You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging
|
|---|---|
| Published in |
Radiology, July 2017
|
| DOI | 10.1148/radiol.2017162664 |
| Pubmed ID | |
| Authors |
Luciano M Prevedello, Barbaros S Erdal, John L Ryu, Kevin J Little, Mutlu Demirer, Songyue Qian, Richard D White |
| Abstract |
Purpose To evaluate the performance of an artificial intelligence (AI) tool using a deep learning algorithm for detecting hemorrhage, mass effect, or hydrocephalus (HMH) at non-contrast material-enhanced head computed tomographic (CT) examinations and to determine algorithm performance for detection of suspected acute infarct (SAI). Materials and Methods This HIPAA-compliant retrospective study was completed after institutional review board approval. A training and validation dataset of noncontrast-enhanced head CT examinations that comprised 100 examinations of HMH, 22 of SAI, and 124 of noncritical findings was obtained resulting in 2583 representative images. Examinations were processed by using a convolutional neural network (deep learning) using two different window and level configurations (brain window and stroke window). AI algorithm performance was tested on a separate dataset containing 50 examinations with HMH findings, 15 with SAI findings, and 35 with noncritical findings. Results Final algorithm performance for HMH showed 90% (45 of 50) sensitivity (95% confidence interval [CI]: 78%, 97%) and 85% (68 of 80) specificity (95% CI: 76%, 92%), with area under the receiver operating characteristic curve (AUC) of 0.91 with the brain window. For SAI, the best performance was achieved with the stroke window showing 62% (13 of 21) sensitivity (95% CI: 38%, 82%) and 96% (27 of 28) specificity (95% CI: 82%, 100%), with AUC of 0.81. Conclusion AI using deep learning demonstrates promise for detecting critical findings at noncontrast-enhanced head CT. A dedicated algorithm was required to detect SAI. Detection of SAI showed lower sensitivity in comparison to detection of HMH, but showed reasonable performance. Findings support further investigation of the algorithm in a controlled and prospective clinical setting to determine whether it can independently screen noncontrast-enhanced head CT examinations and notify the interpreting radiologist of critical findings. (©) RSNA, 2017 Online supplemental material is available for this article. |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 50% |
| Hungary | 1 | 13% |
| Turkey | 1 | 13% |
| Unknown | 2 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 38% |
| Practitioners (doctors, other healthcare professionals) | 3 | 38% |
| Science communicators (journalists, bloggers, editors) | 1 | 13% |
| Scientists | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 255 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 255 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 35 | 14% |
| Other | 31 | 12% |
| Student > Ph. D. Student | 25 | 10% |
| Student > Master | 21 | 8% |
| Student > Bachelor | 21 | 8% |
| Other | 50 | 20% |
| Unknown | 72 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 87 | 34% |
| Computer Science | 19 | 7% |
| Engineering | 18 | 7% |
| Neuroscience | 7 | 3% |
| Nursing and Health Professions | 4 | 2% |
| Other | 25 | 10% |
| Unknown | 95 | 37% |
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 21 May 2019.
All research outputs
#6,335,799
of 25,382,440 outputs
Outputs from Radiology
#3,752
of 10,267 outputs
Outputs of similar age
#92,092
of 326,157 outputs
Outputs of similar age from Radiology
#45
of 83 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 10,267 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.2. This one has gotten more attention than average, scoring higher than 63% 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 326,157 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 71% of its contemporaries.
We're also able to compare this research output to 83 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.