Suśruta: Artificial Intelligence and Bayesian Knowledge Network in Health Care – Smartphone Apps for Diagnosis and Differentiation of Anemias with Higher Accuracy at Resource Constrained Point-of-Care Settings

2019 ◽  
pp. 159-175 ◽  
Author(s):  
Shubham Yadav ◽  
Sakthi Ganesh ◽  
Debanjan Das ◽  
U. Venkanna ◽  
Rajarshi Mahapatra ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Point Of Care ◽  
Knowledge Network ◽  
Smartphone Apps ◽  
Resource Constrained

scholarly journals Performance evaluation of the novel portable PIMA point-of-care CD4 analyzer in comparison with conventional flow cytometer instruments in Cameroon

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Bertrand Sagnia ◽  
Rachel Kamgaing ◽  
Charles Kouanfack ◽  
Georgette Kamdem ◽  
Samuel Sosso ◽  
...  
Keyword(s):  
Health Care ◽  
T Lymphocytes ◽  
T Lymphocyte ◽  
Point Of Care ◽  
Low Cost ◽  
Urban Environments ◽  
Resource Constrained ◽  
Becton Dickinson ◽  
Inform Consent ◽  
Rural And Urban

Abstract Background Absolute CD4+ T-lymphocyte counts are used in the initiation and monitoring of antiretroviral therapy in HIV-infected patients: with the increase number of HIV infected patient and the reduce number of heath care system in rural zones, HIV monitoring in resource-constrained settings demands affordable and reliable CD4+ T lymphocytes enumeration methods. We evaluated a simple PIMA POC which is a dedicated system for enumeration that uses immunomagnetic and immunofluorescent technologies. The instrument was designed to be a low-cost, yet reliable and robust one. In this study, we assessed the correlation between most representative flow cytometry instruments present in Cameroon instead of CyFlow from PARTEC, FACSCount, and FACSCalibur both from Becton Dickinson. Methods CD4 surface markers on lymphocytes was measured on samples collected in EDTA tubes from 268 patients aged from 1 to 65 years old in three different health care structures. HIV infected patients are coming from CIRCB, Day Hospital of Hopital Central de Yaounde (HCY) and Hopital General de Yaounde (HGY). After inform consent, samples were collected and 101 samples were tested with the FACSCalibur, 60 samples were tested with the CyFlow and 107 samples were tested with the FACSCount flow cytometers. All these samples were tested by different technician with PIMA POC present in all these health care structures and the correlation and agreement were analyzed using linear regression and Bland–Altman analysis. Results The PIMA POC system has excellent precision, accuracy and linearity for CD4+ T lymphocytes enumeration. Good correlations were obtained between the PIMA POC system and other single platform methods. Bland–Altman plots showed interchangeability between the three machines. Absolute CD4+ T-lymphocyte values obtained from the PIMA system correlated well with Cyflow, FACSCount, and FACSCalibur method (r2 varies from 0.88 to 0.968, P < 0.0001). The comparison between values obtained from PIMA with CYFLOW, FACSCount, and FACSCalibur give P = 0.17, P = 0.5 and P = 0.6 respectively meaning that there is not significant differences between values obtained with PIMA and other flow machines. Conclusion This POC PIMA system is a simple and reliable system for enumeration of absolute CD4+ T-lymphocytes. Having one PIMA system easy to use, should reduce the cost and thus increase access to CD4 testing for HIV infected patients in resource-constrained countries. POC CD4 may also alleviate testing burdens at traditional central CD4 laboratories, hence improving test access in both rural and urban environments. This will reduce also the loss of follow up.

scholarly journals Performance Evaluation of the Novel Portable PIMA Point-Of-Care CD4 Analyzer in Comparison with Conventional Flow Cytometer Instruments in Cameroon

2020 ◽  
Author(s):  
Bertrand Sagnia ◽  
Rachel Kamgaing ◽  
Charles Kouanfack ◽  
Georgette Kamdem ◽  
Samuel Sosso ◽  
...  
Keyword(s):  
Health Care ◽  
T Lymphocytes ◽  
T Lymphocyte ◽  
Point Of Care ◽  
Low Cost ◽  
Urban Environments ◽  
Resource Constrained ◽  
Becton Dickinson ◽  
Inform Consent ◽  
Rural And Urban

Abstract Background: Absolute CD4 + T-lymphocyte counts are used in the initiation and monitoring of antiretroviral therapy in HIV-infected patients: with the increase number of HIV infected patient and the reduce number of heath care system in rural zones, HIV monitoring in resource-constrained settings demands affordable and reliable CD4 + T lymphocytes enumeration methods. We evaluated a simple PIMA POC which is a dedicated system for enumeration that uses immunomagnetic and immunofluorescent technologies. The instrument was designed to be a low-cost, yet reliable and robust one. In this study, we assessed the correlation between most representative flow cytometry instruments present in Cameroon instead of CyFlow from PARTEC, FACSCount, and FACSCalibur both from Becton DickinsonMethods: CD4 surface markers on lymphocytes was measured on samples collected in EDTA tubes from 268 patients aged from 1 to 65 years old in three different health care structures. HIV infected patients are coming from CIRCB, Day Hospital of Hopital Central de Yaounde (HCY) and Hopital General de Yaounde (HGY). After inform consent, samples were collected and 101 samples were tested with the FACSCalibur, 60 samples were tested with the CyFlow and 107 samples were tested with the FACSCount flow cytometers. All these samples were tested by different technician with PIMA POC present in all these health care structures and the correlation and agreement were analyzed using linear regression and Bland–Altman analysis.Results: The PIMA POC system has excellent precision, accuracy and linearity for CD4 + T lymphocytes enumeration. Good correlations were obtained between the PIMA POC system and other single platform methods. Bland–Altman plots showed interchangeability between the three machines. Absolute CD4 + T-lymphocyte values obtained from the PIMA system correlated well with Cyflow, FACSCount, and FACSCalibur method (r2 varies from 0.88 to 0.968, P < 0.0001). The comparison between values obtained from PIMA with CYFLOW, FACSCount, and FACSCalibur give P = 0.17, P = 0.5 and P = 0.6 respectively meaning that there is not significant differences between values obtained with PIMA and other flow machines.Conclusion: This POC PIMA system is a simple and reliable system for enumeration of absolute CD4 + T-lymphocytes. Having one PIMA system easy to use, should reduce the cost and thus increase access to CD4 testing for HIV infected patients in resource-constrained countries. POC CD4 may also alleviate testing burdens at traditional central CD4 laboratories, hence improving test access in both rural and urban environments. This will reduce also the loss of follow up

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

“PHONE-CONNECT” - A Pilot Program Prescribing Phones to Address Digital Health Equity Needs in the COVID-19 Era (Preprint)

2020 ◽  
Author(s):  
Gill Kazevman ◽  
Marck Mercado ◽  
Jennifer Hulme ◽  
Andrea Somers
Keyword(s):  
Public Health ◽  
Health Care ◽  
Social Services ◽  
Access To Health Care ◽  
Health Care Services ◽  
Point Of Care ◽  
Digital Health ◽  
Contact Tracing ◽  
Health Care Intervention ◽  
Care Intervention

UNSTRUCTURED Vulnerable populations have been identified as having higher infection rates and poorer COVID-19 related outcomes, likely due to their inability to readily access primary care, follow public health directives and adhere to self-isolation guidelines. As a response to the COVID-19 pandemic, many health care services have adopted new digital solutions, relying on phone and internet connectivity. Yet, persons who are digitally inaccessible, such as those struggling with poverty or homelessness, are often unable to utilize these services. In response to this newly highlighted social disparity known as “digital health inequity”, emergency physicians at the University Health Network, Toronto, initiated a program called “PHONE CONNECT”. This novel approach attempts to improve patients’ access to health care, information and social services, as well as improve their ability to adhere to public health directives (social isolation and contact tracing). While similar programs addressing the same emerging issues have been recently described in the media, this is the first time phones are provided as a health care intervention in an emergency department. This innovative ED point-of-care intervention may have a significant impact on improving the health outcomes for vulnerable people during the COVID-19 pandemic, and even beyond it.

scholarly journals How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Medical Data ◽  
General Process ◽  
New Era ◽  
Automated Evaluation ◽  
Machine Learning Applications ◽  
And Training ◽  
Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

scholarly journals 75 Demonstrating leadership in health care: utilising artificial intelligence to improve NCD outcomes

2020 ◽  
Author(s):  
Anurita Majumdar ◽  
Kaushal Shetty ◽  
Kannan Subramaniam
Keyword(s):  
Artificial Intelligence ◽  
Health Care

scholarly journals Thoracic Point-of-Care Ultrasound: A SARS-CoV-2 Data Repository for Future Artificial Intelligence and Machine Learning

2021 ◽  
pp. 155335062110186
Author(s):  
Abdel-Moneim Mohamed Ali ◽  
Emran El-Alali ◽  
Adam S. Weltz ◽  
Scott T. Rehrig
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Point Of Care ◽  
Prognostic Indicators ◽  
Data Repository ◽  
Point Of Care Ultrasound ◽  
Ultrasound Images ◽  
Clinical Protocol ◽  
Improved Outcomes ◽  
Current Experience

Current experience suggests that artificial intelligence (AI) and machine learning (ML) may be useful in the management of hospitalized patients, including those with COVID-19. In light of the challenges faced with diagnostic and prognostic indicators in SARS-CoV-2 infection, our center has developed an international clinical protocol to collect standardized thoracic point of care ultrasound data in these patients for later AI/ML modeling. We surmise that in the future AI/ML may assist in the management of SARS-CoV-2 patients potentially leading to improved outcomes, and to that end, a corpus of curated ultrasound images and linked patient clinical metadata is an invaluable research resource.

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