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| Title |
The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias
|
|---|---|
| Published in |
Frontiers in Medicine, August 2020
|
| DOI | 10.3389/fmed.2020.00550 |
| Pubmed ID | |
| Authors |
Mohamed Elgendi, Muhammad Umer Nasir, Qunfeng Tang, Richard Ribon Fletcher, Newton Howard, Carlo Menon, Rabab Ward, William Parker, Savvas Nicolaou |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 4 | 15% |
| Germany | 1 | 4% |
| United Kingdom | 1 | 4% |
| United States | 1 | 4% |
| Switzerland | 1 | 4% |
| India | 1 | 4% |
| Unknown | 18 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 20 | 74% |
| Scientists | 4 | 15% |
| Practitioners (doctors, other healthcare professionals) | 3 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 117 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 117 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 12% |
| Student > Master | 13 | 11% |
| Student > Bachelor | 11 | 9% |
| Other | 8 | 7% |
| Student > Doctoral Student | 7 | 6% |
| Other | 21 | 18% |
| Unknown | 43 | 37% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 25 | 21% |
| Computer Science | 17 | 15% |
| Engineering | 9 | 8% |
| Nursing and Health Professions | 4 | 3% |
| Biochemistry, Genetics and Molecular Biology | 3 | 3% |
| Other | 10 | 9% |
| Unknown | 49 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 18. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 March 2023.
All research outputs
#2,139,295
of 26,306,521 outputs
Outputs from Frontiers in Medicine
#628
of 7,479 outputs
Outputs of similar age
#55,759
of 430,299 outputs
Outputs of similar age from Frontiers in Medicine
#29
of 203 outputs
Altmetric has tracked 26,306,521 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,479 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.1. This one has done particularly well, scoring higher than 91% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 430,299 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 203 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.