scholarly journals Deep learning reconstruction of drip-infusion cholangiography acquired with ultra-high-resolution computed tomography

2020 ◽  
Vol 45 (9) ◽  
pp. 2698-2704 ◽  
Author(s):  
Keigo Narita ◽  
Yuko Nakamura ◽  
Toru Higaki ◽  
Motonori Akagi ◽  
Yukiko Honda ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
High Resolution ◽  
High Resolution Computed Tomography ◽  
Drip Infusion ◽  
Drip Infusion Cholangiography

scholarly journals Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jun Chen ◽  
Lianlian Wu ◽  
Jun Zhang ◽  
Liang Zhang ◽  
Dexin Gong ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
High Resolution ◽  
Model Development ◽  
Imaging Method ◽  
High Resolution Computed Tomography ◽  
Expert Radiologist ◽  
Comparable Performance ◽  
Novel Coronavirus ◽  
External Test

Abstract Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system’s robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.

scholarly journals Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study

2020 ◽  
Vol 8 (7) ◽  
pp. 450-450 ◽  
Author(s):  
Shuyi Yang ◽  
Longquan Jiang ◽  
Zhuoqun Cao ◽  
Liya Wang ◽  
Jiawang Cao ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Pilot Study ◽  
High Resolution ◽  
Virus Disease ◽  
High Resolution Computed Tomography ◽  
Corona Virus

scholarly journals Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study

2020 ◽  
Author(s):  
Jun Chen ◽  
Lianlian Wu ◽  
Jun Zhang ◽  
Liang Zhang ◽  
Dexin Gong ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
High Resolution ◽  
Model Development ◽  
Imaging Method ◽  
High Resolution Computed Tomography ◽  
Working Pressure ◽  
Expert Radiologist ◽  
Comparable Performance ◽  
Novel Coronavirus

AbstractBackgroundComputed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. Our research aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT, relieve working pressure of radiologists and contribute to the control of the epidemic.MethodsFor model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University (Wuhan, Hubei province, China) were retrospectively collected and processed. Twenty-seven consecutive patients undergoing CT scans in Feb, 5, 2020 in Renmin Hospital of Wuhan University were prospectively collected to evaluate and compare the efficiency of radiologists against 2019-CoV pneumonia with that of the model.FindingsThe model achieved a per-patient sensitivity of 100%, specificity of 93.55%, accuracy of 95.24%, PPV of 84.62%, and NPV of 100%; a per-image sensitivity of 94.34%, specificity of 99.16%, accuracy of 98.85%, PPV of 88.37%, and NPV of 99.61% in retrospective dataset. For 27 prospective patients, the model achieved a comparable performance to that of expert radiologist. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%.ConclusionThe deep learning model showed a comparable performance with expert radiologist, and greatly improve the efficiency of radiologists in clinical practice. It holds great potential to relieve the pressure of frontline radiologists, improve early diagnosis, isolation and treatment, and thus contribute to the control of the epidemic.

Acute Pulmonary Oxygen Toxicity in Rats: Findings and Lung Density Changes in High Resolution Computed Tomography

2000 ◽  
Vol 42 (3) ◽  
pp. 473
Author(s):  
Dae Sik Ryu ◽  
Kun Sang Kim ◽  
Kil Hyen Kang ◽  
Haing Sup Chung ◽  
Bung Wok Lee ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Oxygen Toxicity ◽  
High Resolution Computed Tomography ◽  
Lung Density
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