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Currently in the UK, a national trial to test the effect of a transition from traditional Full Field Digital Mammography (FFDM) to Digital Breast Tomosynthesis (DBT) is being conducted. DBT, having a higher sensitivity and specificity as compared to FFDM alone, could be a better modality in national breast cancer screening. However, its incorporation in the incredibly busy and detailed UK screening program is difficult. Reading times in DBT have been shown to be longer and strenuous (Connor et al, 2012). Therefore, much research needs to be completed to develop recommendations for its efficiency. One key factor in DBT reading is the progression of fatigue, as both a cause and effect of prolonged reading times. We aimed to develop a program to process real time raw eye tracking data to identify a change in fatigue-state through blink detection. Our focus was on analysing the whole data set and defining blinks through observed events. Two real time signals which the eye tracker generates, namely the left and right ‘Eyelid Opening’ value, were considered. Through assessment of these signals, blinks of varying duration were identified. Additional parameters such as recorded frame sequences and time stamps were added to the processing to delineate the exact occurrence of these blinks during the reading process. We aim to analyse past and future large DBT eye tracked files, with our processing software, to identify the point of fatigue onset in a DBT reading session.
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