scholarly journals Computer-aided reading of tuberculosis chest radiography: moving the research agenda forward to inform policy

2017 ◽  
Vol 50 (1) ◽  
pp. 1700953 ◽  
Author(s):  
Faiz Ahmad Khan ◽  
Tripti Pande ◽  
Belay Tessema ◽  
Rinn Song ◽  
Andrea Benedetti ◽  
...  
2010 ◽  
Vol 7 (3) ◽  
pp. 387-404 ◽  
Author(s):  
Edward Red ◽  
Vonn Holyoak ◽  
C. Greg Jensen ◽  
Felicia Marshall ◽  
Jordan Ryskamp ◽  
...  

Author(s):  
Ammar Chaudhry ◽  
Ammar Chaudhry ◽  
William H. Moore

Purpose: The radiographic diagnosis of lung nodules is associated with low sensitivity and specificity. Computer-aided detection (CAD) system has been shown to have higher accuracy in the detection of lung nodules. The purpose of this study is to assess the effect on sensitivity and specificity when a CAD system is used to review chest radiographs in real-time setting. Methods: Sixty-three patients, including 24 controls, who had chest radiographs and CT within three months were included in this study. Three radiologists were presented chest radiographs without CAD and were asked to mark all lung nodules. Then the radiologists were allowed to see the CAD region-of-interest (ROI) marks and were asked to agree or disagree with the marks. All marks were correlated with CT studies. Results: The mean sensitivity of the three radiologists without CAD was 16.1%, which showed a statistically significant improvement to 22.5% with CAD. The mean specificity of the three radiologists was 52.5% without CAD and decreased to 48.1% with CAD. There was no significant change in the positive predictive value or negative predictive value. Conclusion: The addition of a CAD system to chest radiography interpretation statistically improves the detection of lung nodules without affecting its specificity. Thus suggesting CAD would improve overall detection of lung nodules.


2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Syed Mohammad Asad Zaidi ◽  
Shifa Salman Habib ◽  
Bram Van Ginneken ◽  
Rashida Abbas Ferrand ◽  
Jacob Creswell ◽  
...  

2006 ◽  
Vol 19 (4) ◽  
pp. 376-382 ◽  
Author(s):  
Shuji Sakai ◽  
Hiroyasu Soeda ◽  
Naoki Takahashi ◽  
Takashi Okafuji ◽  
Tadamasa Yoshitake ◽  
...  

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Shifa Salman Habib ◽  
Sana Rafiq ◽  
Syed Mohammad Asad Zaidi ◽  
Rashida Abbas Ferrand ◽  
Jacob Creswell ◽  
...  

2021 ◽  
Author(s):  
Ibrahim Chikowe ◽  
Elias Peter Mwakilama

Pharmacoepidemiology is a relatively new area of study that focuses on research aimed at producing data about drugs’ usage and safety in well-defined populations. Its significant impact on patient safety has translated into improving health care systems worldwide, where it has been widely adopted. This field has developed to an extent that policy and guidelines makers have started using its evidence alongside that produced from randomised controlled clinical trials. Although this significant improvement has been partly attributed to the adoption of statistics and computer-aided models into the way pharmacoepidemiology studies are designed and conducted, certain gaps still exist. This chapter reports some of the significant developments made, along with the gaps observed so far, in the adoption of statistics and computing into pharmacoepidemiology research. The goal is to highlight efforts that have led to the new pharmacoepidemiology developments, while examining the intersection between data science and pharmacology through research narrative reviews of computer-aided pharmacology. The chapter shows the significant number of initiatives that have been applied/adopted to improve pharmacoepidemiology research. Nonetheless, further developments in integrating pharmacoepidemiology with computers and statistics are needed in order to enhance the research agenda.


2012 ◽  
Vol 11 (1) ◽  
pp. 536-541
Author(s):  
Zhenghao Shi ◽  
Li Li ◽  
Kenji Suzuki ◽  
Yinghui Wang ◽  
Lifeng He ◽  
...  

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