Covid-19 Detection Using AI

The sudden increase in COVID-19 patients is a major shock to our global health care systems. With limited availability of test kits, it is not possible for all patients with respiratory infections to be tested using RT-PCR. Testing also takes a long time, with limited sensitivity. The detection of COVID-19 infections on Chest X-Ray can help isolate patients at high risk while awaiting test results. X-Ray machines are already available in many health care systems, and with many modern X-Ray systems already installed on the computer, there is no travel time involved in the samples. In this work we propose the use of chest X-Ray to prioritize the selection of patients for further RT-PCR testing. This can be useful in a hospital setting where current systems have difficulty deciding whether to keep the patient in the ward with other patients or isolate them from COVID-19 areas. It may also be helpful in identifying patients with high risk of COVID with false positive RT-PCR that will require repeated testing. In addition, we recommend the use of modern AI techniques to detect COVID-19 patients who use X-Ray imaging in an automated manner, especially in areas where radiologists are not available, and help make the proposed diagnostic technology easier. Introducing the CovidAID: COVID-19 AI Detector, a model based on a deep neural network of screening patients for proper diagnosis. In a publicly available covid-chest x-ray-dataset [2], our model provides 90.5% accuracy with 100% sensitivity (remember) to COVID-19 infection. We are greatly improving the results of Covid-Net [10] on the same database.

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RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray

Scientific Reports ◽

10.1038/s41598-021-87994-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Saman Motamed ◽

Patrik Rogalla ◽

Farzad Khalvati

Keyword(s):

Health Care ◽

Medical Images ◽

Health Care Systems ◽

Generative Adversarial Networks ◽

X Rays ◽

Generative Adversarial Network ◽

X Ray ◽

Adversarial Network ◽

Care Systems ◽

Chest X Ray

AbstractCOVID-19 spread across the globe at an immense rate and has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results for detection of COVID-19 from viral bacterial pneumonia in chest X-rays. Automation of COVID-19 testing using medical images can speed up the testing process of patients where health care systems lack sufficient numbers of the reverse-transcription polymerase chain reaction tests. Supervised deep learning models such as convolutional neural networks need enough labeled data for all classes to correctly learn the task of detection. Gathering labeled data is a cumbersome task and requires time and resources which could further strain health care systems and radiologists at the early stages of a pandemic such as COVID-19. In this study, we propose a randomized generative adversarial network (RANDGAN) that detects images of an unknown class (COVID-19) from known and labelled classes (Normal and Viral Pneumonia) without the need for labels and training data from the unknown class of images (COVID-19). We used the largest publicly available COVID-19 chest X-ray dataset, COVIDx, which is comprised of Normal, Pneumonia, and COVID-19 images from multiple public databases. In this work, we use transfer learning to segment the lungs in the COVIDx dataset. Next, we show why segmentation of the region of interest (lungs) is vital to correctly learn the task of classification, specifically in datasets that contain images from different resources as it is the case for the COVIDx dataset. Finally, we show improved results in detection of COVID-19 cases using our generative model (RANDGAN) compared to conventional generative adversarial networks for anomaly detection in medical images, improving the area under the ROC curve from 0.71 to 0.77.

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An Evaluation of Statin Use Among Patients with Type 2 Diabetes at High Risk of Cardiovascular Events Across Multiple Health Care Systems

Journal of Managed Care & Specialty Pharmacy ◽

10.18553/jmcp.2020.26.9.1090 ◽

2020 ◽

Vol 26 (9) ◽

pp. 1090-1098

Author(s):

Ralph Ward ◽

Erin R. Weeda ◽

Kinfe G. Bishu ◽

R. Neal Axon ◽

David J. Taber ◽

...

Keyword(s):

Type 2 Diabetes ◽

Health Care ◽

High Risk ◽

Cardiovascular Events ◽

Health Care Systems ◽

Care Systems ◽

Multiple Health ◽

Statin Use

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A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph182212191 ◽

2021 ◽

Vol 18 (22) ◽

pp. 12191

Author(s):

Prabhjot Kaur ◽

Shilpi Harnal ◽

Rajeev Tiwari ◽

Fahd S. Alharithi ◽

Ahmed H. Almulihi ◽

...

Keyword(s):

Health Care Systems ◽

Svm Classifier ◽

Detection Accuracy ◽

Short Supply ◽

X Ray ◽

Proposed Model ◽

Long Time ◽

Care Systems ◽

Chest X Ray ◽

Multiclass Svm

COVID-19 declared as a pandemic that has a faster rate of infection and has impacted the lives and the country’s economy due to forced lockdowns. Its detection using RT-PCR is required long time and due to which its infection has grown exponentially. This creates havoc for the shortage of testing kits in many countries. This work has proposed a new image processing-based technique for the health care systems named “C19D-Net”, to detect “COVID-19” infection from “Chest X-Ray” (XR) images, which can help radiologists to improve their accuracy of detection COVID-19. The proposed system extracts deep learning (DL) features by applying the InceptionV4 architecture and Multiclass SVM classifier to classify and detect COVID-19 infection into four different classes. The dataset of 1900 Chest XR images has been collected from two publicly accessible databases. Images are pre-processed with proper scaling and regular feeding to the proposed model for accuracy attainments. Extensive tests are conducted with the proposed model (“C19D-Net”) and it has succeeded to achieve the highest COVID-19 detection accuracy as 96.24% for 4-classes, 95.51% for three-classes, and 98.1% for two-classes. The proposed method has outperformed well in expressions of “precision”, “accuracy”, “F1-score” and “recall” in comparison with most of the recent previously published methods. As a result, for the present situation of COVID-19, the proposed “C19D-Net” can be employed in places where test kits are in short supply, to help the radiologists to improve their accuracy of detection of COVID-19 patients through XR-Images.

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A predictive model of pcsk-9 inhibitors eligibility in coronary and dyslipidemic patients

European Heart Journal ◽

10.1093/ehjci/ehaa946.2990 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

I Dima ◽

D Soulis ◽

D Terentes-Printzios ◽

I Skoumas ◽

K Aznaouridis ◽

...

Keyword(s):

Health Care ◽

Cardiovascular Risk ◽

High Risk ◽

Predictive Model ◽

Health Care Systems ◽

Clinical Status ◽

Risk Groups ◽

Funding Source ◽

Private Company ◽

Care Systems

Abstract Purpose Dyslipidemia is a major cardiovascular risk factor and treatment is mostly based on statins and ezetimibe. PCSK-9 inhibitors are monoclonal antibodies that reduce LDL-c levels and have shown significant reduction of cardiovascular risk in high risk patients. Data regarding potential eligibility for PCSK-9, is limited especially when referring to the recent guidelines. Methods Eligibility was calculated using a proprietary adjustable software, which stores data and patient information and thus by using different criteria it can determine potential candidates for PCSK-9 inhibitors. For this purpose, 2000 patients were enrolled prospectively. Our study population was comprised of inpatients diagnosed either with acute coronary syndromes (ACS) or with chronic coronary disease (cCAD) and outpatients from Lipids' Clinic (OLC) (n=407, n=1087, n=506, respectively). In order to test eligibility, three different LDL thresholds were used in our model for high and very high risk groups: a) 70mg/dl and 55mg/dl, respectively, as recommended by the recently updated 2019 ESC/EAS Guidelines for Dyslipidaemia b) 100mg/dl and 70mg/dl, respectively, as recommended by the 2016 ESC/EAS Guidelines for Dyslipidaemias and c) 130mg/dl and 100mg/dl respectively, as mandated by our National Health Care system but also applicable in other countries. Results The eligible percentages for the three thresholds were 18.85%, 9.75% and 2.15%, in the total population (TP) respectively and it varied according to clinical status. Subgroup analysis of eligible population revealed the trends in each group (Figure 1). The increase toward more recent guidelines was mostly attributed to the increasing number of coronary patients who become eligible as our criteria become stricter. Conclusions Our predictive model provides a realistic estimation of PCSK-9 inhibitors potential eligibility in coronary and dyslipidaemic patients and thus it can become a useful tool for the use of PCSK-9 in health care systems. Figure 1 Funding Acknowledgement Type of funding source: Private company. Main funding source(s): Amgen Hellas LTD

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Artificial Intelligence in the Fight Against COVID-19 in the Banking Sector

Advances in Machine Learning & Artificial Intelligence ◽

10.33140/amlai.01.01.02 ◽

2020 ◽

Vol 1 (1) ◽

Keyword(s):

Artificial Intelligence ◽

Health Care ◽

Decision Making ◽

High Risk ◽

Health Care System ◽

Banking Sector ◽

Health Care Systems ◽

Use Cases ◽

Care Systems ◽

Human Thinking

The COVID-19 pandemic has been causing a massive strain in different sectors around the globe, especially in the health care systems in many countries. Artificial Intelligence has found its way in the health care system in helping to find a cure or vaccine by screening out medicines that could be promising for cure. Not only that but by containing the virus and predicting highly effected areas and limiting the spread of the virus. Many use cases based on AI was successful to monitor the spread and lock areas that were predicted by AI algorithms to be at high risk. Broadly speaking, AI involves ‘the ability of machines to emulate human thinking, reasoning and decision - making.

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Comprehensive Treatment of Women with Postpartum Psychosis across Health Care Systems from Swedish Psychiatrists' Perspectives

The Qualitative Report ◽

10.46743/2160-3715/2011.1039 ◽

2014 ◽

Author(s):

Inger Engqvist ◽

Arne Åhlin ◽

Ginette Ferszt ◽

Kerstin Nilsson

Keyword(s):

Health Care ◽

High Risk ◽

Health Care Organizations ◽

Health Care Systems ◽

Psychiatric Hospitals ◽

Postpartum Psychosis ◽

Comprehensive Treatment ◽

Structured Interviews ◽

Peer Supervision ◽

Care Systems

Studies concerning the psychiatrist's experiences of treating women with postpartum psychosis (PPP) or how they react to these women are limited in the literature. In this study a qualitative design is used. Data collection includes semi-structured interviews with nine Swedish psychiatrists working in psychiatric hospitals. The audio-taped interviews are transcribed verbatim and analyzed using content analysis. The findings consist of the categories: Protection, Treatment, Care, and Reactions. The psychiatrists describe emotions such as compassion, empathy and distress. A conclusion is that the psychiatrists focus on protecting the women from suicide and/or infanticide. Given the degree of stress the psychiatrists can experience caring for high risk challenging patients, health care organizations need to provide support and/or opportunities for peer supervision.

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