OBJECTIVE: This article describes the time course of lesion detection on digital mammograms using data about both eye position and decision time to compare performance between experienced mammographers and trainees. Research indicates that a longer decision time works against performance in the interpretation of chest radiographs because the likelihood of error is increased, particularly for trainees. Is this relation between decision time and performance also true for interpreting mammograms? Is there an optimal decision time-performance trade-off for detecting breast lesions? MATERIALS AND METHODS: Six radiology trainees (experience, 302-976 cases) and three mammographers (experience, 3000-5000 cases per year) reviewed 40 test cases. Each test case was represented by two mammograms that showed different views of the same breast. Twenty breasts contained suspicious lesions, and 20 were lesion-free. An interactive computer display system with an eye-head tracker measured the timing of decisions, where visual attention was directed, and how much time was spent fixating on a region of interest for each decision. Eye position was monitored during an initial-decision phase, and decision times were measured throughout a final-decision phase during which suspicious lesions recognized initially were interpreted and localized. Performance was analyzed using localization receiver operating characteristic curves. RESULTS: The time course of interpreting mammograms is similar to that for interpreting chest radiographs. Mammographers detected 71% of the true lesions within 25 sec, and trainees detected 46% within 40 sec. Both a fixation dwell time of 1000 msec and a high level of confidence in the decision were associated with the detection of true lesions for the mammographers but not for the trainees. CONCLUSION: Mammographers detected most breast lesions by global recognition within 25 sec, but trainees took more time. Prolonging one's search beyond the global recognition phase yielded few new lesions and increased the risk of error.
OBJECTIVE: This article describes the time course of lesion detection on digital mammograms using data about both eye position and decision time to compare performance between experienced mammographers and trainees. Research indicates that a longer decision time works against performance in the interpretation of chest radiographs because the likelihood of error is increased, particularly for trainees. Is this relation between decision time and performance also true for interpreting mammograms? Is there an optimal decision time-performance trade-off for detecting breast lesions? MATERIALS AND METHODS: Six radiology trainees (experience, 302-976 cases) and three mammographers (experience, 3000-5000 cases per year) reviewed 40 test cases. Each test case was represented by two mammograms that showed different views of the same breast. Twenty breasts contained suspicious lesions, and 20 were lesion-free. An interactive computer display system with an eye-head tracker measured the timing of decisions, where visual attention was directed, and how much time was spent fixating on a region of interest for each decision. Eye position was monitored during an initial-decision phase, and decision times were measured throughout a final-decision phase during which suspicious lesions recognized initially were interpreted and localized. Performance was analyzed using localization receiver operating characteristic curves. RESULTS: The time course of interpreting mammograms is similar to that for interpreting chest radiographs. Mammographers detected 71% of the true lesions within 25 sec, and trainees detected 46% within 40 sec. Both a fixation dwell time of 1000 msec and a high level of confidence in the decision were associated with the detection of true lesions for the mammographers but not for the trainees. CONCLUSION: Mammographers detected most breast lesions by global recognition within 25 sec, but trainees took more time. Prolonging one's search beyond the global recognition phase yielded few new lesions and increased the risk of error.
Authors: Xiao Hui Wang; Janet E Durick; Amy Lu; David L Herbert; Saraswathi K Golla; Kristin Foley; C Samia Piracha; Dilip D Shinde; Betty E Shindel; Carl R Fuhrman; Cynthia A Britton; Diane C Strollo; Sherry S Shang; Joan M Lacomis; Walter F Good Journal: J Digit Imaging Date: 2007-09-15 Impact factor: 4.056
Authors: Trafton Drew; Melissa Le-Hoa Vo; Alex Olwal; Francine Jacobson; Steven E Seltzer; Jeremy M Wolfe Journal: J Vis Date: 2013-08-06 Impact factor: 2.240