Dr. Adnan Taib Profile

Dr. Adnan Taib

at The Univ of Nottingham

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Publications (2)

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Proceedings Article | 3 April 2023 Presentation + Paper

Challenges and solutions to processing and visualising eye tracking data from digital pathology studies

George J. W. Partridge, Peter Phillips, Adnan Taib, Yan Chen

Proceedings Volume 12467, 1246706 (2023) https://doi.org/10.1117/12.2652981

KEYWORDS: Eye tracking, Pathology, Displays, Eye, Visualization, Software development

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Digital Pathology (DP) reporting workstations permit eye tracking experiments which can aid our understanding of reading strategies and medical errors in pathology. However, eye tracking with DP slides is complex due to the nature of the slide viewing process: slide panning and zooming. Eye tracking technology records gaze coordinates to a screen surface, but these coordinates do not account for the ever-changing on-screen content (due to slide navigation), and therefore it is essential to track pathologists’ slide navigation to determine where on the slide the pathologist has viewed and what features were fixated. Additionally, visualising the resulting eye tracking data proves challenging due to the zooming component. Other eye tracking studies in DP have accounted for slide navigation by employing custom slide viewers that output slide movements as a data stream with the eye tracking data which are co-registered for analysis. We are conducting a DP eye tracking study using a commercial slide viewer which has been adopted at selected UK hospital sites, but slide movement data cannot be outputted as a data stream in this context. Therefore, we’re developing a software platform using computer vision techniques that can be applied to the recorded screen capture of the DP workstation which is synchronised with the eye tracking data. The developed algorithm could be adapted for use with other commercial slide viewers for future studies. Here, we explore how studies have addressed these issues and we discuss our approach.

Proceedings Article | 3 April 2023 Presentation + Paper

Identifying and preventing fatigue in digital breast tomosynthesis

Adnan Taib, George Partridge, Iain Darker, Peter Phillips, Yan Chen

Proceedings Volume 12467, 1246707 (2023) https://doi.org/10.1117/12.2654936

KEYWORDS: Digital breast tomosynthesis, Eye tracking, Diagnostics, Breast, Material fatigue, Displays, Cameras, Tunable filters, Statistical analysis, Signal detection

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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.

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