Global Disease Detection Program

In 2010, the GDD program helped coordinate a CDC response to several high-profile and important outbreaks including cholera in Haiti (see page 13), Nodding Syndrome in Uganda (see page 17), and lead poisoning in Nigeria. These outbreaks showed us that emerging health threats remain a complicated reality, yet they also highlight the value of our robust network of partners, which is essential for rapidly responding to outbreaks. Several examples of the contributions and partnerships of the GDD program are highlighted throughout this report.Foreword (Letter from the Division Director; Letter from the Global Disease Detection Branch Chief) -- Overview (Global Disease Detection (GDD) Program; GDD broadens coverage; Monitoring and evaluation) -- GDD Activities (Outbreak response; Pathogen discovery; Training; Surveillance; Networking) -- Appendix: Core capacities of GDD Regional CentersCS218664A

Global Disease Detection and Emergency Response activities at CDC 2012

Global disease detection and emergency response has always been a core public health activity for CDC, ensuring the public health security of Americans and others around the world. Over the past year, the agency continued to formalize this by centralizing activities in the Division of Global Disease Detection and Emergency Response (DGDDER) that now span all facets of global health security. Our founding Global Disease Detection Program (GDD), was established in 2004 to promote global health security by building capacity to rapidly detect and contain emerging health threats. In the last eight years, the GDD Program has grown by establishing seven GDD Regional Centers and three GDD Regional Centers under development. With the formation of CDC's Center for Global Health in 2010, DGDDER was established not just to include GDD but also International Emergency and Refugee Health, Global Health Security, and Health Systems Reconstruction programs. This structure has led to increased collaborations among the programs as well as an increased network of partnerships throughout CDC. In 2011, CDC coordinated and contributed to several high-profile and important responses including cholera in Haiti, famine in the Horn of Africa, and outbreaks of typhoid, Ebola, and dengue fever. In addition, CDC programs worked together to build host country capacity for global health security and the International Health Regulations (IHR), including developing surveillance and epidemiologic systems, strengthening laboratories, and increasing public health workforce overseas. The GDD Regional Centers have also continued to provide key field support and leadership for global disease threat and response, as highlighted by the 2011 monitoring and evaluation data in this report. These events and outbreaks of 2011 reminded us that emerging health threats and humanitarian emergencies remain a complicated reality, yet they also highlight the value of our robust network of partners, which is essential for building capacity and rapidly responding to events. As the World Health Organization (WHO) Collaborating Center for Implementation of IHR National Surveillance and Response Capacity, and through ongoing work with our host country partners, we are making important progress towards building health security globally. In coordination with WHO, U.S. Agency for International Development, U.S. Department of State, U.S. Department of Defense, and others, CDC is strategically placing scientific expertise and resources in each of the WHO regions to build and strengthen national public health core capacities in host countries and throughout the region. Increased collaboration between CDC programs and global partner networks has meant that our efforts in 2011 were stronger and more meaningful than ever.Foreword -- Introduction -- Building capacity -- Monitoring and detecting threats -- Responding to international emergencies -- Reconstructing public health systems -- Appendix A: Table of GDD accomplishments: 2011 and cumulative.August 2012.CS234416A.Available via the World Wide Web as an Acrobat .pdf file (5.63 MB, 36 p.)

SSM-Net for Plants Disease Identification in Low Data Regime

Plant disease detection is an essential factor in increasing agricultural production. Due to the difficulty of disease detection, farmers spray various pesticides on their crops to protect them, causing great harm to crop growth and food standards. Deep learning can offer critical aid in detecting such diseases. However, it is highly inconvenient to collect a large volume of data on all forms of the diseases afflicting a specific plant species. In this paper, we propose a new metrics-based few-shot learning SSM net architecture, which consists of stacked siamese and matching network components to address the problem of disease detection in low data regimes. We demonstrated our experiments on two datasets: mini-leaves diseases and sugarcane diseases dataset. We have showcased that the SSM-Net approach can achieve better decision boundaries with an accuracy of 92.7% on the mini-leaves dataset and 94.3% on the sugarcane dataset. The accuracy increased by ~10% and ~5% respectively, compared to the widely used VGG16 transfer learning approach. Furthermore, we attained F1 score of 0.90 using SSM Net on the sugarcane dataset and 0.91 on the mini-leaves dataset. Our code implementation is available on Github: https://github.com/shruti-jadon/PlantsDiseaseDetection.Comment: 5 pages, 7 Figure

Disease detection in citrus crops using optical and thermal remote sensing: a literature review.

Brazil stands out in the international citrus trade, especially due to its oranges, having produced around 16 million tons in 2021. However, productivity could be increased with greater control of diseases such as greening, which has spread around the world and leads to the total loss of affected trees. Given this scenario, it is necessary to perform fast and accurate detections in order to better manage actions and inputs. Since remote sensing is a pillar of digital agriculture, a literature review was carried out to analyze the use of optical and thermal sensors for the detection of diseases that affect citrus groves. For this purpose, the international databases Scopus and Web of Science were used to select references published between 2012 and 2022, resulting in twelve studies - most from China or the United States of America. The results showed a prevalence of methodologies that combine bands and spectral indices obtained through the use of multispectral and hyperspectral sensors, predominantly on board unmanned aircrafts (UAVs). Machine learning (ML) and deep learning (DL) classification algorithms produced good results in the detection of citrus groves affected by diseases, mainly greening. These results are affected by the stage of the infection, the presence or absence of symptoms, and the spectral and spatial resolutions of the sensors: the Red-Edge band and data with higher spatial detail result in more accurate classification models. However, the analyzed literature is still inconclusive regarding the early detection of infected plants

Parkinsons Disease Detection by using Isosurfaces with Convolutional Neural Networks

Computer aided diagnosis systems based on brain imaging are an important tool to assist in the diagnosis of Parkinson’s disease. The ultimate goal would be detec- tion by automatic recognizing of patterns that characterize the disease. In recent times Convolutional Neural Networks (CNN) have proved to be amazingly useful for that task. The drawback, however, is that 3D brain images contains a huge amount of information that leads to complex CNN architectures. When these architectures become too complex, classification performances often degrades be- cause the limitations of the training algorithm and overfitting. Thus, this paper proposes the use of isosurfaces as a way to reduce such amount of data while keeping the most relevant information. These isosurfaces are then used to im- plement a classification system which uses two of the most well-known CNN architectures to classify DaTScan images with an average accuracy of 95.1% and AUC=97%, obtaining comparable (slightly better) values to those obtained for most of the recently proposed systems. It can be concluded therefore that the computation of isosurfaces reduces the complexity of the inputs significantly, resulting in high classification accuracies with reduced computa- tional burden.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Building Disease Detection Algorithms with Very Small Numbers of Positive Samples

Although deep learning can provide promising results in medical image analysis, the lack of very large annotated datasets confines its full potential. Furthermore, limited positive samples also create unbalanced datasets which limit the true positive rates of trained models. As unbalanced datasets are mostly unavoidable, it is greatly beneficial if we can extract useful knowledge from negative samples to improve classification accuracy on limited positive samples. To this end, we propose a new strategy for building medical image analysis pipelines that target disease detection. We train a discriminative segmentation model only on normal images to provide a source of knowledge to be transferred to a disease detection classifier. We show that using the feature maps of a trained segmentation network, deviations from normal anatomy can be learned by a two-class classification network on an extremely unbalanced training dataset with as little as one positive for 17 negative samples. We demonstrate that even though the segmentation network is only trained on normal cardiac computed tomography images, the resulting feature maps can be used to detect pericardial effusion and cardiac septal defects with two-class convolutional classification networks

Iteratively Optimized Patch Label Inference Network for Automatic Pavement Disease Detection

We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement diseases that are not solely limited to specific ones, such as cracks and potholes. IOPLIN can be iteratively trained with only the image label via the Expectation-Maximization Inspired Patch Label Distillation (EMIPLD) strategy, and accomplish this task well by inferring the labels of patches from the pavement images. IOPLIN enjoys many desirable properties over the state-of-the-art single branch CNN models such as GoogLeNet and EfficientNet. It is able to handle images in different resolutions, and sufficiently utilize image information particularly for the high-resolution ones, since IOPLIN extracts the visual features from unrevised image patches instead of the resized entire image. Moreover, it can roughly localize the pavement distress without using any prior localization information in the training phase. In order to better evaluate the effectiveness of our method in practice, we construct a large-scale Bituminous Pavement Disease Detection dataset named CQU-BPDD consisting of 60,059 high-resolution pavement images, which are acquired from different areas at different times. Extensive results on this dataset demonstrate the superiority of IOPLIN over the state-of-the-art image classification approaches in automatic pavement disease detection. The source codes of IOPLIN are released on \url{https://github.com/DearCaat/ioplin}.Comment: Revision on IEEE Trans on IT

Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery

The electrocardiogram or ECG has been in use for over 100 years and remains the most widely performed diagnostic test to characterize cardiac structure and electrical activity. We hypothesized that parallel advances in computing power, innovations in machine learning algorithms, and availability of large-scale digitized ECG data would enable extending the utility of the ECG beyond its current limitations, while at the same time preserving interpretability, which is fundamental to medical decision-making. We identified 36,186 ECGs from the UCSF database that were 1) in normal sinus rhythm and 2) would enable training of specific models for estimation of cardiac structure or function or detection of disease. We derived a novel model for ECG segmentation using convolutional neural networks (CNN) and Hidden Markov Models (HMM) and evaluated its output by comparing electrical interval estimates to 141,864 measurements from the clinical workflow. We built a 725-element patient-level ECG profile using downsampled segmentation data and trained machine learning models to estimate left ventricular mass, left atrial volume, mitral annulus e' and to detect and track four diseases: pulmonary arterial hypertension (PAH), hypertrophic cardiomyopathy (HCM), cardiac amyloid (CA), and mitral valve prolapse (MVP). CNN-HMM derived ECG segmentation agreed with clinical estimates, with median absolute deviations (MAD) as a fraction of observed value of 0.6% for heart rate and 4% for QT interval. Patient-level ECG profiles enabled quantitative estimates of left ventricular and mitral annulus e' velocity with good discrimination in binary classification models of left ventricular hypertrophy and diastolic function. Models for disease detection ranged from AUROC of 0.94 to 0.77 for MVP. Top-ranked variables for all models included known ECG characteristics along with novel predictors of these traits/diseases.Comment: 13 pages, 6 figures, 1 Table + Supplemen