Breast cancer screening is a critical component of healthcare, engaging over 2.23 million women annually in the UK. The National Health Service Breast Screening Program requires breast screening readers to participate in the PERFORMS scheme for quality assurance and training. This study investigates the potential of a specificity-focused test set within PERFORMS to reduce false positive recalls and enhance readers’ image interpretation skills in England. The specificity set comprised 60 challenging breast screening cases, including 15 malignant and 45 benign/normal cases. Among the 529 participating readers in the pre-specificity (pre-SP) round, post-specificity (post-SP) round, or both, they were categorized as those who underwent the specificity set (n = 317) and those who did not (n = 212). The post-SP recall rate was significantly lower (36.0%) compared to the pre-SP rate (37.7%) (p = 0.000). This decrease was more pronounced in those who undertook the specificity set (-2.7%) than in those who did not (-0.5%) (p = 0.0018). The correct return to screen rate and positive predictive value (PPV) improved in the post-SP set, with rates of 86.4% and 76.0%, respectively, compared to 82.4% and 68.5% in the pre-SP set (p = 0.000). The increase in correct return to screen and PPV was comparable between those who undertook the specificity set and those who did not (p = 0.0933 and p = 0.2515, respectively). In conclusion, integrating a specificity-focused test set within PERFORMS shows promise in positively impacting breast screening reader performance, offering insights for future training and quality assurance enhancements.
KEYWORDS: Digital breast tomosynthesis, Eye tracking, Diagnostics, Breast, Material fatigue, Displays, Cameras, Tunable filters, Statistical analysis, Signal detection
Digital Breast Tomosynthesis (DBT) increases breast cancer detection rates but produces a significantly greater number
of images for screeners to read compared to traditional two-dimensional (2-D) mammograms. Putting screeners at risk of
fatigue and therefore error in detecting cancers.
The aim of this study was to explore if screeners showed differences in subjective fatigue, blink metrics and diagnostic
accuracy during a DBT reading session with and without breaks.
Prospective study including 45 participants from 6 different hospital sites around England between December 2020 to
April 2022. Non-intrusive, screen mounted eye tracking cameras (60Hz sampling rate) were set up in the participant’s
natural reading environment. Forty DBT cases were read in a random order (47.5% malignant, 12.5% benign, 40%
normal). Each breast was rated as normal or benign (return to screen) or indeterminate, suspicious or highly suspicious
(recall). Twenty-one participants had a break at approximately 40 minutes into the session.
Participants without a break showed a significantly greater difference in subjective fatigue before and after the reporting
session (44% vs 33%, p=0.037). Furthermore, those without breaks exhibited significantly greater blinks per minute
(15.75 vs 13.25, p<0.001) and blink duration (milliseconds) (296 vs 286, p<0.001). There was no significant difference
in overall accuracy between the cohorts (p=0.921).
Blink metrics have the potential to be used in identifying early onset of fatigue during reading sessions.
Purpose Digital breast tomosynthesis (DBT) exhibits increased sensitivity and specificity compared to 2D mammography (DM), but DBT images are complex and interpretation takes longer. Clinicians may fatigue or hit a cognitive limit sooner when reading DBT, potentially reducing diagnostic accuracy. Eye blink behaviour was investigated to explore fatigue and cognitive load. Methods Screeners (N=47) from five UK breast screening centres were eye tracked as they read 40 DBT cases (15 normal, 6 benign and 19 malignant), from November 2019-July 2021. Differences in diagnostic accuracy and blink behaviour were analysed over the course of the reading session. Blink rates and case durations were investigated by case malignancy and outcome using T-tests and ANOVAs (α=0.05). Results Blink rates were higher on malignant cases than on normal cases (p=0.004), and blink rates were higher for cases with true positive outcomes than for cases with true negative outcomes (p=0.013). Participants spent less time on malignant cases than normal or benign cases (ps=<0.0001), whilst spending more time on cases with a false positive outcome than on cases with a true negative or true positive outcome (ps<0.0001). No significant difference in blink rate or diagnostic performance by time through reporting session. Conclusion Differences in blink rate and time on case are associated with case malignancy and outcome, potentially reflecting varying cognitive demand and interpretation strategies. Further investigation into blinking during medical image interpretation may identify robust signals of cognition and fatigue that could be used for education and training purposes, whilst indicating optimal screening session duration.
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.
Higher breast density is associated with a greater chance of developing breast cancer. Additionally, it is well known that
higher mammographic breast density is associated with increased difficulty in accurately identifying breast cancer.
However, comparatively little is known of the reliability of breast density judgements. All UK breast screeners
(primarily radiologists and technologists) annually participate in the PERFORMS self-assessment scheme where they
make several judgements about series of challenging recent screening cases of known outcomes. As part of this process,
for each case, they provide a radiological assessment of the likelihood of cancer on a confidence scale, alongside an
assessment of case density using a three point scale. Analysis of the data from two years of the scheme found that the
degree of agreement on case density was significantly greater than no agreement (p < .001). However, only a moderate
degree of inter-rater reliability was exhibited (κ = .44) with significant differences between the occupational groups. The
reasons for differences between the occupational groups and the relationship between agreement on density rating and
case reading ability are explored.
Findings from the current UK national research programme, MEDUSA (Multi Environment Deployable Universal
Software Application), are presented. MEDUSA brings together two approaches to facilitate the design of an automatic,
CCTV-based firearm detection system: psychological-to elicit strategies used by CCTV operators; and machine
vision-to identify key cues derived from camera imagery. Potentially effective human- and machine-based strategies
have been identified; these will form elements of the final system. The efficacies of these algorithms have been tested on
staged CCTV footage in discriminating between firearms and matched distractor objects. Early results indicate the
potential for this combined approach.
Historically, radiology research has been dominated by chest and breast screening. Few studies have examined complex
interpretative tasks such as the reading of multidimensional brain CT or MRI scans. Additionally, no studies at the time
of writing have explored the interpretation of stroke images; from novices through to experienced practitioners using eye
movement analysis. Finally, there appears a lack of evidence on the clinical effects of radiology reports and their
influence on image appraisal and clinical diagnosis. A computer-based, eye-tracking study was designed to assess
diagnostic accuracy and interpretation in stroke CT and MR imagery. Eight predetermined clinical cases, five images per
case, were presented to participants (novices, trainee, and radiologists; n=8). The presence or absence of abnormalities
was rated on a five-point Likert scale and their locations reported. Half cases of the cases were accompanied by clinical
information; half were not, to assess the impact of information on observer performance. Results highlight differences in
visual search patterns amongst novice, trainee and expert observers; the most marked differences occurred between
novice readers and experts. Experts spent more time in challenging areas of interest (AOI) than novices and trainee, and
were more confident unless a lesion was large and obvious. The time to first AOI fixation differed by size, shape and
clarity of lesion. 'Time to lesion' dropped significantly when recognition appeared to occur between slices. The
influence of clinical information was minimal.
KEYWORDS: Firearms, Video surveillance, Surveillance, Video, Cameras, Visualization, Signal detection, Image processing, Detection and tracking algorithms, Data modeling
CCTV operators are able to detect firearms, via CCTV, but their capacity for surveillance is limited. Thus, it is desirable to automate the monitoring of CCTV cameras for firearms using machine vision techniques. The abilities of CCTV operators to detect concealed and unconcealed firearms in CCTV footage were quantified within a signal detection framework. Additionally, the visual search strategies adopted by the CCTV operators were elicited and their efficacies indexed with respect to signal detection performance, separately for concealed and unconcealed firearms. Future work will automate effective, human visual search strategies using image processing algorithms.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.