| Title |
Machine learning: from radiomics to discovery and routine
|
|---|---|
| Published in |
Die Radiologie, June 2018
|
| DOI | 10.1007/s00117-018-0407-3 |
| Pubmed ID | |
| Authors |
G. Langs, S. Röhrich, J. Hofmanninger, F. Prayer, J. Pan, C. Herold, H. Prosch |
| Abstract |
Machine learning is rapidly gaining importance in radiology. It allows for the exploitation of patterns in imaging data and in patient records for a more accurate and precise quantification, diagnosis, and prognosis. Here, we outline the basics of machine learning relevant for radiology, and review the current state of the art, the limitations, and the challenges faced as these techniques become an important building block of precision medicine. Furthermore, we discuss the roles machine learning can play in clinical routine and research and predict how it might change the field of radiology. |
X Demographics
The data shown below were collected from the profiles of 49 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 Kingdom | 10 | 20% |
| United States | 10 | 20% |
| Germany | 2 | 4% |
| Austria | 2 | 4% |
| Canada | 2 | 4% |
| China | 1 | 2% |
| Mexico | 1 | 2% |
| Switzerland | 1 | 2% |
| Australia | 1 | 2% |
| Other | 4 | 8% |
| Unknown | 15 | 31% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 27 | 55% |
| Practitioners (doctors, other healthcare professionals) | 13 | 27% |
| Scientists | 9 | 18% |
Mendeley readers
The data shown below were compiled from readership statistics for 123 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 123 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 18 | 15% |
| Researcher | 18 | 15% |
| Student > Master | 15 | 12% |
| Student > Bachelor | 10 | 8% |
| Student > Doctoral Student | 9 | 7% |
| Other | 18 | 15% |
| Unknown | 35 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 29 | 24% |
| Computer Science | 14 | 11% |
| Engineering | 7 | 6% |
| Biochemistry, Genetics and Molecular Biology | 6 | 5% |
| Agricultural and Biological Sciences | 3 | 2% |
| Other | 19 | 15% |
| Unknown | 45 | 37% |
Attention Score in Context
This research output has an Altmetric Attention Score of 30. 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 08 October 2019.
All research outputs
#1,343,998
of 25,734,859 outputs
Outputs from Die Radiologie
#3
of 328 outputs
Outputs of similar age
#28,007
of 342,620 outputs
Outputs of similar age from Die Radiologie
#1
of 6 outputs
Altmetric has tracked 25,734,859 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 328 research outputs from this source. They receive a mean Attention Score of 3.8. This one has done particularly well, scoring higher than 99% 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 342,620 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them