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Hao Q, Ma L, Sbert M, Feixas M, Zhang J. Gaze Information Channel in Van Gogh's Paintings. ENTROPY (BASEL, SWITZERLAND) 2020; 22:e22050540. [PMID: 33286312 PMCID: PMC7517036 DOI: 10.3390/e22050540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Revised: 05/02/2020] [Accepted: 05/04/2020] [Indexed: 06/12/2023]
Abstract
This paper uses quantitative eye tracking indicators to analyze the relationship between images of paintings and human viewing. First, we build the eye tracking fixation sequences through areas of interest (AOIs) into an information channel, the gaze channel. Although this channel can be interpreted as a generalization of a first-order Markov chain, we show that the gaze channel is fully independent of this interpretation, and stands even when first-order Markov chain modeling would no longer fit. The entropy of the equilibrium distribution and the conditional entropy of a Markov chain are extended with additional information-theoretic measures, such as joint entropy, mutual information, and conditional entropy of each area of interest. Then, the gaze information channel is applied to analyze a subset of Van Gogh paintings. Van Gogh artworks, classified by art critics into several periods, have been studied under computational aesthetics measures, which include the use of Kolmogorov complexity and permutation entropy. The gaze information channel paradigm allows the information-theoretic measures to analyze both individual gaze behavior and clustered behavior from observers and paintings. Finally, we show that there is a clear correlation between the gaze information channel quantities that come from direct human observation, and the computational aesthetics measures that do not rely on any human observation at all.
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Affiliation(s)
- Qiaohong Hao
- College of Intelligence and Computing, Tianjin University, Yaguan Road 135, Tianjin 300350, China; (Q.H.); (L.M.)
| | - Lijing Ma
- College of Intelligence and Computing, Tianjin University, Yaguan Road 135, Tianjin 300350, China; (Q.H.); (L.M.)
| | - Mateu Sbert
- Institute of Informatics and Applications, University of Girona, 17003 Girona, Spain;
| | - Miquel Feixas
- Institute of Informatics and Applications, University of Girona, 17003 Girona, Spain;
| | - Jiawan Zhang
- College of Intelligence and Computing, Tianjin University, Yaguan Road 135, Tianjin 300350, China; (Q.H.); (L.M.)
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Shirley K, Williams M, McLaughlin L, Parker N, Bond R. Impact of an educational intervention on eye gaze behaviour in retinal image interpretation by consultant and trainee ophthalmologists. Health Informatics J 2019; 26:1419-1430. [PMID: 31630618 DOI: 10.1177/1460458219881337] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This study uses eye-tracking technology to assess the differences in gaze behaviours between ophthalmologists of different experience levels while interpreting retinal images of diabetic retinopathy. The differences in gaze behaviours before and after a teaching intervention which introduced a suggested search strategy is also investigated. A total of 9 trainees and 10 consultant ophthalmologists interpreted six retinal images. They were then shown a 5-min tutorial that demonstrated a search strategy. This was followed by six further retinal image interpretations. Participants completed questionnaires indicating clinical signs seen, appropriate retinopathy grade, and confidence. Eye movements were tracked during each interpretation.Overall, trainees compared to consultants demonstrated more uncertain and unstructured gaze behaviours. Trainee eye gaze metrics included: longer interpretation time, 36.5 s (SD = 6.2 vs. 31.4 s) (SD = 4.2) (p = 0.024), higher visit count, 17.38 visits (SD = 5.13) versus 12.18 visits(SD = 2.64) (p = 0.01), higher proportion of fixation, 57.0 per cent (SD = 5) versus 50.5 per cent (SD = 5) (p = 0.05) and shorter time to first fixation, 0.232 s (SD = 0.10) versus 0.821 s (SD = 0.77) (p = 0.001), respectively. The teaching intervention resulted in more focused gaze patterns in both groups. Pre-intervention and post-intervention mean proportion fixation on areas of interest were 38.6 per cent (SD = 6.8) and 51.8 per cent (SD = 13.9) for the trainee group, respectively, and 39.9 per cent (SD = 4.1) and 50.9 per cent (SD = 9.3) for the consultant group (p = 0.01).Consultants used more systematic and efficient approaches than trainees during interpretation. After the introduction of a suggested search strategy, trainees showed trends towards consultant eye gaze behaviours. Eye tracking gives an interesting insight into the thought processes of physicians carrying out complex tasks. The implication is that eye tracking may have future use in teaching and assessment. Its use in objectively assessing different teaching strategies could be a valuable tool for medical education.
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Affiliation(s)
- Kate Shirley
- Royal Victoria Hospital, UK; Queen's University Belfast, UK
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Currie J, Bond RR, McCullagh P, Black P, Finlay DD, Gallagher S, Kearney P, Peace A, Stoyanov D, Bicknell CD, Leslie S, Gallagher AG. Wearable technology-based metrics for predicting operator performance during cardiac catheterisation. Int J Comput Assist Radiol Surg 2019; 14:645-657. [PMID: 30730031 PMCID: PMC6420895 DOI: 10.1007/s11548-019-01918-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2018] [Accepted: 01/17/2019] [Indexed: 01/16/2023]
Abstract
Introduction Unobtrusive metrics that can auto-assess performance during clinical procedures are of value. Three approaches to deriving wearable technology-based metrics are explored: (1) eye tracking, (2) psychophysiological measurements [e.g. electrodermal activity (EDA)] and (3) arm and hand movement via accelerometry. We also measure attentional capacity by tasking the operator with an additional task to track an unrelated object during the procedure. Methods Two aspects of performance are measured: (1) using eye gaze and psychophysiology metrics and (2) measuring attentional capacity via an additional unrelated task (to monitor a visual stimulus/playing cards). The aim was to identify metrics that can be used to automatically discriminate between levels of performance or at least between novices and experts. The study was conducted using two groups: (1) novice operators and (2) expert operators. Both groups made two attempts at a coronary angiography procedure using a full-physics virtual reality simulator. Participants wore eye tracking glasses and an E4 wearable wristband. Areas of interest were defined to track visual attention on display screens, including: (1) X-ray, (2) vital signs, (3) instruments and (4) the stimulus screen (for measuring attentional capacity). Results Experts provided greater dwell time (63% vs 42%, p = 0.03) and fixations (50% vs 34%, p = 0.04) on display screens. They also provided greater dwell time (11% vs 5%, p = 0.006) and fixations (9% vs 4%, p = 0.007) when selecting instruments. The experts’ performance for tracking the unrelated object during the visual stimulus task negatively correlated with total errors (r = − 0.95, p = 0.0009). Experts also had a higher standard deviation of EDA (2.52 µS vs 0.89 µS, p = 0.04). Conclusions Eye tracking metrics may help discriminate between a novice and expert operator, by showing that experts maintain greater visual attention on the display screens. In addition, the visual stimulus study shows that an unrelated task can measure attentional capacity. Trial registration This work is registered through clinicaltrials.gov, a service of the U.S. National Health Institute, and is identified by the trial reference: NCT02928796.
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Affiliation(s)
- Jonathan Currie
- School of Computing, Jordanstown Campus, Ulster University, Shore Road, Newtownabbey, BT37 0QB Northern Ireland UK
| | - Raymond R. Bond
- School of Computing, Jordanstown Campus, Ulster University, Shore Road, Newtownabbey, BT37 0QB Northern Ireland UK
| | - Paul McCullagh
- School of Computing, Jordanstown Campus, Ulster University, Shore Road, Newtownabbey, BT37 0QB Northern Ireland UK
| | - Pauline Black
- School of Nursing, Magee Campus, Ulster University, Londonderry, BT48 7JL Northern Ireland UK
| | - Dewar D. Finlay
- School of Engineering, Jordanstown Campus, Ulster University, Londonderry, BT48 7JL Northern Ireland UK
| | - Stephen Gallagher
- School of Psychology, Coleraine Campus, Ulster University, Cromore Road, Coleraine, BT52 1SA Northern Ireland UK
| | - Peter Kearney
- Application of Science to Simulation Based Education and Research on Training (ASSERT) Centre, University College Cork, Cork, Ireland
| | - Aaron Peace
- Clinical Translational Research and Innovation Centre (C-TRIC), Londonderry, Northern Ireland UK
| | | | | | | | - Anthony G. Gallagher
- Application of Science to Simulation Based Education and Research on Training (ASSERT) Centre, University College Cork, Cork, Ireland
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Sharma V, Fong A, Beckman RA, Rao S, Boca SM, McGarvey PB, Ratwani RM, Madhavan S. Eye-Tracking Study to Enhance Usability of Molecular Diagnostics Reports in Cancer Precision Medicine. JCO Precis Oncol 2018; 2:1-11. [PMID: 35135129 DOI: 10.1200/po.17.00296] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE We conducted usability studies on commercially available molecular diagnostic (MDX) test reports to identify strengths and weaknesses in content and form that drive clinical decision making. Given routine genomic testing in cancer medicine, oncologists must interpret MDX reports as well as evidence concerning clinical utility of biomarkers accurately for treatment or trial selection. This work aims to evaluate effectiveness of MDX reports in facilitating cancer treatment planning. METHODS Fourteen clinicians at an academic tertiary care medical facility, with a wide range of experience in oncology and in the use of molecular testing, participated in this study. Three commercially available, widely used, Clinical Laboratory Improvement Amendments (CLIA)-certified, College of American Pathologists (CAP)-accredited test reports (labeled Laboratories A, B, and C) were used. Eye tracking, surveys, and think-aloud protocols were used to collect usability data for these MDX reports focusing on ease of comprehension and actionability. RESULTS Clinicians found two primary areas in molecular diagnostic reports most useful for patient care: therapy options with benefit or lack of benefit to patients, including enrolling clinical trials; and pathogenic tumor molecular anomalies detected. Therapeutic implications and therapy classes such as US Food and Drug Administration-approved off-label, on-label, clinical trials were critical for decision making. However, all reports had usability and comprehension issues in these areas and could be improved. CONCLUSION Focused usability studies can help drive our understanding of the clinical workflow for use of molecular diagnostic tests in cancer care. This in turn can have major effects on quality of care, outcomes, costs, and patient satisfaction. This study demonstrates the use of specific usability techniques (eye tracking and think-aloud protocols) to help clinical laboratories improve MDX report design in a precision oncology treatment setting.
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Affiliation(s)
- Vishakha Sharma
- Vishakha Sharma, Robert A. Beckman, Shruti Rao, Simina M. Boca, Peter B. McGarvey, and Subha Madhavan, Georgetown University; Vishakha Sharma, Robert A. Beckman, Simina M. Boca, and Subha Madhavan, Georgetown University Medical Center; Allan Fong and Raj M. Ratwani, National Center for Human Factors in Healthcare, MedStar Health; and Raj M. Ratwani, Georgetown University School of Medicine, Washington, DC
| | - Allan Fong
- Vishakha Sharma, Robert A. Beckman, Shruti Rao, Simina M. Boca, Peter B. McGarvey, and Subha Madhavan, Georgetown University; Vishakha Sharma, Robert A. Beckman, Simina M. Boca, and Subha Madhavan, Georgetown University Medical Center; Allan Fong and Raj M. Ratwani, National Center for Human Factors in Healthcare, MedStar Health; and Raj M. Ratwani, Georgetown University School of Medicine, Washington, DC
| | - Robert A Beckman
- Vishakha Sharma, Robert A. Beckman, Shruti Rao, Simina M. Boca, Peter B. McGarvey, and Subha Madhavan, Georgetown University; Vishakha Sharma, Robert A. Beckman, Simina M. Boca, and Subha Madhavan, Georgetown University Medical Center; Allan Fong and Raj M. Ratwani, National Center for Human Factors in Healthcare, MedStar Health; and Raj M. Ratwani, Georgetown University School of Medicine, Washington, DC
| | - Shruti Rao
- Vishakha Sharma, Robert A. Beckman, Shruti Rao, Simina M. Boca, Peter B. McGarvey, and Subha Madhavan, Georgetown University; Vishakha Sharma, Robert A. Beckman, Simina M. Boca, and Subha Madhavan, Georgetown University Medical Center; Allan Fong and Raj M. Ratwani, National Center for Human Factors in Healthcare, MedStar Health; and Raj M. Ratwani, Georgetown University School of Medicine, Washington, DC
| | - Simina M Boca
- Vishakha Sharma, Robert A. Beckman, Shruti Rao, Simina M. Boca, Peter B. McGarvey, and Subha Madhavan, Georgetown University; Vishakha Sharma, Robert A. Beckman, Simina M. Boca, and Subha Madhavan, Georgetown University Medical Center; Allan Fong and Raj M. Ratwani, National Center for Human Factors in Healthcare, MedStar Health; and Raj M. Ratwani, Georgetown University School of Medicine, Washington, DC
| | - Peter B McGarvey
- Vishakha Sharma, Robert A. Beckman, Shruti Rao, Simina M. Boca, Peter B. McGarvey, and Subha Madhavan, Georgetown University; Vishakha Sharma, Robert A. Beckman, Simina M. Boca, and Subha Madhavan, Georgetown University Medical Center; Allan Fong and Raj M. Ratwani, National Center for Human Factors in Healthcare, MedStar Health; and Raj M. Ratwani, Georgetown University School of Medicine, Washington, DC
| | - Raj M Ratwani
- Vishakha Sharma, Robert A. Beckman, Shruti Rao, Simina M. Boca, Peter B. McGarvey, and Subha Madhavan, Georgetown University; Vishakha Sharma, Robert A. Beckman, Simina M. Boca, and Subha Madhavan, Georgetown University Medical Center; Allan Fong and Raj M. Ratwani, National Center for Human Factors in Healthcare, MedStar Health; and Raj M. Ratwani, Georgetown University School of Medicine, Washington, DC
| | - Subha Madhavan
- Vishakha Sharma, Robert A. Beckman, Shruti Rao, Simina M. Boca, Peter B. McGarvey, and Subha Madhavan, Georgetown University; Vishakha Sharma, Robert A. Beckman, Simina M. Boca, and Subha Madhavan, Georgetown University Medical Center; Allan Fong and Raj M. Ratwani, National Center for Human Factors in Healthcare, MedStar Health; and Raj M. Ratwani, Georgetown University School of Medicine, Washington, DC
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Hettinger AZ, Roth EM, Bisantz AM. Cognitive engineering and health informatics: Applications and intersections. J Biomed Inform 2017; 67:21-33. [PMID: 28126605 DOI: 10.1016/j.jbi.2017.01.010] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 01/13/2017] [Accepted: 01/17/2017] [Indexed: 10/20/2022]
Abstract
Cognitive engineering is an applied field with roots in both cognitive science and engineering that has been used to support design of information displays, decision support, human-automation interaction, and training in numerous high risk domains ranging from nuclear power plant control to transportation and defense systems. Cognitive engineering provides a set of structured, analytic methods for data collection and analysis that intersect with and complement methods of Cognitive Informatics. These methods support discovery of aspects of the work that make performance challenging, as well as the knowledge, skills, and strategies that experts use to meet those challenges. Importantly, cognitive engineering methods provide novel representations that highlight the inherent complexities of the work domain and traceable links between the results of cognitive analyses and actionable design requirements. This article provides an overview of relevant cognitive engineering methods, and illustrates how they have been applied to the design of health information technology (HIT) systems. Additionally, although cognitive engineering methods have been applied in the design of user-centered informatics systems, methods drawn from informatics are not typically incorporated into a cognitive engineering analysis. This article presents a discussion regarding ways in which data-rich methods can inform cognitive engineering.
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Affiliation(s)
- A Zachary Hettinger
- Department of Emergency Medicine, Georgetown University School of Medicine, Washington, DC, United States; National Center for Human Factors in Healthcare, MedStar Health, Washington, DC, United States.
| | - Emilie M Roth
- Roth Cognitive Engineering, Stanford, CA, United States
| | - Ann M Bisantz
- Department of Industrial and Systems Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States
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