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  1. Book ; Online ; E-Book: Deep learning models for medical imaging

    Santosh, K. C. / Das, Nibaran / Ghosh, Swarnendu

    (Primers in Biomedical Imaging Devices and Systems)

    2021  

    Author's details K. C. Santosh, Nibaran Das, Swarnendu Ghosh
    Series title Primers in Biomedical Imaging Devices and Systems
    Keywords Artificial intelligence/Medical applications ; Diagnostic imaging
    Subject code 610.28563
    Language English
    Size 1 online resource (172 pages)
    Publisher Elsevier
    Publishing place London
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 0-12-823650-7 ; 0-12-823504-7 ; 978-0-12-823650-5 ; 978-0-12-823504-1
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Book ; Online ; E-Book: COVID-19: prediction, decision-Making, and its impacts

    Santosh, K. C. / Joshi, Amit Umesh

    (Lecture notes on data engineering and communications technologies ; 60)

    2021  

    Author's details K. C. Santosh, Amit Joshi editors
    Series title Lecture notes on data engineering and communications technologies ; 60
    Collection
    Keywords Computational intelligence ; Artificial intelligence ; Health informatics ; Machine learning ; Technology—Sociological aspects
    Subject code 006.3
    Language English
    Size 1 Online-Ressource (xii, 137 Seiten), Illustrationen, Diagramme
    Publisher Springer
    Publishing place Singapore
    Publishing country Singapore
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    HBZ-ID HT020697681
    ISBN 978-981-15-9682-7 ; 9789811596810 ; 9789811596834 ; 981-15-9682-4 ; 9811596816 ; 9811596832
    DOI 10.1007/978-981-15-9682-7
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  3. Article ; Online: Arachidonic acid inhibits Na⁺-K⁺-ATPase via cytochrome P-450, lipoxygenase and protein kinase C-dependent pathways in sheep pulmonary artery.

    Singh, Thakur Uttam / Choudhury, Soumen / Parida, Subhashree / Maruti, Bhojane Somnath / Mishra, Santosh Kumar

    Vascular pharmacology

    2012  Volume 56, Issue 1-2, Page(s) 84–90

    Abstract: ... It is also evident that protein kinase C is involved in the inhibition of Na(+)-K(+)-ATPase ... The purpose of the study was to examine whether arachidonic acid inhibits vascular Na(+)-K ... ATPase in pulmonary vasculature and if so, what are the mechanisms involved. Functional Na(+)-K(+)-ATPase ...

    Abstract The purpose of the study was to examine whether arachidonic acid inhibits vascular Na(+)-K(+)-ATPase in pulmonary vasculature and if so, what are the mechanisms involved. Functional Na(+)-K(+)-ATPase activity was studied in terms of K(+)-induced relaxation in sheep pulmonary arterial rings contracted with K(+)-free solution and 5-HT. Arachidonic acid (10-100 μM) caused concentration-dependent inhibition of KCl-induced relaxations and also increased basal arterial tone. Cytochrome P-450 inhibitor, 17-octadecynoic acid (17-ODYA) completely reversed the arachidonic acid (30 μM)-induced inhibition of KCl relaxation. Further, in the presence of HET0016, a selective blocker of 20-hydroxyeicosatetraenoic acid (20-HETE), arachidonic acid-induced inhibition of KCl relaxation was not evident. Accordingly, 20-HETE, a cytochrome P-450 metabolite of arachidonic acid, also significantly attenuated KCl-induced relaxations. Norhydihydroguaiaretic acid (NDGA), a lipoxygenase inhibitor, however, partially restored the relaxation to K(+), impaired in the presence of arachidonic acid (30 μM). On the other hand, cyclooxygenase inhibitor indomethacin failed to reverse the inhibitory effect of arachidonic acid on KCl-induced relaxation. Staurosporin, a protein kinase C inhibitor, completely reversed the inhibitory effect of arachidonic acid and 20-HETE on K(+)-induced relaxation. In conclusion, the results suggest that 20-HETE, a cytochrome P-450 metabolite of arachidonic acid has a predominant role in the inhibition of functional Na(+)-K(+)-ATPase activity in the sheep pulmonary artery, while the lipooxygenase pathway has a secondary role. It is also evident that protein kinase C is involved in the inhibition of Na(+)-K(+)-ATPase by arachidonic acid/20-HETE in sheep pulmonary artery.
    MeSH term(s) Animals ; Arachidonic Acid/pharmacology ; Cytochrome P-450 Enzyme System/metabolism ; Fatty Acids, Unsaturated/pharmacology ; Hydroxyeicosatetraenoic Acids/pharmacology ; Indomethacin/pharmacology ; Lipoxygenase/metabolism ; Lung/blood supply ; Lung/drug effects ; Male ; Potassium/metabolism ; Potassium Chloride/pharmacology ; Prostaglandin-Endoperoxide Synthases/metabolism ; Protein Kinase C/antagonists & inhibitors ; Protein Kinase C/metabolism ; Pulmonary Artery/drug effects ; Pulmonary Artery/enzymology ; Pulmonary Artery/metabolism ; Serotonin/metabolism ; Sheep ; Signal Transduction/drug effects ; Sodium-Potassium-Exchanging ATPase/antagonists & inhibitors ; Sodium-Potassium-Exchanging ATPase/metabolism ; Staurosporine/pharmacology ; Vasodilation/drug effects
    Chemical Substances Fatty Acids, Unsaturated ; Hydroxyeicosatetraenoic Acids ; Arachidonic Acid (27YG812J1I) ; Serotonin (333DO1RDJY) ; 17-octadecynoic acid (34450-18-5) ; Potassium Chloride (660YQ98I10) ; 20-hydroxy-5,8,11,14-eicosatetraenoic acid (79551-86-3) ; Cytochrome P-450 Enzyme System (9035-51-2) ; Lipoxygenase (EC 1.13.11.12) ; Prostaglandin-Endoperoxide Synthases (EC 1.14.99.1) ; Protein Kinase C (EC 2.7.11.13) ; Sodium-Potassium-Exchanging ATPase (EC 3.6.3.9) ; Staurosporine (H88EPA0A3N) ; Potassium (RWP5GA015D) ; Indomethacin (XXE1CET956)
    Language English
    Publishing date 2012-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2082846-9
    ISSN 1879-3649 ; 1537-1891 ; 1879-3649
    ISSN (online) 1879-3649 ; 1537-1891
    ISSN 1879-3649
    DOI 10.1016/j.vph.2011.11.005
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 591: Show issues Location:
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  4. Book ; Online ; E-Book: Intelligent systems and methods to combat Covid-19

    Joshi, Amit Umesh / Dey, Nilanjan / Santosh, K. C.

    (SpringerBriefs in applied sciences and technology: computational intelligence)

    2020  

    Author's details Amit Joshi, Nilanjan Dey, K. C. Santosh editors
    Series title SpringerBriefs in applied sciences and technology: computational intelligence
    Keywords Computational intelligence ; Artificial intelligence ; Health informatics ; Control engineering ; Robotics ; Mechatronics ; Big data
    Language English
    Size 1 Online-Ressource (xii, 91 Seiten), Illustrationen, Diagramme
    Publisher Springer
    Publishing place Singapore
    Publishing country Singapore
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    HBZ-ID HT020570480
    ISBN 978-981-15-6572-4 ; 9789811565717 ; 981-15-6572-4 ; 9811565716
    DOI 10.1007/978-981-15-6572-4
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  5. Article ; Online: Editorial: Current Trends in Image Processing and Pattern Recognition.

    Santosh, K C

    Frontiers in robotics and AI

    2021  Volume 8, Page(s) 785075

    Language English
    Publishing date 2021-12-09
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2781824-X
    ISSN 2296-9144 ; 2296-9144
    ISSN (online) 2296-9144
    ISSN 2296-9144
    DOI 10.3389/frobt.2021.785075
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article: Analyzing Overlaid Foreign Objects in Chest X-rays-Clinical Significance and Artificial Intelligence Tools.

    Roy, Shotabdi / Santosh, K C

    Healthcare (Basel, Switzerland)

    2023  Volume 11, Issue 3

    Abstract: The presence of non-biomedical foreign objects (NBFO), such as coins, buttons and jewelry, and biomedical foreign objects (BFO), such as medical tubes and devices in chest X-rays (CXRs), make accurate interpretation difficult, as they do not indicate ... ...

    Abstract The presence of non-biomedical foreign objects (NBFO), such as coins, buttons and jewelry, and biomedical foreign objects (BFO), such as medical tubes and devices in chest X-rays (CXRs), make accurate interpretation difficult, as they do not indicate known biological abnormalities like excess fluids, tuberculosis (TB) or cysts. Such foreign objects need to be detected, localized, categorized as either NBFO or BFO, and removed from CXR or highlighted in CXR for effective abnormality analysis. Very specifically, NBFOs can adversely impact the process, as typical machine learning algorithms would consider these objects to be biological abnormalities producing false-positive cases. It holds true for BFOs in CXRs. This paper examines detailed discussions on numerous clinical reports in addition to computer-aided detection (CADe) with diagnosis (CADx) tools, where both shallow learning and deep learning algorithms are applied. Our discussion reflects the importance of accurately detecting, isolating, classifying, and either removing or highlighting NBFOs and BFOs in CXRs by taking 29 peer-reviewed research reports and articles into account.
    Language English
    Publishing date 2023-01-19
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2721009-1
    ISSN 2227-9032
    ISSN 2227-9032
    DOI 10.3390/healthcare11030308
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X-rays.

    Makkar, Aaisha / Santosh, K C

    International journal of machine learning and cybernetics

    2023  , Page(s) 1–12

    Abstract: Machine learning is an effective and accurate technique to diagnose COVID-19 infections using image data, and chest X-Ray (CXR) is no exception. Considering privacy issues, machine learning scientists end up receiving less medical imaging data. Federated ...

    Abstract Machine learning is an effective and accurate technique to diagnose COVID-19 infections using image data, and chest X-Ray (CXR) is no exception. Considering privacy issues, machine learning scientists end up receiving less medical imaging data. Federated Learning (FL) is a privacy-preserving distributed machine learning paradigm that generates an unbiased global model that follows local model (from clients) without exposing their personal data. In the case of heterogeneous data among clients, vanilla or default FL mechanism still introduces an insecure method for updating models. Therefore, we proposed SecureFed-a secure aggregation method-which ensures fairness and robustness. In our experiments, we employed COVID-19 CXR dataset (of size 2100 positive cases) and compared it with the existing FL frameworks such as FedAvg, FedMGDA+, and FedRAD. In our comparison, we primarily considered robustness (accuracy) and fairness (consistency). As the SecureFed produced consistently better results, it is generic enough to be considered for multimodal data.
    Language English
    Publishing date 2023-02-14
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2572473-3
    ISSN 1868-808X ; 1868-8071
    ISSN (online) 1868-808X
    ISSN 1868-8071
    DOI 10.1007/s13042-023-01789-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data.

    Santosh, K C

    Journal of medical systems

    2020  Volume 44, Issue 5, Page(s) 93

    Abstract: The novel coronavirus (COVID-19) outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible global threats. Beside clinical procedures and treatments, since Artificial Intelligence (AI) promises a ...

    Abstract The novel coronavirus (COVID-19) outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible global threats. Beside clinical procedures and treatments, since Artificial Intelligence (AI) promises a new paradigm for healthcare, several different AI tools that are built upon Machine Learning (ML) algorithms are employed for analyzing data and decision-making processes. This means that AI-driven tools help identify COVID-19 outbreaks as well as forecast their nature of spread across the globe. However, unlike other healthcare issues, for COVID-19, to detect COVID-19, AI-driven tools are expected to have active learning-based cross-population train/test models that employs multitudinal and multimodal data, which is the primary purpose of the paper.
    MeSH term(s) Algorithms ; Artificial Intelligence ; COVID-19 ; Coronavirus Infections/diagnosis ; Coronavirus Infections/epidemiology ; Decision Making ; Delivery of Health Care ; Disease Outbreaks ; Forecasting ; Humans ; Machine Learning ; Pneumonia, Viral/diagnosis ; Pneumonia, Viral/epidemiology
    Keywords covid19
    Language English
    Publishing date 2020-03-18
    Publishing country United States
    Document type Journal Article
    ZDB-ID 423488-1
    ISSN 1573-689X ; 0148-5598
    ISSN (online) 1573-689X
    ISSN 0148-5598
    DOI 10.1007/s10916-020-01562-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1426: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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  9. Article ; Online: COVID-19 Prediction Models and Unexploited Data.

    Santosh, K C

    Journal of medical systems

    2020  Volume 44, Issue 9, Page(s) 170

    Abstract: ... uncertainties/factors, such as a) hospital settings/capacity; b) test capacity/rate (on a daily basis); c ...

    Abstract For COVID-19, predictive modeling, in the literature, uses broadly SEIR/SIR, agent-based, curve-fitting techniques/models. Besides, machine-learning models that are built on statistical tools/techniques are widely used. Predictions aim at making states and citizens aware of possible threats/consequences. However, for COVID-19 outbreak, state-of-the-art prediction models are failed to exploit crucial and unprecedented uncertainties/factors, such as a) hospital settings/capacity; b) test capacity/rate (on a daily basis); c) demographics; d) population density; e) vulnerable people; and f) income versus commodities (poverty). Depending on what factors are employed/considered in their models, predictions can be short-term and long-term. In this paper, we discuss how such continuous and unprecedented factors lead us to design complex models, rather than just relying on stochastic and/or discrete ones that are driven by randomly generated parameters. Further, it is a time to employ data-driven mathematically proved models that have the luxury to dynamically and automatically tune parameters over time.
    MeSH term(s) Betacoronavirus ; COVID-19 ; Coronavirus Infections ; Data Accuracy ; Disease Outbreaks ; Forecasting ; Humans ; Machine Learning ; Models, Statistical ; Pandemics ; Pneumonia, Viral ; SARS-CoV-2
    Keywords covid19
    Language English
    Publishing date 2020-08-13
    Publishing country United States
    Document type Journal Article
    ZDB-ID 423488-1
    ISSN 1573-689X ; 0148-5598
    ISSN (online) 1573-689X
    ISSN 0148-5598
    DOI 10.1007/s10916-020-01645-z
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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  10. Article: A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.

    Santosh, K C / GhoshRoy, Debasmita / Nakarmi, Suprim

    Healthcare (Basel, Switzerland)

    2023  Volume 11, Issue 17

    Abstract: The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel coronavirus. The World Health Organization (WHO) designated it as a global pandemic on 11 March 2020 due to its rapid and widespread transmission. Its impact has had ... ...

    Abstract The emergence of the COVID-19 pandemic in Wuhan in 2019 led to the discovery of a novel coronavirus. The World Health Organization (WHO) designated it as a global pandemic on 11 March 2020 due to its rapid and widespread transmission. Its impact has had profound implications, particularly in the realm of public health. Extensive scientific endeavors have been directed towards devising effective treatment strategies and vaccines. Within the healthcare and medical imaging domain, the application of artificial intelligence (AI) has brought significant advantages. This study delves into peer-reviewed research articles spanning the years 2020 to 2022, focusing on AI-driven methodologies for the analysis and screening of COVID-19 through chest CT scan data. We assess the efficacy of deep learning algorithms in facilitating decision making processes. Our exploration encompasses various facets, including data collection, systematic contributions, emerging techniques, and encountered challenges. However, the comparison of outcomes between 2020 and 2022 proves intricate due to shifts in dataset magnitudes over time. The initiatives aimed at developing AI-powered tools for the detection, localization, and segmentation of COVID-19 cases are primarily centered on educational and training contexts. We deliberate on their merits and constraints, particularly in the context of necessitating cross-population train/test models. Our analysis encompassed a review of 231 research publications, bolstered by a meta-analysis employing search keywords (
    Language English
    Publishing date 2023-08-24
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2721009-1
    ISSN 2227-9032
    ISSN 2227-9032
    DOI 10.3390/healthcare11172388
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