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  1. Artikel ; Online: COVID-19 in CXR: From Detection and Severity Scoring to Patient Disease Monitoring.

    Frid-Adar, Maayan / Amer, Rula / Gozes, Ophir / Nassar, Jannette / Greenspan, Hayit

    IEEE journal of biomedical and health informatics

    2021  Band 25, Heft 6, Seite(n) 1892–1903

    Abstract: This work estimates the severity of pneumonia in COVID-19 patients and reports the findings of a longitudinal study of disease progression. It presents a deep learning model for simultaneous detection and localization of pneumonia in chest Xray (CXR) ... ...

    Abstract This work estimates the severity of pneumonia in COVID-19 patients and reports the findings of a longitudinal study of disease progression. It presents a deep learning model for simultaneous detection and localization of pneumonia in chest Xray (CXR) images, which is shown to generalize to COVID-19 pneumonia. The localization maps are utilized to calculate a "Pneumonia Ratio" which indicates disease severity. The assessment of disease severity serves to build a temporal disease extent profile for hospitalized patients. To validate the model's applicability to the patient monitoring task, we developed a validation strategy which involves a synthesis of Digital Reconstructed Radiographs (DRRs - synthetic Xray) from serial CT scans; we then compared the disease progression profiles that were generated from the DRRs to those that were generated from CT volumes.
    Mesh-Begriff(e) COVID-19/diagnostic imaging ; COVID-19/pathology ; COVID-19/virology ; Case-Control Studies ; Humans ; Monitoring, Physiologic/methods ; Pneumonia, Viral/diagnostic imaging ; Pneumonia, Viral/pathology ; Pneumonia, Viral/virology ; Radiography, Thoracic ; SARS-CoV-2/isolation & purification ; Severity of Illness Index
    Sprache Englisch
    Erscheinungsdatum 2021-06-03
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2695320-1
    ISSN 2168-2208 ; 2168-2194
    ISSN (online) 2168-2208
    ISSN 2168-2194
    DOI 10.1109/JBHI.2021.3069169
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel: COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring

    Amer, Rula / Frid-Adar, Maayan / Gozes, Ophir / Nassar, Jannette / Greenspan, Hayit

    Abstract: In this work, we estimate the severity of pneumonia in COVID-19 patients and conduct a longitudinal study of disease progression. To achieve this goal, we developed a deep learning model for simultaneous detection and segmentation of pneumonia in chest ... ...

    Abstract In this work, we estimate the severity of pneumonia in COVID-19 patients and conduct a longitudinal study of disease progression. To achieve this goal, we developed a deep learning model for simultaneous detection and segmentation of pneumonia in chest Xray (CXR) images and generalized to COVID-19 pneumonia. The segmentations were utilized to calculate a"Pneumonia Ratio"which indicates the disease severity. The measurement of disease severity enables to build a disease extent profile over time for hospitalized patients. To validate the model relevance to the patient monitoring task, we developed a validation strategy which involves a synthesis of Digital Reconstructed Radiographs (DRRs - synthetic Xray) from serial CT scans; we then compared the disease progression profiles that were generated from the DRRs to those that were generated from CT volumes.
    Schlagwörter covid19
    Verlag ArXiv
    Dokumenttyp Artikel
    Datenquelle COVID19

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  3. Buch ; Online: COVID-19 in CXR

    Amer, Rula / Frid-Adar, Maayan / Gozes, Ophir / Nassar, Jannette / Greenspan, Hayit

    from Detection and Severity Scoring to Patient Disease Monitoring

    2020  

    Abstract: In this work, we estimate the severity of pneumonia in COVID-19 patients and conduct a longitudinal study of disease progression. To achieve this goal, we developed a deep learning model for simultaneous detection and segmentation of pneumonia in chest ... ...

    Abstract In this work, we estimate the severity of pneumonia in COVID-19 patients and conduct a longitudinal study of disease progression. To achieve this goal, we developed a deep learning model for simultaneous detection and segmentation of pneumonia in chest Xray (CXR) images and generalized to COVID-19 pneumonia. The segmentations were utilized to calculate a "Pneumonia Ratio" which indicates the disease severity. The measurement of disease severity enables to build a disease extent profile over time for hospitalized patients. To validate the model relevance to the patient monitoring task, we developed a validation strategy which involves a synthesis of Digital Reconstructed Radiographs (DRRs - synthetic Xray) from serial CT scans; we then compared the disease progression profiles that were generated from the DRRs to those that were generated from CT volumes.

    Comment: paper was accepted to JBHI IEEE journal
    Schlagwörter Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; covid19
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2020-08-04
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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  4. Artikel: Automated triage of COVID-19 from various lung abnormalities using chest CT features

    Amran, Dor / Frid-Adar, Maayan / Sagie, Nimrod / Nassar, Jannette / Kabakovitch, Asher / Greenspan, Hayit

    Abstract: The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to ... ...

    Abstract The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to reverse transcription-polymerase chain reaction (RT-PCR) test. In this paper, we propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. More specifically, we produce multiple descriptive features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier that distinguishes between COVID-19 and other lung abnormalities (including community acquired pneumonia). We evaluated our system on a dataset of 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at 85.4% specificity with 94.0% ROC-AUC. In addition, we present an elaborated feature analysis and ablation study to explore the importance of each feature.
    Schlagwörter covid19
    Verlag ArXiv
    Dokumenttyp Artikel
    Datenquelle COVID19

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  5. Artikel: Coronavirus Detection and Analysis on Chest CT with Deep Learning

    Gozes, Ophir / Frid-Adar, Maayan / Sagie, Nimrod / Zhang, Huangqi / Ji, Wenbin / Greenspan, Hayit

    Abstract: The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed cases of which 80,881 are in"Mainland China"with 7,949 deaths, a mortality ... ...

    Abstract The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed cases of which 80,881 are in"Mainland China"with 7,949 deaths, a mortality rate of 3.4%. In order to support radiologists in this overwhelming challenge, we develop a deep learning based algorithm that can detect, localize and quantify severity of COVID-19 manifestation from chest CT scans. The algorithm is comprised of a pipeline of image processing algorithms which includes lung segmentation, 2D slice classification and fine grain localization. In order to further understand the manifestations of the disease, we perform unsupervised clustering of abnormal slices. We present our results on a dataset comprised of 110 confirmed COVID-19 patients from Zhejiang province, China.
    Schlagwörter covid19
    Verlag ArXiv
    Dokumenttyp Artikel
    Datenquelle COVID19

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  6. Buch ; Online: Coronavirus Detection and Analysis on Chest CT with Deep Learning

    Gozes, Ophir / Frid-Adar, Maayan / Sagie, Nimrod / Zhang, Huangqi / Ji, Wenbin / Greenspan, Hayit

    2020  

    Abstract: The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed cases of which 80,881 are in "Mainland China" with 7,949 deaths, a ... ...

    Abstract The outbreak of the novel coronavirus, officially declared a global pandemic, has a severe impact on our daily lives. As of this writing there are approximately 197,188 confirmed cases of which 80,881 are in "Mainland China" with 7,949 deaths, a mortality rate of 3.4%. In order to support radiologists in this overwhelming challenge, we develop a deep learning based algorithm that can detect, localize and quantify severity of COVID-19 manifestation from chest CT scans. The algorithm is comprised of a pipeline of image processing algorithms which includes lung segmentation, 2D slice classification and fine grain localization. In order to further understand the manifestations of the disease, we perform unsupervised clustering of abnormal slices. We present our results on a dataset comprised of 110 confirmed COVID-19 patients from Zhejiang province, China.
    Schlagwörter Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; covid19
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2020-04-06
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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  7. Buch ; Online: Automated triage of COVID-19 from various lung abnormalities using chest CT features

    Amran, Dor / Frid-Adar, Maayan / Sagie, Nimrod / Nassar, Jannette / Kabakovitch, Asher / Greenspan, Hayit

    2020  

    Abstract: The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to ... ...

    Abstract The outbreak of COVID-19 has lead to a global effort to decelerate the pandemic spread. For this purpose chest computed-tomography (CT) based screening and diagnosis of COVID-19 suspected patients is utilized, either as a support or replacement to reverse transcription-polymerase chain reaction (RT-PCR) test. In this paper, we propose a fully automated AI based system that takes as input chest CT scans and triages COVID-19 cases. More specifically, we produce multiple descriptive features, including lung and infections statistics, texture, shape and location, to train a machine learning based classifier that distinguishes between COVID-19 and other lung abnormalities (including community acquired pneumonia). We evaluated our system on a dataset of 2191 CT cases and demonstrated a robust solution with 90.8% sensitivity at 85.4% specificity with 94.0% ROC-AUC. In addition, we present an elaborated feature analysis and ablation study to explore the importance of each feature.

    Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
    Schlagwörter Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
    Thema/Rubrik (Code) 004
    Erscheinungsdatum 2020-10-24
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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  8. Artikel: Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection&Patient Monitoring using Deep Learning CT Image Analysis

    Ophir Gozes / Maayan Frid-Adar / Hayit Greenspan / Patrick Browning D. / Huangqi Zhang / Wenbin Ji / Adam Bernheim / Eliot Siegel

    Abstract: Purpose: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from non-patients. Materials and Methods: Multiple international datasets, ... ...

    Abstract Purpose: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from non-patients. Materials and Methods: Multiple international datasets, including from Chinese disease-infected areas were included. We present a system that utilizes robust 2D and 3D deep learning models, modifying and adapting existing AI models and combining them with clinical understanding. We conducted multiple retrospective experiments to analyze the performance of the system in the detection of suspected COVID-19 thoracic CT features and to evaluate evolution of the disease in each patient over time using a 3D volume review, generating a Corona score. The study includes a testing set of 157 international patients (China and U.S). Results: Classification results for Coronavirus vs Non-coronavirus cases per thoracic CT studies were 0.996 AUC (95%CI: 0.989-1.00)

    on datasets of Chinese control and infected patients. Possible working point: 98.2% sensitivity, 92.2% specificity. For time analysis of Coronavirus patients, the system output enables quantitative measurements for smaller opacities (volume, diameter) and visualization of the larger opacities in a slice-based heat map or a 3D volume display. Our suggested Corona score measures the progression of disease over time. Conclusion: This initial study, which is currently being expanded to a larger population, demonstrated that rapidly developed AI-based image analysis can achieve high accuracy in detection of Coronavirus as well as quantification and tracking of disease burden.
    Schlagwörter covid19
    Verlag arxiv
    Dokumenttyp Artikel
    Datenquelle COVID19

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  9. Artikel: Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection&Patient Monitoring using Deep Learning CT Image Analysis

    Gozes, Ophir / Frid-Adar, Maayan / Greenspan, Hayit / Browning, Patrick D. / Zhang, Huangqi / Ji, Wenbin / Bernheim, Adam / Siegel, Eliot

    Abstract: Purpose: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from non-patients. Materials and Methods: Multiple international datasets, ... ...

    Abstract Purpose: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from non-patients. Materials and Methods: Multiple international datasets, including from Chinese disease-infected areas were included. We present a system that utilizes robust 2D and 3D deep learning models, modifying and adapting existing AI models and combining them with clinical understanding. We conducted multiple retrospective experiments to analyze the performance of the system in the detection of suspected COVID-19 thoracic CT features and to evaluate evolution of the disease in each patient over time using a 3D volume review, generating a Corona score. The study includes a testing set of 157 international patients (China and U.S). Results: Classification results for Coronavirus vs Non-coronavirus cases per thoracic CT studies were 0.996 AUC (95%CI: 0.989-1.00)

    on datasets of Chinese control and infected patients. Possible working point: 98.2% sensitivity, 92.2% specificity. For time analysis of Coronavirus patients, the system output enables quantitative measurements for smaller opacities (volume, diameter) and visualization of the larger opacities in a slice-based heat map or a 3D volume display. Our suggested Corona score measures the progression of disease over time. Conclusion: This initial study, which is currently being expanded to a larger population, demonstrated that rapidly developed AI-based image analysis can achieve high accuracy in detection of Coronavirus as well as quantification and tracking of disease burden.
    Schlagwörter covid19
    Verlag ArXiv
    Dokumenttyp Artikel
    Datenquelle COVID19

    Kategorien

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  10. Buch ; Online: Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic

    Gozes, Ophir / Frid-Adar, Maayan / Greenspan, Hayit / Browning, Patrick D. / Zhang, Huangqi / Ji, Wenbin / Bernheim, Adam / Siegel, Eliot

    Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis

    2020  

    Abstract: Purpose: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from non-patients. Materials and Methods: Multiple international datasets, ... ...

    Abstract Purpose: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from non-patients. Materials and Methods: Multiple international datasets, including from Chinese disease-infected areas were included. We present a system that utilizes robust 2D and 3D deep learning models, modifying and adapting existing AI models and combining them with clinical understanding. We conducted multiple retrospective experiments to analyze the performance of the system in the detection of suspected COVID-19 thoracic CT features and to evaluate evolution of the disease in each patient over time using a 3D volume review, generating a Corona score. The study includes a testing set of 157 international patients (China and U.S). Results: Classification results for Coronavirus vs Non-coronavirus cases per thoracic CT studies were 0.996 AUC (95%CI: 0.989-1.00)

    on datasets of Chinese control and infected patients. Possible working point: 98.2% sensitivity, 92.2% specificity. For time analysis of Coronavirus patients, the system output enables quantitative measurements for smaller opacities (volume, diameter) and visualization of the larger opacities in a slice-based heat map or a 3D volume display. Our suggested Corona score measures the progression of disease over time. Conclusion: This initial study, which is currently being expanded to a larger population, demonstrated that rapidly developed AI-based image analysis can achieve high accuracy in detection of Coronavirus as well as quantification and tracking of disease burden.

    Comment: 19 pages, 6 figures
    Schlagwörter Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; covid19
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2020-03-10
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

    Volltext online

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