Measuring reader fatigue in the interpretation of screening digital breast tomosynthesis (DBT)

Abstract

Objectives: The interpretation of digital breast tomosynthesis (DBT) screening examinations is a complex task for an already overstretched workforce which has the potential to increase pressure on readers leading to fatigue and patient safety issues. Studies in non-medical and medical settings have suggested that changes in blink characteristics can reflect fatigue. The purpose of this study is to investigate the use of blink characteristics as an objective marker of fatigue in readers interpreting DBT breast screening examinations.

Methods: Twenty-six DBT readers involved in the UK PROSPECTS trial interpreted a test set of 40 DBT cases while being observed by an eye tracking device from November 2019 to February 2021. Raw data from the eye tracker were collected and automated processing software was used to produce eye blinking characteristics data which were analysed using multiple linear regression statistical models.

Results: Of the 26 DBT readers recruited, eye tracking data from 23 participants were analysed due to missing data rendering 3 participants' data uninterpretable. The mean reading time per DBT case was 2.81 min. There was a statistically significant increase in blinking duration of 0.38 ms/case as the reading session progressed (p < 0.0001). This was the result of a significant decrease in the number of ultra-short blinks lasting ≤50 ms (p = 0.0005) and a significant increase in longer blinks lasting 51-100 ms (p = 0.008).

Conclusion: Changes in blinking characteristics could serve as objective measures of reader fatigue and may prove useful in the development of DBT reading protocols.

Advances in knowledge: Blink characteristics can be used as an objective measure of fatigue; however there is limited evidence of their use in radiological settings. Our study suggests that changes in blink duration and frequency could be used to monitor fatigue in DBT reading sessions.

Figure 1.

An example of the experimental set-up showing the non-intrusive eye tracking cameras (inferior 3 circles) and the scene camera (single superior circle) positioned on a participant workstation. The monitor to the right was used for eye tracking calibration and monitoring during the experiment.

Figure 2.

Change in average blink duration over case progression. Blink duration significantly increased by 0.38 ms/case (p < 0.0001) as the reading session progressed. Bars represent the mean blink duration per case. Error bars denote 95% confidence interval. Solid line shows exponential moving average. Dotted line shows regression line.

Figure 3.

Average blink count per minute over case progression. Blink frequency decreased non-significantly as reading session progressed (−0.05 blinks/min/case, p = 0.089). Bars indicate mean blink rate per case. Error bars denote 95% confidence interval. Solid line represents exponential moving average. Dotted line predicts rate by linear regression.

Figure 4.

Mean short blink (51–100 ms) count per minute over progression of cases. There was a significant increase in short blinks with session progression (0.024 blinks/min/case, p = 0.008). Bars indicate mean short blink rate per case. Error bars denote 95% confidence interval. Solid line represents exponential moving average. Dotted line predicts rate by linear regression.

Figure 5.

Mean ultrashort blink (≤50 ms) count per minute over progression of cases. Ultrashort blink frequency decreased as reporting session progressed (−0.066 blinks/min/case, p = 0.0005). Bars indicate mean ultrashort blink rate per case. Error bars denote 95% confidence interval. Solid line represents exponential moving average. Dotted line predicts rate by linear regression.

Figure 6.

Tabulation of cases based on diagnostic accuracy against case order. There was no significant change in diagnostic accuracy as reporting progressed (p = 0.491). Superior dots represent correct cases, whereas inferior dots represent incorrect cases. The line shows the estimated accuracy by logistic regression.