| Title |
Performance of a deep learning algorithm for the evaluation of CAD-RADS classification with CCTA
|
|---|---|
| Published in |
Atherosclerosis (00219150), December 2019
|
| DOI | 10.1016/j.atherosclerosis.2019.12.001 |
| Pubmed ID | |
| Authors |
Giuseppe Muscogiuri, Mattia Chiesa, Michela Trotta, Marco Gatti, Vitanio Palmisano, Serena Dell'Aversana, Francesca Baessato, Annachiara Cavaliere, Gloria Cicala, Antonella Loffreno, Giulia Rizzon, Marco Guglielmo, Andrea Baggiano, Laura Fusini, Luca Saba, Daniele Andreini, Mauro Pepi, Mark G Rabbat, Andrea I Guaricci, Carlo N De Cecco, Gualtiero Colombo, Gianluca Pontone |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 50% |
| France | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 78 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 14% |
| Student > Bachelor | 8 | 10% |
| Student > Ph. D. Student | 7 | 9% |
| Student > Doctoral Student | 7 | 9% |
| Student > Master | 4 | 5% |
| Other | 14 | 18% |
| Unknown | 27 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 27 | 35% |
| Computer Science | 4 | 5% |
| Biochemistry, Genetics and Molecular Biology | 4 | 5% |
| Nursing and Health Professions | 3 | 4% |
| Engineering | 3 | 4% |
| Other | 7 | 9% |
| Unknown | 30 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 30 March 2022.
All research outputs
#7,783,733
of 25,387,668 outputs
Outputs from Atherosclerosis (00219150)
#1,898
of 5,588 outputs
Outputs of similar age
#159,051
of 474,668 outputs
Outputs of similar age from Atherosclerosis (00219150)
#26
of 59 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 5,588 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has gotten more attention than average, scoring higher than 65% 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 474,668 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.