Selected article for: "accuracy sensitivity and PCR method"
Author: Javaheri, Tahereh; Homayounfar, Morteza; Amoozgar, Zohreh; Reiazi, Reza; Homayounieh, Fatemeh; Abbas, Engy; Laali, Azadeh; Radmard, Amir Reza; Gharib, Mohammad Hadi; Mousavi, Seyed Ali Javad; Ghaemi, Omid; Babaei, Rosa; Mobin, Hadi Karimi; Hosseinzadeh, Mehdi; Jahanban-Esfahlan, Rana; Seidi, Khaled; Kalra, Mannudeep K.; Zhang, Guanglan; Chitkushev, L. T.; Haibe-Kains, Benjamin; Malekzadeh, Reza; Rawassizadeh, Reza
Title: CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image
  • Cord-id: n1kqn66g
  • Document date: 2020_5_6
    • ID: n1kqn66g
    Snippet: Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method, however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imagi
    Document: Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method, however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 90% compared to radiologists (70%). The model is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. In order to facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open-source format. Open-source sharing of our CovidCTNet enables developers to rapidly improve and optimize services, while preserving user privacy and data ownership.

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