient's chest pain. Cardiovascular research has provided convincing evidence that aggressive treatment of hypertension and hyperlipidemia--along with a management plan, based on the patient's combined risk factor profile, that includes blood glucose assessment, tobacco cessation, weight loss, healthy eating choices, and consistent aerobic exercise--must be provided to achieve optimal care for our patients. Over the ensuing decade, we will likely continue to see a shift away from routine percutaneous treatment of coronary lesions in favor of an aggressive assessment of a patient's cardiac risk profile followed by a treatment plan centered on active patient involvement including appropriate lifestyle changes and selective medications.

3D and multispectral imaging for subcutaneous veins detection.

The first and perhaps most important phase of a surgical procedure is the insertion of an intravenous (IV) catheter. Currently, this is performed manually by trained personnel. In some visions of future operating rooms, however, this process is to be replaced by an automated system. Experiments to determine the best NIR wavelengths to optimize vein contrast for physiological differences such as skin tone and/or the presence of hair on the arm or wrist surface are presented. For illumination our system is composed of a mercury arc lamp coupled to a 10nm band-pass spectrometer. A structured lighting system is also coupled to our multispectral system in order to provide 3D information of the patient arm orientation. Images of each patient arm are captured under every possible combinations of illuminants and the optimal combination of wavelengths for a given subject to maximize vein contrast using linear discriminant analysis is determined.

Automated diagnosis of retinopathy by content-based image retrieval.

PURPOSE: To describe a novel computer-based image analysis method that is being developed to assist and automate the diagnosis of retinal disease. METHODS: Content-based image retrieval is the process of retrieving related images from large database collections using their pictorial content. The content feature list becomes the index for storage, search, and retrieval of related images from a library based upon specific visual characteristics. Low-level analyses use feature description models and higher-level analyses use perceptual organization and spatial relationships, including clinical metadata, to extract semantic information. RESULTS: We defined, extracted, and tested a large number of region- and lesion-based features from a dataset of 395 retinal images. Using a statistical hold-one-out method, independent queries for each image were submitted to the system and a diagnostic prediction was formulated. The diagnostic sensitivity for all stratified levels of age-related macular degeneration ranged from 75% to 100%. Similarly, the sensitivity of detection and accuracy for proliferative diabetic retinopathy ranged from 75% to 91.7% and for nonproliferative diabetic retinopathy, ranged from 75% to 94.7%. The overall purity of the diagnosis (specificity) for all disease states in the dataset was 91.3%. CONCLUSIONS: The probabilistic nature of content-based image retrieval permits us to make statistically relevant predictions regarding the presence, severity, and manifestations of common retinal diseases from digital images in an automated and deterministic manner.

Quantitative phase imaging by three-wavelength digital holography.

Three-wavelength digital holography is applied to obtain surface height measurements over several microns of range, while simultaneously maintaining the low noise precision of the single wavelength phase measurement. The precision is preserved by the use of intermediate synthetic wavelength steps generated from the three wavelengths and the use of hierarchical optical phase unwrapping. As the complex wave-front of each wavelength can be captured simultaneously in one digital image, real-time performance is achievable.

Retina lesion and microaneurysm segmentation using morphological reconstruction methods with ground-truth data.

In this work we report on a method for lesion segmentation based on the morphological reconstruction methods of Sbeh et. al. We adapt the method to include segmentation of dark lesions with a given vasculature segmentation. The segmentation is performed at a variety of scales determined using ground-truth data. Since the method tends to over-segment imagery, ground-truth data was used to create post-processing filters to separate nuisance blobs from true lesions. A sensitivity and specificity of 90% of classification of blobs into nuisance and actual lesion was achieved on two data sets of 86 images and 1296 images.

Using a patient image archive to diagnose retinopathy.

Diabetes has become an epidemic that is expected to impact 365 million people worldwide by 2025. Consequently, diabetic retinopathy is the leading cause of blindness in the industrialized world today. If detected early, treatments can preserve vision and significantly reduce debilitating blindness. Through this research we are developing and testing a method for automating the diagnosis of retinopathy in a screening environment using a patient archive and digital fundus imagery. We present an overview of our content-based image retrieval (CBIR) approach and provide performance results for a dataset of 98 images from a study in Canada when compared to an archive of 1,355 patients from a study in the Netherlands. An aggregate performance of 89% correct diagnosis is achieved, demonstrating the potential of automated, web-based diagnosis for a broad range of imagery collected under different conditions and with different cameras.

Simulation of skin reflectance images using 3D tissue modeling and multispectral Monte Carlo light propagation.

In this work we propose a method to simulate the expected, i.e. seen by a camera, multispectral reflectance images of a large skin surface area by combining Monte Carlo light propagation model and realistic tissue modeling based on three dimensional data acquisition of human body areas. In particular, we aim to simulate more accurately light transport in biological tissue by taking into account the geometrical topography of the skin surface, the structure and optical properties of the skin layers, and the subcutaneous veins in presence. We describe our computation method in detail and present simulated reflectance images results.

Detection of anatomic structures in human retinal imagery.

The widespread availability of electronic imaging devices throughout the medical community is leading to a growing body of research on image processing and analysis to diagnose retinal disease such as diabetic retinopathy (DR). Productive computer-based screening of large, at-risk populations at low cost requires robust, automated image analysis. In this paper we present results for the automatic detection of the optic nerve and localization of the macula using digital red-free fundus photography. Our method relies on the accurate segmentation of the vasculature of the retina followed by the determination of spatial features describing the density, average thickness, and average orientation of the vasculature in relation to the position of the optic nerve. Localization of the macula follows using knowledge of the optic nerve location to detect the horizontal raphe of the retina using a geometric model of the vasculature. We report 90.4% detection performance for the optic nerve and 92.5% localization performance for the macula for red-free fundus images representing a population of 345 images corresponding to 269 patients with 18 different pathologies associated with DR and other common retinal diseases such as age-related macular degeneration.

A probabilistic framework for content-based diagnosis of retinal disease.

Diabetic retinopathy is the leading cause of blindness in the working age population in the industrialized world. Computer assisted analysis has the potential to assist in the early detection of diabetes by regular screening of large populations. The widespread availability of digital fundus cameras today is leading to the accumulation of large image archives of diagnosed patient data that captures historical knowledge of retinal pathology. Through this research we are developing a content-based image retrieval method to verify our hypothesis that retinal pathology can be identified and quantified from visually similar retinal images in an image archive. We will present diagnostic results for specificity and sensitivity on a population of 395 fundus images representing the normal fundus and 14 stratified disease states.

Locating the optic nerve in retinal images: comparing model-based and Bayesian decision methods.

In this work we compare two methods for automatic optic nerve (ON) localization in retinal imagery. The first method uses a Bayesian decision theory discriminator based on four spatial features of the retina imagery. The second method uses a principal component-based reconstruction to model the ON. We report on an improvement to the model-based technique by incorporating linear discriminant analysis and Bayesian decision theory methods. We explore a method to combine both techniques to produce a composite technique with high accuracy and rapid throughput. Results are shown for a data set of 395 images with 2-fold validation testing.

Application of a cardiac arrest score in patients with sudden death and ST segment elevation for triage to angiography and intervention.

The aim of this study was to test a previously validated, prognostic, cardiac arrest score in patients with ST segment elevation acute myocardial infarction (AMI) who suffered a witnessed cardiac arrest and survived to emergency department admission. A consecutive series constructed retrospectively from a sudden death database (n = 22) of patients with ST segment elevation AMI resuscitated from cardiac arrest underwent angiography and angioplasty of the culprit vessel within 24 hours of presentation. A cardiac arrest score was assigned to each case by explicit criteria present on evaluation. Primary outcomes were survival to hospital discharge and the degree of neurological recovery during the hospitalization. All patients underwent successful coronary angioplasty and 77% received adjunctive intraaortic balloon counterpulsation. The overall rate of survival to discharge was 41%. For cardiac arrest scores of 0, 1, 2, and 3, respectively, the rates of neurologic recovery were 0 (0%) of 4 (95% CI 0-53%), 3 (50%) of 6 (95% CI 15-85%), 2 (67%) of 3 (95% CI 13-98%), and 9 (100%) of 9 (95% CI 72-100%), and the rates of survival to discharge were 0 (0%) of 4, (95% CI 0-53%), 2 (33%) of 6 (95% CI 6-74%), 2 (67%) of 3 (95% CI 13-98%), and 9 (100%) of 9 (95% CI 72-100%), P < 0.01 for both outcomes over ascending scores. These results suggest appropriate patients for primary angioplasty after cardiac arrest are those with ST segment elevation AMI and an emergency department cardiac arrest score of > or = 2, thus predicting a 11 (92%) of 12 (95% CI 65-100%) chance of survival to discharge.