| Title |
Characterizing Diagnostic Search Patterns in Digital Breast Pathology: Scanners and Drillers
|
|---|---|
| Published in |
Journal of Digital Imaging, July 2017
|
| DOI | 10.1007/s10278-017-9990-5 |
| Pubmed ID | |
| Authors |
Ezgi Mercan, Linda G. Shapiro, Tad T. Brunyé, Donald L. Weaver, Joann G. Elmore |
| Abstract |
Following a baseline demographic survey, 87 pathologists interpreted 240 digital whole slide images of breast biopsy specimens representing a range of diagnostic categories from benign to atypia, ductal carcinoma in situ, and invasive cancer. A web-based viewer recorded pathologists' behaviors while interpreting a subset of 60 randomly selected and randomly ordered slides. To characterize diagnostic search patterns, we used the viewport location, time stamp, and zoom level data to calculate four variables: average zoom level, maximum zoom level, zoom level variance, and scanning percentage. Two distinct search strategies were confirmed: scanning is characterized by panning at a constant zoom level, while drilling involves zooming in and out at various locations. Statistical analysis was applied to examine the associations of different visual interpretive strategies with pathologist characteristics, diagnostic accuracy, and efficiency. We found that females scanned more than males, and age was positively correlated with scanning percentage, while the facility size was negatively correlated. Throughout 60 cases, the scanning percentage and total interpretation time per slide decreased, and these two variables were positively correlated. The scanning percentage was not predictive of diagnostic accuracy. Increasing average zoom level, maximum zoom level, and zoom variance were correlated with over-interpretation. |
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 % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 45 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 16% |
| Student > Master | 5 | 11% |
| Researcher | 4 | 9% |
| Student > Doctoral Student | 3 | 7% |
| Student > Bachelor | 3 | 7% |
| Other | 10 | 22% |
| Unknown | 13 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 7 | 16% |
| Computer Science | 6 | 13% |
| Biochemistry, Genetics and Molecular Biology | 4 | 9% |
| Medicine and Dentistry | 3 | 7% |
| Nursing and Health Professions | 2 | 4% |
| Other | 6 | 13% |
| Unknown | 17 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 06 February 2018.
All research outputs
#17,902,783
of 22,985,065 outputs
Outputs from Journal of Digital Imaging
#812
of 1,059 outputs
Outputs of similar age
#224,855
of 313,319 outputs
Outputs of similar age from Journal of Digital Imaging
#24
of 33 outputs
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