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Background  Radiological techniques for breast cancer detection are undergoing a massive technological shift—moving from mammography, a process that takes a two-dimensional (2D) image of breast tissue, to tomosynthesis, a technique that creates a segmented-three-dimensional (3D) image. There are distinct benefits of tomosynthesis over mammography with radiologists having fewer false positives and more accurate detections; yet there is a significant and meaningful disadvantage with tomosynthesis in that it takes longer to evaluate each patient. This added time can dramatically impact workflow and have negative attentional and cognitive impacts on interpretation of medical images. To better understand the nature of segmented-3D visual search and the implications for radiology, the current study looked to establish a new testing platform that could reliably examine differences between 2D and segmented-3D search.    Results  In Experiment 1, both professionals (radiology residents and certified radiologists) and non-professionals (undergraduate students) were found to have fewer false positives and were more accurate in segmented-3D displays, but at the cost of taking significantly longer in search. Experiment 2 tested a second group of non-professional participants, using a background that more closely resembled a mammogram, and replicated the results of Experiment 1—search was more accurate and there were fewer false alarms in segmented 3D displays but took more time.    Conclusion  The results of Experiments 1 and 2 matched the performance patterns found in previous radiology studies and in the clinic, suggesting this novel experimental paradigm potentially provides a flexible and cost-effective tool that can be utilized with non-professional populations to inform relevant visual search performance. From an academic perspective, this paradigm holds promise for examining the nature of segmented-3D visual search.

Mammography to tomosynthesis: examining the differences between two-dimensional and segmented-three-dimensional visual search