41 research outputs found

    Digital training platform for interpreting radiographic images of the chest

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    Introduction: Time delays and errors exist which lead to delays in patient care and misdiagnosis. Reporting clinicians follow guidance to form their own search strategy. However, little research has tested these training guides. With the use of eye tracking technology and expert input we developed a digital training platform to be used in chest image interpretation learning. Methods: Two sections of a digital training platform were planned and developed; A) a search strategy training tool to assist reporters during their interpretation of images, and B) an educational tool to communicate the search strategies of expert viewers to trainees by using eye tracking technology. Results: A digital training platform for use in chest image interpretation was created based on evidence within the literature, expert input and two search strategies previously used in clinical practice. Images and diagrams, aiding translation of the platform content, were incorporated where possible. The platform is structured to allow the chest image interpretation process to be clear, concise and methodical. Conclusion: A search strategy was incorporated within the tool to investigate its use, with the possibility that it could be recommended as an evidence based approach for use by reporting clinicians. Eye tracking, a checklist and voice recordings have been combined to form a multi-dimensional learning tool, which has never been used in chest image interpretation learning before. The training platform for use in chest image interpretation learning has been designed, created and digitised. Future work will establish the efficacy of the developed approaches

    Pre-registration UK diagnostic radiography student ability and confidence in interpretation of chest X-rays

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    Introduction Chest X-rays are the most frequently requested X-ray imaging in English hospitals. This study aimed to assess final year UK radiography students' confidence and ability in image interpretation of chest X-rays. Methods Thirty-three diagnostic radiography students were invited to assess their confidence and ability in interpreting chest x-rays from a bank of n=10 cases using multiple choice answers. Data analysis included 2x2 contingency tables, Kappa for inter-rater reliability, a Likert scale of confidence for each case, and questions to assess individual interpretation skills and ways to increase the learning of the subject. Results Twenty-three students participated in the study. The pooled accuracy achieved was 61% (95% CI 38.4-77.7; k=0.22). The degree of confidence and ability varied depending upon the student and the conditions observed. High confidence was noted with COVID-19 (n=12/23; 52%), lung metastasis (n=14/23; 61%), and pneumothorax (n=13/23; 57%). Low confidence was noted with conditions of consolidation (n=8/23; 35%), haemothorax (n=8/23; 35%), and surgical emphysema (n=8/23; 35%). From the sample n=11 (48%), participants stated they felt they had the knowledge to interpret chest X-rays required for a newly qualified radiographer. Conclusion The results demonstrated final year radiography students' confidence and ability in image interpretation of chest X-rays. Student feedback indicated a preference for learning support through university lectures, online study resources, and time spent with reporting radiographers on clinical practice to improve ability and confidence in interpreting chest X-rays

    The Application of Eye-Tracking Technology in the Assessment of Radiology Practices: A Systematic Review

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    The aim of this review is to provide an in-depth analysis of literature pertaining to the use of eye-tracking equipment in the evaluation of radiological image interpretation by professionals in clinical practice. A systematic search of current literature was conducted through the databases of CINAHL, Medline, ProQuest, PubMed, Scopus, Web of Science and Wiley Online Library. A total of 25 articles were included in the final analysis. The literature gathered referenced four main discussions, which were competency assessment, educational tools, visual search behaviour and assistive aid evaluations. The majority of articles (68%) referenced to the competency assessment of professional groups yet appeared to have conflicting results within the categories of speed and eye-metrics. Significant conclusions could be made pertaining to confidence (100%) and accuracy measurements (56%), which suggested a background of higher experience correlates to a higher rate of accuracy and a higher confidence level. Other findings regarding the main themes focused on eye-tracking as an educational tool, where the literature suggests that such equipment may be useful in improving educational repertoire and interpretation technique. Literature pertaining to the visual search behaviour analysis and the evaluation of assistive aids did not provide strong conclusions due to research limitations. Whilst the use of eye-tracking in the analysis of radiological practices is a promising new venture to quantify the interpretation patterns of professionals, undertaking future research is recommended to solidify conclusions and provide greater insight

    A situated method for modelling and analysing the efficiency of cognitive activity during the radiology reporting workflow using eye-tracking

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    The success of modern medical imaging systems has created a data overload problem, where an ever-increasing number of examinations, generate more images per study, which all need to be evaluated by radiologists or other reporting practitioners. This operational bottleneck hasthe potentialto create fatigue and burnout due to the high mental workload that is required to keep up with the demand. The focus of this problem centres around the cognitive complexity of the radiology reporting workflow, and the associated workstation interactions involved in diagnostic report generation. There has been a significant body of work evaluating the behaviour of radiologists using controlled laboratory-based techniques, but these non-naturalistic studies fail to address the highly context dependant nature of the radiology reporting workflow. For example, the early eye-tracking work of Charmody et al; the psychometric studies by Krupinksi et al; and also the workstation interaction evaluations of Moise et al; whilst highly principled, can be all be questioned on the grounds of ecological validity and authenticity. This thesis asserts that the only way to truly understand and resolve the radiology data overload problem, is by developing a situated method for observing the reporting workflow that can evaluate the behaviours of the reporting clinicians in relation to their authentic reporting context. To this end, this study has set out to develop a new approach for observing and analysing the cognitive activities of the reporters relative to the demands of their genuine working environment, and supported through the application of a Critical Realist’s perspective to naturalistic workplace observations. This goal was achieved through the development of four key project deliverables: • An in-depth exploratory study of the radiology overload problem based on an extensive literature review and situated observations of authentic reporting workflows. • A descriptive hierarchical activity modelof the reporting workflow that can be understood by both clinicians, application designers and researchers. • A generalised methodology and research protocolfor conducting situated observations of the radiology reporting workflow, using an analysis based on the process tracing of sequencesof Object Related Actions, captured with eye-tracking and multimodal recordings. • A set of case studies demonstrating the applicability of the research protocol involving 5 Radiology Consultants, 2 Radiology Registrars and one Reporting Radiographer at a single NHS Hospital within the UK. The final workflow evaluation of the case studies demonstrated that activities such as error correction, and the collection of supporting radiological information from previous studies is complex, time consuming and cognitively demanding. These types of activities are characterised by long, low utility actions that correspond to what Kahneman refers to as “Thinking Slow”. Also, the participants appeared to be self-optimising their workflow via a sparse use of complex functionality and system tools. From these observations, the author recommends that any intervention that can reduce the number and the duration of the object related actions used to produce radiology reports, will reduce cognitive load, increase overall efficiency, and go some way to alleviate the data overload problem. 4 This study establishes a new set of situated techniques that are able to capture and quantify the complex dynamicactivities that make up the radiology reporting workflow. Itis hoped that the ability to distil usefuland impactful insightsfrom the user’s workstation behaviours can be used as the basis for further development in the area of workflow analysis and redesign, which will ultimately improve the working lives of Radiologists and other Reporting Clinicians. Lastly, the generic nature of these techniques make them amenable for use within any type of complex sociotechnical human factors study related to the cognitive efficiency of the user

    Eye Tracking Methods for Analysis of Visuo-Cognitive Behavior in Medical Imaging

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    Predictive modeling of human visual search behavior and the underlying metacognitive processes is now possible thanks to significant advances in bio-sensing device technology and machine intelligence. Eye tracking bio-sensors, for example, can measure psycho-physiological response through change events in configuration of the human eye. These events include positional changes such as visual fixation, saccadic movements, and scanpath, and non-positional changes such as blinks and pupil dilation and constriction. Using data from eye-tracking sensors, we can model human perception, cognitive processes, and responses to external stimuli. In this study, we investigated the visuo-cognitive behavior of clinicians during the diagnostic decision process for breast cancer screening under clinically equivalent experimental conditions involving multiple monitors and breast projection views. Using a head-mounted eye tracking device and a customized user interface, we recorded eye change events and diagnostic decisions from 10 clinicians (three breast-imaging radiologists and seven Radiology residents) for a corpus of 100 screening mammograms (comprising cases of varied pathology and breast parenchyma density). We proposed novel features and gaze analysis techniques, which help to encode discriminative pattern changes in positional and non-positional measures of eye events. These changes were shown to correlate with individual image readers' identity and experience level, mammographic case pathology and breast parenchyma density, and diagnostic decision. Furthermore, our results suggest that a combination of machine intelligence and bio-sensing modalities can provide adequate predictive capability for the characterization of a mammographic case and image readers diagnostic performance. Lastly, features characterizing eye movements can be utilized for biometric identification purposes. These findings are impactful in real-time performance monitoring and personalized intelligent training and evaluation systems in screening mammography. Further, the developed algorithms are applicable in other application domains involving high-risk visual tasks
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