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Attention Score in Context
| Title |
Real‐time intelligent classification of COVID‐19 and thrombosis via massive image‐based analysis of platelet aggregates
|
|---|---|
| Published in |
Cytometry Part A, February 2023
|
| DOI | 10.1002/cyto.a.24721 |
| Pubmed ID | |
| Authors |
Chenqi Zhang, Maik Herbig, Yuqi Zhou, Masako Nishikawa, Mohammad Shifat‐E‐Rabbi, Hiroshi Kanno, Ruoxi Yang, Yuma Ibayashi, Ting‐Hui Xiao, Gustavo K. Rohde, Masataka Sato, Satoshi Kodera, Masao Daimon, Yutaka Yatomi, Keisuke Goda |
| Abstract |
Microvascular thrombosis is a typical symptom of COVID-19 and shows similarities to thrombosis. Using a microfluidic imaging flow cytometer, we measured the blood of 181 COVID-19 samples and 101 non-COVID-19 thrombosis samples, resulting in a total of 6.3 million bright-field images. We trained a convolutional neural network to distinguish single platelets, platelet aggregates, and white blood cells and performed classical image analysis for each subpopulation individually. Based on derived single-cell features for each population, we trained machine learning models for classification between COVID-19 and non-COVID-19 thrombosis, resulting in a patient testing accuracy of 75%. This result indicates that platelet formation differs between COVID-19 and non-COVID-19 thrombosis. All analysis steps were optimized for efficiency and implemented in an easy-to-use plugin for the image viewer napari, allowing the entire analysis to be performed within seconds on mid-range computers, which could be used for real-time diagnosis. This article is protected by copyright. All rights reserved. |
X Demographics
The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 3 | 33% |
| United States | 1 | 11% |
| Unknown | 5 | 56% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 78% |
| Scientists | 1 | 11% |
| Science communicators (journalists, bloggers, editors) | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 5 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Unspecified | 1 | 20% |
| Other | 1 | 20% |
| Unknown | 3 | 60% |
| Readers by discipline | Count | As % |
|---|---|---|
| Unspecified | 1 | 20% |
| Chemistry | 1 | 20% |
| Medicine and Dentistry | 1 | 20% |
| Unknown | 2 | 40% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 14 June 2023.
All research outputs
#6,305,403
of 25,658,139 outputs
Outputs from Cytometry Part A
#448
of 1,513 outputs
Outputs of similar age
#108,725
of 427,830 outputs
Outputs of similar age from Cytometry Part A
#1
of 14 outputs
Altmetric has tracked 25,658,139 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,513 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 70% 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 427,830 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 74% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.