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The UK national screening program for breast cancer currently uses Full Field Digital Mammography (FFDM). Various studies have shown that DBT has a higher sensitivity and specificity in identifying early breast cancer apart from benign pathologies, even in very dense breasts. This potentially makes DBT a better screening modality to detect early breast cancer, as well as minimize false positive recall rates. However, DBT has multiple image slices and thereby makes reading cases inherently a longer and potentially more visually fatiguing task. Our previous studies (Dong et al, 2017 and 2018) have demonstrated the impact of institutional training on reading techniques in DBT. The reading technique itself appears to have an effect on total reading time. In other follow-on studies we have employed eye tracking which gives rise to complex data sets, including parameters such as eyelid opening and pupil diameter measures, which can then be employed to gauge blinks and fatigue onset. Findings from this work have guided changes in our blink identification techniques and we have now developed semi-automated programmed processes which can analyze the large data set and provide a more accurate assessment of fatigue and vigilance parameters through blink detection. Here, we have considered ‘eyelid opening’ parameters of both the left and the right eye separately. Having such a separated approach allowed us to tease out particular aspects of blinking. Similar to Schleicher et al (2008), we found there to be ultra-short blinks (30-50 milli seconds), short blinks (51- 100 msecs), long blinks (101-500 msecs) and also microsleeps (>500 msecs). We argue that the changes observed in the frequencies of these blinks can be used as a measure of vigilance and fatigue during DBT reading.
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