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  1. Article: Artificial intelligence-enabled rapid diagnosis of COVID-19 patients.

    Mei, Xueyan / Lee, Hao-Chih / Diao, Kaiyue / Huang, Mingqian / Lin, Bin / Liu, Chenyu / Xie, Zongyu / Ma, Yixuan / Robson, Philip M / Chung, Michael / Bernheim, Adam / Mani, Venkatesh / Calcagno, Claudia / Li, Kunwei / Li, Shaolin / Shan, Hong / Lv, Jian / Zhao, Tongtong / Xia, Junli /
    Long, Qihua / Steinberger, Sharon / Jacobi, Adam / Deyer, Timothy / Luksza, Marta / Liu, Fang / Little, Brent P / Fayad, Zahi A / Yang, Yang

    medRxiv : the preprint server for health sciences

    2020  

    Abstract: ... diagnosis of COVID-19 patients. Chest computed tomography (CT) is a valuable component in the evaluation ... system can help to rapidly diagnose COVID-19 patients. ... COVID-19 positive patients. Among a total of 905 patients tested by real-time RT-PCR assay and next ...

    Abstract For diagnosis of COVID-19, a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to two days to complete, serial testing may be required to rule out the possibility of false negative results, and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of COVID-19 patients. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiologic findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history, and laboratory testing to rapidly diagnose COVID-19 positive patients. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an AUC of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of RT-PCR positive COVID-19 patients who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.
    Keywords covid19
    Language English
    Publishing date 2020-04-17
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2020.04.12.20062661
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Artificial intelligence-enabled rapid diagnosis of patients with COVID-19.

    Mei, Xueyan / Lee, Hao-Chih / Diao, Kai-Yue / Huang, Mingqian / Lin, Bin / Liu, Chenyu / Xie, Zongyu / Ma, Yixuan / Robson, Philip M / Chung, Michael / Bernheim, Adam / Mani, Venkatesh / Calcagno, Claudia / Li, Kunwei / Li, Shaolin / Shan, Hong / Lv, Jian / Zhao, Tongtong / Xia, Junli /
    Long, Qihua / Steinberger, Sharon / Jacobi, Adam / Deyer, Timothy / Luksza, Marta / Liu, Fang / Little, Brent P / Fayad, Zahi A / Yang, Yang

    Nature medicine

    2020  Volume 26, Issue 8, Page(s) 1224–1228

    Abstract: ... diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component ... history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients. ... patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT-PCR assay and ...

    Abstract For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT-PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.
    MeSH term(s) Adult ; Artificial Intelligence ; Betacoronavirus/genetics ; Betacoronavirus/isolation & purification ; Betacoronavirus/pathogenicity ; COVID-19 ; Coronavirus Infections/diagnosis ; Coronavirus Infections/diagnostic imaging ; Coronavirus Infections/genetics ; Coronavirus Infections/virology ; Female ; Humans ; Male ; Middle Aged ; Pandemics ; Pneumonia, Viral/diagnosis ; Pneumonia, Viral/diagnostic imaging ; Pneumonia, Viral/genetics ; Pneumonia, Viral/virology ; Real-Time Polymerase Chain Reaction ; SARS-CoV-2 ; Thorax/diagnostic imaging ; Thorax/pathology ; Thorax/virology ; Tomography, X-Ray Computed
    Keywords covid19
    Language English
    Publishing date 2020-05-19
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1220066-9
    ISSN 1546-170X ; 1078-8956
    ISSN (online) 1546-170X
    ISSN 1078-8956
    DOI 10.1038/s41591-020-0931-3
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  3. Article ; Online: Artificial intelligence–enabled rapid diagnosis of patients with COVID-19

    Mei, Xueyan / Lee, Hao-Chih / Diao, Kai-yue / Huang, Mingqian / Lin, Bin / Liu, Chenyu / Xie, Zongyu / Ma, Yixuan / Robson, Philip M. / Chung, Michael / Bernheim, Adam / Mani, Venkatesh / Calcagno, Claudia / Li, Kunwei / Li, Shaolin / Shan, Hong / Lv, Jian / Zhao, Tongtong / Xia, Junli /
    Long, Qihua / Steinberger, Sharon / Jacobi, Adam / Deyer, Timothy / Luksza, Marta / Liu, Fang / Little, Brent P. / Fayad, Zahi A. / Yang, Yang

    Nature Medicine

    2020  Volume 26, Issue 8, Page(s) 1224–1228

    Keywords General Biochemistry, Genetics and Molecular Biology ; General Medicine ; covid19
    Language English
    Publisher Springer Science and Business Media LLC
    Publishing country us
    Document type Article ; Online
    ZDB-ID 1220066-9
    ISSN 1546-170X ; 1078-8956
    ISSN (online) 1546-170X
    ISSN 1078-8956
    DOI 10.1038/s41591-020-0931-3
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article: Artificial intelligence-enabled rapid diagnosis of patients with COVID-19

    Mei, Xueyan / Lee, Hao-Chih / Diao, Kai-Yue / Huang, Mingqian / Lin, Bin / Liu, Chenyu / Xie, Zongyu / Ma, Yixuan / Robson, Philip M / Chung, Michael / Bernheim, Adam / Mani, Venkatesh / Calcagno, Claudia / Li, Kunwei / Li, Shaolin / Shan, Hong / Lv, Jian / Zhao, Tongtong / Xia, Junli /
    Long, Qihua / Steinberger, Sharon / Jacobi, Adam / Deyer, Timothy / Luksza, Marta / Liu, Fang / Little, Brent P / Fayad, Zahi A / Yang, Yang

    Nat Med

    Abstract: ... diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component ... history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients. ... patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT-PCR assay and ...

    Abstract For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT-PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #291852
    Database COVID19

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  5. Article ; Online: A non-invasive ultrasensitive diagnostic approach for COVID-19 infection using salivary label-free SERS fingerprinting and artificial intelligence.

    Karunakaran, Varsha / Joseph, Manu M / Yadev, Induprabha / Sharma, Himanshu / Shamna, Kottarathil / Saurav, Sumeet / Sreejith, Remanan Pushpa / Anand, Veena / Beegum, Rosenara / Regi David, S / Iype, Thomas / Sarada Devi, K L / Nizarudheen, A / Sharmad, M S / Sharma, Rishi / Mukhiya, Ravindra / Thouti, Eshwar / Yoosaf, Karuvath / Joseph, Joshy /
    Sujatha Devi, P / Savithri, S / Agarwal, Ajay / Singh, Sanjay / Maiti, Kaustabh Kumar

    Journal of photochemistry and photobiology. B, Biology

    2022  Volume 234, Page(s) 112545

    Abstract: ... a prediction accuracy of 95% and F1-score of 94.73%, and 95.28% for healthy and COVID-19 infected patients ... signature-based discrimination method of the COVID-19 condition from the patient saliva ensured high ... 19 diagnostics. However, genetic diversity of the virus due to rapid mutations limits the efficiency ...

    Abstract Clinical diagnostics for SARS-CoV-2 infection usually comprises the sampling of throat or nasopharyngeal swabs that are invasive and create patient discomfort. Hence, saliva is attempted as a sample of choice for the management of COVID-19 outbreaks that cripples the global healthcare system. Although limited by the risk of eliciting false-negative and positive results, tedious test procedures, requirement of specialized laboratories, and expensive reagents, nucleic acid-based tests remain the gold standard for COVID-19 diagnostics. However, genetic diversity of the virus due to rapid mutations limits the efficiency of nucleic acid-based tests. Herein, we have demonstrated the simplest screening modality based on label-free surface enhanced Raman scattering (LF-SERS) for scrutinizing the SARS-CoV-2-mediated molecular-level changes of the saliva samples among healthy, COVID-19 infected and COVID-19 recovered subjects. Moreover, our LF-SERS technique enabled to differentiate the three classes of corona virus spike protein derived from SARS-CoV-2, SARS-CoV and MERS-CoV. Raman spectral data was further decoded, segregated and effectively managed with the aid of machine learning algorithms. The classification models built upon biochemical signature-based discrimination method of the COVID-19 condition from the patient saliva ensured high accuracy, specificity, and sensitivity. The trained support vector machine (SVM) classifier achieved a prediction accuracy of 95% and F1-score of 94.73%, and 95.28% for healthy and COVID-19 infected patients respectively. The current approach not only differentiate SARS-CoV-2 infection with healthy controls but also predicted a distinct fingerprint for different stages of patient recovery. Employing portable hand-held Raman spectrophotometer as the instrument and saliva as the sample of choice will guarantee a rapid and non-invasive diagnostic strategy to warrant or assure patient comfort and large-scale population screening for SARS-CoV-2 infection and monitoring the recovery process.
    MeSH term(s) Artificial Intelligence ; COVID-19/diagnosis ; COVID-19 Testing ; Delivery of Health Care ; Humans ; Nucleic Acids ; SARS-CoV-2 ; Saliva
    Chemical Substances Nucleic Acids
    Language English
    Publishing date 2022-08-19
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 623022-2
    ISSN 1873-2682 ; 1011-1344
    ISSN (online) 1873-2682
    ISSN 1011-1344
    DOI 10.1016/j.jphotobiol.2022.112545
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  6. Article ; Online: A non-invasive ultrasensitive diagnostic approach for COVID-19 infection using salivary label-free SERS fingerprinting and artificial intelligence

    Karunakaran, Varsha / Joseph, Manu M. / Yadev, Induprabha / Sharma, Himanshu / Shamna, Kottarathil / Saurav, Sumeet / Sreejith, Remanan Pushpa / Anand, Veena / Beegum, Rosenara / Regi David, S. / Iype, Thomas / Sarada Devi, K.L. / Nizarudheen, A. / Sharmad, M.S. / Sharma, Rishi / Mukhiya, Ravindra / Thouti, Eshwar / Yoosaf, Karuvath / Joseph, Joshy /
    Sujatha Devi, P. / Savithri, S. / Agarwal, Ajay / Singh, Sanjay / Maiti, Kaustabh Kumar

    Journal of Photochemistry & Photobiology, B: Biology. 2022 Sept., v. 234 p.112545-

    2022  

    Abstract: ... a prediction accuracy of 95% and F1-score of 94.73%, and 95.28% for healthy and COVID-19 infected patients ... signature-based discrimination method of the COVID-19 condition from the patient saliva ensured high ... 19 diagnostics. However, genetic diversity of the virus due to rapid mutations limits the efficiency ...

    Abstract Clinical diagnostics for SARS-CoV-2 infection usually comprises the sampling of throat or nasopharyngeal swabs that are invasive and create patient discomfort. Hence, saliva is attempted as a sample of choice for the management of COVID-19 outbreaks that cripples the global healthcare system. Although limited by the risk of eliciting false-negative and positive results, tedious test procedures, requirement of specialized laboratories, and expensive reagents, nucleic acid-based tests remain the gold standard for COVID-19 diagnostics. However, genetic diversity of the virus due to rapid mutations limits the efficiency of nucleic acid-based tests. Herein, we have demonstrated the simplest screening modality based on label-free surface enhanced Raman scattering (LF-SERS) for scrutinizing the SARS-CoV-2-mediated molecular-level changes of the saliva samples among healthy, COVID-19 infected and COVID-19 recovered subjects. Moreover, our LF-SERS technique enabled to differentiate the three classes of corona virus spike protein derived from SARS-CoV-2, SARS-CoV and MERS-CoV. Raman spectral data was further decoded, segregated and effectively managed with the aid of machine learning algorithms. The classification models built upon biochemical signature-based discrimination method of the COVID-19 condition from the patient saliva ensured high accuracy, specificity, and sensitivity. The trained support vector machine (SVM) classifier achieved a prediction accuracy of 95% and F1-score of 94.73%, and 95.28% for healthy and COVID-19 infected patients respectively. The current approach not only differentiate SARS-CoV-2 infection with healthy controls but also predicted a distinct fingerprint for different stages of patient recovery. Employing portable hand-held Raman spectrophotometer as the instrument and saliva as the sample of choice will guarantee a rapid and non-invasive diagnostic strategy to warrant or assure patient comfort and large-scale population screening for SARS-CoV-2 infection and monitoring the recovery process.
    Keywords COVID-19 infection ; Severe acute respiratory syndrome coronavirus 2 ; Severe acute respiratory syndrome-related coronavirus ; diagnostic techniques ; genetic variation ; health services ; patients ; photobiology ; photochemistry ; prediction ; risk ; saliva ; spectral analysis ; spectrophotometers ; support vector machines ; throat ; viruses ; Surface enhanced Raman spectroscopy ; COVID-19 ; Label-free ; Diagnosis ; Artificial intelligence ; SERS ; AuNPs ; PCA ; SVM
    Language English
    Dates of publication 2022-09
    Publishing place Elsevier B.V.
    Document type Article ; Online
    ZDB-ID 623022-2
    ISSN 1873-2682 ; 1011-1344
    ISSN (online) 1873-2682
    ISSN 1011-1344
    DOI 10.1016/j.jphotobiol.2022.112545
    Database NAL-Catalogue (AGRICOLA)

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