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Castner N, Arsiwala-Scheppach L, Mertens S, Krois J, Thaqi E, Kasneci E, Wahl S, Schwendicke F. Expert gaze as a usability indicator of medical AI decision support systems: a preliminary study. NPJ Digit Med 2024; 7:199. [PMID: 39068241 PMCID: PMC11283514 DOI: 10.1038/s41746-024-01192-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 07/12/2024] [Indexed: 07/30/2024] Open
Abstract
Given the current state of medical artificial intelligence (AI) and perceptions towards it, collaborative systems are becoming the preferred choice for clinical workflows. This work aims to address expert interaction with medical AI support systems to gain insight towards how these systems can be better designed with the user in mind. As eye tracking metrics have been shown to be robust indicators of usability, we employ them for evaluating the usability and user interaction with medical AI support systems. We use expert gaze to assess experts' interaction with an AI software for caries detection in bitewing x-ray images. We compared standard viewing of bitewing images without AI support versus viewing where AI support could be freely toggled on and off. We found that experts turned the AI on for roughly 25% of the total inspection task, and generally turned it on halfway through the course of the inspection. Gaze behavior showed that when supported by AI, more attention was dedicated to user interface elements related to the AI support, with more frequent transitions from the image itself to these elements. When considering that expert visual strategy is already optimized for fast and effective image inspection, such interruptions in attention can lead to increased time needed for the overall assessment. Gaze analysis provided valuable insights into an AI's usability for medical image inspection. Further analyses of these tools and how to delineate metrical measures of usability should be developed.
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Affiliation(s)
- Nora Castner
- Carl Zeiss Vision International GmbH, Tübingen, Germany.
- University of Tübingen, Tübingen, Germany.
| | | | - Sarah Mertens
- Charité - Univesitätsmedizin, Oral Diagnostics, Digital Health and Services Research, Berlin, Germany
| | - Joachim Krois
- Charité - Univesitätsmedizin, Oral Diagnostics, Digital Health and Services Research, Berlin, Germany
| | - Enkeleda Thaqi
- Technical University of Munich, Human-Centered Technologies for Learning, Munich, Germany
| | - Enkelejda Kasneci
- Technical University of Munich, Human-Centered Technologies for Learning, Munich, Germany
| | - Siegfried Wahl
- Carl Zeiss Vision International GmbH, Tübingen, Germany
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Falk Schwendicke
- Ludwig Maximilian University, Operative, Preventative and Pediatric Dentistry and Periodontology, Munich, Germany
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2
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Ramirez-Tamayo C, Faruqui SHA, Martinez S, Brisco A, Czarnek N, Alaeddini A, Mock JR, Golob EJ, Clark KL. Incorporation of Eye Tracking and Gaze Feedback to Characterize and Improve Radiologist Search Patterns of Chest X-Rays: A Randomized Controlled Clinical Trial. J Am Coll Radiol 2024; 21:942-946. [PMID: 38369046 DOI: 10.1016/j.jacr.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/19/2024] [Accepted: 02/01/2024] [Indexed: 02/20/2024]
Affiliation(s)
| | | | - Stanford Martinez
- Department of Mechanical Engineering, The University of Texas at San Antonio, Texas
| | | | | | - Adel Alaeddini
- Department of Mechanical Engineering, The University of Texas at San Antonio, Texas.
| | - Jeffrey R Mock
- Department of Psychology, The University of Texas at San Antonio, Texas
| | - Edward J Golob
- Department of Psychology, The University of Texas at San Antonio, Texas
| | - Kal L Clark
- Department of Radiology, University of Texas Health Science Center at San Antonio, Texas
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3
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Ibragimov B, Mello-Thoms C. The Use of Machine Learning in Eye Tracking Studies in Medical Imaging: A Review. IEEE J Biomed Health Inform 2024; 28:3597-3612. [PMID: 38421842 PMCID: PMC11262011 DOI: 10.1109/jbhi.2024.3371893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
Machine learning (ML) has revolutionized medical image-based diagnostics. In this review, we cover a rapidly emerging field that can be potentially significantly impacted by ML - eye tracking in medical imaging. The review investigates the clinical, algorithmic, and hardware properties of the existing studies. In particular, it evaluates 1) the type of eye-tracking equipment used and how the equipment aligns with study aims; 2) the software required to record and process eye-tracking data, which often requires user interface development, and controller command and voice recording; 3) the ML methodology utilized depending on the anatomy of interest, gaze data representation, and target clinical application. The review concludes with a summary of recommendations for future studies, and confirms that the inclusion of gaze data broadens the ML applicability in Radiology from computer-aided diagnosis (CAD) to gaze-based image annotation, physicians' error detection, fatigue recognition, and other areas of potentially high research and clinical impact.
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Wu W. We know what attention is! Trends Cogn Sci 2024; 28:304-318. [PMID: 38103983 DOI: 10.1016/j.tics.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 12/19/2023]
Abstract
Attention is one of the most thoroughly investigated psychological phenomena, yet skepticism about attention is widespread: we do not know what it is, it is too many things, there is no such thing. The deficiencies highlighted are not about experimental work but the adequacy of the scientific theory of attention. Combining common scientific claims about attention into a single theory leads to internal inconsistency. This paper demonstrates that a specific functional conception of attention is incorporated into the tasks used in standard experimental paradigms. In accepting these paradigms as valid probes of attention, we commit to this common conception. The conception unifies work at multiple levels of analysis into a coherent scientific explanation of attention. Thus, we all know what attention is.
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Affiliation(s)
- Wayne Wu
- Italian Academy for Advanced Studies in America, Columbia University, New York, NY, USA; Department of Philosophy and Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
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5
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Kok EM, Niehorster DC, van der Gijp A, Rutgers DR, Auffermann WF, van der Schaaf M, Kester L, van Gog T. The effects of gaze-display feedback on medical students' self-monitoring and learning in radiology. ADVANCES IN HEALTH SCIENCES EDUCATION : THEORY AND PRACTICE 2024:10.1007/s10459-024-10322-6. [PMID: 38555550 DOI: 10.1007/s10459-024-10322-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 03/03/2024] [Indexed: 04/02/2024]
Abstract
Self-monitoring is essential for effectively regulating learning, but difficult in visual diagnostic tasks such as radiograph interpretation. Eye-tracking technology can visualize viewing behavior in gaze displays, thereby providing information about visual search and decision-making. We hypothesized that individually adaptive gaze-display feedback improves posttest performance and self-monitoring of medical students who learn to detect nodules in radiographs. We investigated the effects of: (1) Search displays, showing which part of the image was searched by the participant; and (2) Decision displays, showing which parts of the image received prolonged attention in 78 medical students. After a pretest and instruction, participants practiced identifying nodules in 16 cases under search-display, decision-display, or no feedback conditions (n = 26 per condition). A 10-case posttest, without feedback, was administered to assess learning outcomes. After each case, participants provided self-monitoring and confidence judgments. Afterward, participants reported on self-efficacy, perceived competence, feedback use, and perceived usefulness of the feedback. Bayesian analyses showed no benefits of gaze displays for post-test performance, monitoring accuracy (absolute difference between participants' estimated and their actual test performance), completeness of viewing behavior, self-efficacy, and perceived competence. Participants receiving search-displays reported greater feedback utilization than participants receiving decision-displays, and also found the feedback more useful when the gaze data displayed was precise and accurate. As the completeness of search was not related to posttest performance, search displays might not have been sufficiently informative to improve self-monitoring. Information from decision displays was rarely used to inform self-monitoring. Further research should address if and when gaze displays can support learning.
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Affiliation(s)
- Ellen M Kok
- Department of Education, Utrecht University, P.O. Box 80140, 3508 CS, Utrecht, The Netherlands.
| | - Diederick C Niehorster
- Lund University Humanities Lab, Lund University, Lund, Sweden
- Department of Psychology, Lund University, Lund, Sweden
| | - Anouk van der Gijp
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Dirk R Rutgers
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Marieke van der Schaaf
- Utrecht Center for Research and Development in Health Professions Education, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Liesbeth Kester
- Department of Education, Utrecht University, P.O. Box 80140, 3508 CS, Utrecht, The Netherlands
| | - Tamara van Gog
- Department of Education, Utrecht University, P.O. Box 80140, 3508 CS, Utrecht, The Netherlands
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6
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Neves J, Hsieh C, Nobre IB, Sousa SC, Ouyang C, Maciel A, Duchowski A, Jorge J, Moreira C. Shedding light on ai in radiology: A systematic review and taxonomy of eye gaze-driven interpretability in deep learning. Eur J Radiol 2024; 172:111341. [PMID: 38340426 DOI: 10.1016/j.ejrad.2024.111341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/04/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024]
Abstract
X-ray imaging plays a crucial role in diagnostic medicine. Yet, a significant portion of the global population lacks access to this essential technology due to a shortage of trained radiologists. Eye-tracking data and deep learning models can enhance X-ray analysis by mapping expert focus areas, guiding automated anomaly detection, optimizing workflow efficiency, and bolstering training methods for novice radiologists. However, the literature shows contradictory results regarding the usefulness of eye-tracking data in deep-learning architectures for abnormality detection. We argue that these discrepancies between studies in the literature are due to (a) the way eye-tracking data is (or is not) processed, (b) the types of deep learning architectures chosen, and (c) the type of application that these architectures will have. We conducted a systematic literature review using PRISMA to address these contradicting results. We analyzed 60 studies that incorporated eye-tracking data in a deep-learning approach for different application goals in radiology. We performed a comparative analysis to understand if eye gaze data contains feature maps that can be useful under a deep learning approach and whether they can promote more interpretable predictions. To the best of our knowledge, this is the first survey in the area that performs a thorough investigation of eye gaze data processing techniques and their impacts in different deep learning architectures for applications such as error detection, classification, object detection, expertise level analysis, fatigue estimation and human attention prediction in medical imaging data. Our analysis resulted in two main contributions: (1) taxonomy that first divides the literature by task, enabling us to analyze the value eye movement can bring for each case and build guidelines regarding architectures and gaze processing techniques adequate for each application, and (2) an overall analysis of how eye gaze data can promote explainability in radiology.
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Affiliation(s)
- José Neves
- Instituto Superior Técnico / INESC-ID, University of Lisbon, Portugal.
| | - Chihcheng Hsieh
- School of Information Systems, Queensland University of Technology, Australia.
| | | | | | - Chun Ouyang
- School of Information Systems, Queensland University of Technology, Australia.
| | - Anderson Maciel
- Instituto Superior Técnico / INESC-ID, University of Lisbon, Portugal.
| | | | - Joaquim Jorge
- Instituto Superior Técnico / INESC-ID, University of Lisbon, Portugal.
| | - Catarina Moreira
- Human Technology Institute, University of Technology Sydney, Australia.
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Specian Junior FC, Litchfield D, Sandars J, Cecilio-Fernandes D. Use of eye tracking in medical education. MEDICAL TEACHER 2024:1-8. [PMID: 38382474 DOI: 10.1080/0142159x.2024.2316863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 02/06/2024] [Indexed: 02/23/2024]
Abstract
Eye tracking has become increasingly applied in medical education research for studying the cognitive processes that occur during the performance of a task, such as image interpretation and surgical skills development. However, analysis and interpretation of the large amount of data obtained by eye tracking can be confusing. In this article, our intention is to clarify the analysis and interpretation of the data obtained from eye tracking. Understanding the relationship between eye tracking metrics (such as gaze, pupil and blink rate) and cognitive processes (such as visual attention, perception, memory and cognitive workload) is essential. The importance of calibration and how the limitations of eye tracking can be overcome is also highlighted.
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Affiliation(s)
| | | | - John Sandars
- Health Research Institute, Edge Hill University, Ormskirk, UK
| | - Dario Cecilio-Fernandes
- Department of Medical Psychology and Psychiatry, School of Medical Sciences, University of Campinas, Campinas, São Paulo, Brazil
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8
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Cioffi GM, Pinilla-Echeverri N, Sheth T, Sibbald MG. Does artificial intelligence enhance physician interpretation of optical coherence tomography: insights from eye tracking. Front Cardiovasc Med 2023; 10:1283338. [PMID: 38144364 PMCID: PMC10739524 DOI: 10.3389/fcvm.2023.1283338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/20/2023] [Indexed: 12/26/2023] Open
Abstract
Background and objectives The adoption of optical coherence tomography (OCT) in percutaneous coronary intervention (PCI) is limited by need for real-time image interpretation expertise. Artificial intelligence (AI)-assisted Ultreon™ 2.0 software could address this barrier. We used eye tracking to understand how these software changes impact viewing efficiency and accuracy. Methods Eighteen interventional cardiologists and fellows at McMaster University, Canada, were included in the study and categorized as experienced or inexperienced based on lifetime OCT use. They were tasked with reviewing OCT images from both Ultreon™ 2.0 and AptiVue™ software platforms while their eye movements were recorded. Key metrics, such as time to first fixation on the area of interest, total task time, dwell time (time spent on the area of interest as a proportion of total task time), and interpretation accuracy, were evaluated using a mixed multivariate model. Results Physicians exhibited improved viewing efficiency with Ultreon™ 2.0, characterized by reduced time to first fixation (Ultreon™ 0.9 s vs. AptiVue™ 1.6 s, p = 0.007), reduced total task time (Ultreon™ 10.2 s vs. AptiVue™ 12.6 s, p = 0.006), and increased dwell time in the area of interest (Ultreon™ 58% vs. AptiVue™ 41%, p < 0.001). These effects were similar for experienced and inexperienced physicians. Accuracy of OCT image interpretation was preserved in both groups, with experienced physicians outperforming inexperienced physicians. Discussion Our study demonstrated that AI-enabled Ultreon™ 2.0 software can streamline the image interpretation process and improve viewing efficiency for both inexperienced and experienced physicians. Enhanced viewing efficiency implies reduced cognitive load potentially reducing the barriers for OCT adoption in PCI decision-making.
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Affiliation(s)
| | | | | | - Matthew Gary Sibbald
- Division of Cardiology, Hamilton General Hospital, Hamilton Health Sciences, McMaster University, Hamilton, ON, Canada
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9
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Shafiei SB, Shadpour S, Mohler JL, Sasangohar F, Gutierrez C, Seilanian Toussi M, Shafqat A. Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms. J Robot Surg 2023; 17:2963-2971. [PMID: 37864129 PMCID: PMC10678814 DOI: 10.1007/s11701-023-01722-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 08/19/2023] [Indexed: 10/22/2023]
Abstract
The aim of this study was to develop machine learning classification models using electroencephalogram (EEG) and eye-gaze features to predict the level of surgical expertise in robot-assisted surgery (RAS). EEG and eye-gaze data were recorded from 11 participants who performed cystectomy, hysterectomy, and nephrectomy using the da Vinci robot. Skill level was evaluated by an expert RAS surgeon using the modified Global Evaluative Assessment of Robotic Skills (GEARS) tool, and data from three subtasks were extracted to classify skill levels using three classification models-multinomial logistic regression (MLR), random forest (RF), and gradient boosting (GB). The GB algorithm was used with a combination of EEG and eye-gaze data to classify skill levels, and differences between the models were tested using two-sample t tests. The GB model using EEG features showed the best performance for blunt dissection (83% accuracy), retraction (85% accuracy), and burn dissection (81% accuracy). The combination of EEG and eye-gaze features using the GB algorithm improved the accuracy of skill level classification to 88% for blunt dissection, 93% for retraction, and 86% for burn dissection. The implementation of objective skill classification models in clinical settings may enhance the RAS surgical training process by providing objective feedback about performance to surgeons and their teachers.
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Affiliation(s)
- Somayeh B Shafiei
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA.
| | - Saeed Shadpour
- Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - James L Mohler
- Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
| | - Farzan Sasangohar
- Mike and Sugar Barnes Faculty Fellow II, Wm Michael Barnes and Department of Industrial and Systems Engineering at Texas A&M University, College Station, TX, 77843, USA
| | - Camille Gutierrez
- Obstetrics and Gynecology Residency Program, Sisters of Charity Health System, Buffalo, NY, 14214, USA
| | - Mehdi Seilanian Toussi
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
| | - Ambreen Shafqat
- Intelligent Cancer Care Laboratory, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
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Liu CH, Chang CW, Hung J, Lin JJH, Sung PS, Lee LA, Hsiao CT, Chao YP, Huang ES, Wang SL. Brain computed tomography reading of stroke patients by resident doctors from different medical specialities: An eye-tracking study. J Clin Neurosci 2023; 117:173-180. [PMID: 37837935 DOI: 10.1016/j.jocn.2023.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 10/16/2023]
Abstract
BACKGROUND Using the eye-tracking technique, our work aimed to examine whether difference in clinical background may affect the training outcome of resident doctors' interpretation skills and reading behaviour related to brain computed tomography (CT). METHODS Twelve resident doctors in the neurology, radiology, and emergency departments were recruited. Each participant had to read CT images of the brain for two cases. We evaluated each participant's accuracy of lesion identification. We also used the eye-tracking technique to assess reading behaviour. We recorded dwell times, fixation counts, run counts, and first-run dwell times of target lesions to evaluate visual attention. Transition entropy was applied to assess the temporal relations and spatial dynamics of systematic image reading. RESULTS The eye-tracking results showed that the image reading sequence examined by transition entropy was comparable among resident doctors from different medical specialties (p = 0.82). However, the dwell time of the target lesions was shorter for the resident doctors from the neurology department (4828.63 ms, p = 0.01) than for those from the resident doctors from the radiology (6275.88 ms) and emergency (5305.00 ms) departments. The eye-tracking results in individual areas of interest only showed differences in the eye-tracking performance of the first-run dwell time (p = 0.05) in the anterior cerebral falx. DISCUSSION Our findings demonstrate that resident doctors from different medical specialties may achieve similar imaging reading patterns for brain CT. This may mitigate queries regarding the influence of different backgrounds on training outcomes.
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Affiliation(s)
- Chi-Hung Liu
- Department of Neurology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan; School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Division of Medical Education, Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Chun-Wei Chang
- Department of Neurology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan; School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - June Hung
- Department of Neurology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan; School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - John J H Lin
- Graduate Institute of Science Education, National Taiwan Normal University, Taipei City, Taiwan.
| | - Pi-Shan Sung
- Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Li-Ang Lee
- School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Otorhinolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Linkou Main Branch, Taoyuan, Taiwan; Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Cheng-Ting Hsiao
- School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Emergency Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan; Chang Gung Medical Education Research Centre, Taoyuan, Taiwan
| | - Yi-Ping Chao
- Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan; Department of Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan
| | - Elaine Shinwei Huang
- Department of Neurology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Shu-Ling Wang
- Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taiwan
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Akerman M, Choudhary S, Liebmann JM, Cioffi GA, Chen RWS, Thakoor KA. Extracting decision-making features from the unstructured eye movements of clinicians on glaucoma OCT reports and developing AI models to classify expertise. Front Med (Lausanne) 2023; 10:1251183. [PMID: 37841006 PMCID: PMC10571140 DOI: 10.3389/fmed.2023.1251183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 09/14/2023] [Indexed: 10/17/2023] Open
Abstract
This study aimed to investigate the eye movement patterns of ophthalmologists with varying expertise levels during the assessment of optical coherence tomography (OCT) reports for glaucoma detection. Objectives included evaluating eye gaze metrics and patterns as a function of ophthalmic education, deriving novel features from eye-tracking, and developing binary classification models for disease detection and expertise differentiation. Thirteen ophthalmology residents, fellows, and clinicians specializing in glaucoma participated in the study. Junior residents had less than 1 year of experience, while senior residents had 2-3 years of experience. The expert group consisted of fellows and faculty with over 3 to 30+ years of experience. Each participant was presented with a set of 20 Topcon OCT reports (10 healthy and 10 glaucomatous) and was asked to determine the presence or absence of glaucoma and rate their confidence of diagnosis. The eye movements of each participant were recorded as they diagnosed the reports using a Pupil Labs Core eye tracker. Expert ophthalmologists exhibited more refined and focused eye fixations, particularly on specific regions of the OCT reports, such as the retinal nerve fiber layer (RNFL) probability map and circumpapillary RNFL b-scan. The binary classification models developed using the derived features demonstrated high accuracy up to 94.0% in differentiating between expert and novice clinicians. The derived features and trained binary classification models hold promise for improving the accuracy of glaucoma detection and distinguishing between expert and novice ophthalmologists. These findings have implications for enhancing ophthalmic education and for the development of effective diagnostic tools.
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Affiliation(s)
- Michelle Akerman
- Department of Biomedical Engineering, Columbia University, New York, NY, United States
| | - Sanmati Choudhary
- Department of Computer Science, Columbia University, New York, NY, United States
| | - Jeffrey M. Liebmann
- Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY, United States
| | - George A. Cioffi
- Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY, United States
| | - Royce W. S. Chen
- Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY, United States
| | - Kaveri A. Thakoor
- Department of Biomedical Engineering, Columbia University, New York, NY, United States
- Department of Computer Science, Columbia University, New York, NY, United States
- Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Irving Medical Center, New York, NY, United States
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12
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Darici D, Reissner C, Missler M. Webcam-based eye-tracking to measure visual expertise of medical students during online histology training. GMS JOURNAL FOR MEDICAL EDUCATION 2023; 40:Doc60. [PMID: 37881524 PMCID: PMC10594038 DOI: 10.3205/zma001642] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 06/06/2023] [Accepted: 07/07/2023] [Indexed: 10/27/2023]
Abstract
Objectives Visual expertise is essential for image-based tasks that rely on visual cues, such as in radiology or histology. Studies suggest that eye movements are related to visual expertise and can be measured by near-infrared eye-tracking. With the popularity of device-embedded webcam eye-tracking technology, cost-effective use in educational contexts has recently become amenable. This study investigated the feasibility of such methodology in a curricular online-only histology course during the 2021 summer term. Methods At two timepoints (t1 and t2), third-semester medical students were asked to diagnose a series of histological slides while their eye movements were recorded. Students' eye metrics, performance and behavioral measures were analyzed using variance analyses and multiple regression models. Results First, webcam-eye tracking provided eye movement data with satisfactory quality (mean accuracy=115.7 px±31.1). Second, the eye movement metrics reflected the students' proficiency in finding relevant image sections (fixation count on relevant areas=6.96±1.56 vs. irrelevant areas=4.50±1.25). Third, students' eye movement metrics successfully predicted their performance (R2adj=0.39, p<0.001). Conclusion This study supports the use of webcam-eye-tracking expanding the range of educational tools available in the (digital) classroom. As the students' interest in using the webcam eye-tracking was high, possible areas of implementation will be discussed.
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Affiliation(s)
- Dogus Darici
- Westfälische-Wilhelms-University, Institute of Anatomy and Neurobiology, Münster, Germany
| | - Carsten Reissner
- Westfälische-Wilhelms-University, Institute of Anatomy and Neurobiology, Münster, Germany
| | - Markus Missler
- Westfälische-Wilhelms-University, Institute of Anatomy and Neurobiology, Münster, Germany
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13
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Lee M, Desy J, Tonelli AC, Walsh MH, Ma IWY. The association of attentional foci and image interpretation accuracy in novices interpreting lung ultrasound images: an eye-tracking study. Ultrasound J 2023; 15:36. [PMID: 37697149 PMCID: PMC10495286 DOI: 10.1186/s13089-023-00333-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/02/2023] [Indexed: 09/13/2023] Open
Abstract
It is unclear, where learners focus their attention when interpreting point-of-care ultrasound (POCUS) images. This study seeks to determine the relationship between attentional foci metrics with lung ultrasound (LUS) interpretation accuracy in novice medical learners. A convenience sample of 14 medical residents with minimal LUS training viewed 8 LUS cineloops, with their eye-tracking patterns recorded. Areas of interest (AOI) for each cineloop were mapped independently by two experts, and externally validated by a third expert. Primary outcome of interest was image interpretation accuracy, presented as a percentage. Eye tracking captured 10 of 14 participants (71%) who completed the study. Participants spent a mean total of 8 min 44 s ± standard deviation (SD) 3 min 8 s on the cineloops, with 1 min 14 s ± SD 34 s spent fixated in the AOI. Mean accuracy score was 54.0% ± SD 16.8%. In regression analyses, fixation duration within AOI was positively associated with accuracy [beta-coefficients 28.9 standardized error (SE) 6.42, P = 0.002). Total time spent viewing the videos was also significantly associated with accuracy (beta-coefficient 5.08, SE 0.59, P < 0.0001). For each additional minute spent fixating within the AOI, accuracy scores increased by 28.9%. For each additional minute spent viewing the video, accuracy scores increased only by 5.1%. Interpretation accuracy is strongly associated with time spent fixating within the AOI. Image interpretation training should consider targeting AOIs.
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Affiliation(s)
- Matthew Lee
- Division of General Internal Medicine, Department of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada
| | - Janeve Desy
- Division of General Internal Medicine, Department of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada
| | - Ana Claudia Tonelli
- UNISINOS University, Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil
| | - Michael H Walsh
- Division of General Internal Medicine, Department of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada
| | - Irene W Y Ma
- Division of General Internal Medicine, Department of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada.
- W21C, University of Calgary, Calgary, AB, Canada.
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14
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Arsiwala-Scheppach LT, Castner N, Rohrer C, Mertens S, Kasneci E, Cejudo Grano de Oro JE, Krois J, Schwendicke F. Gaze patterns of dentists while evaluating bitewing radiographs. J Dent 2023; 135:104585. [PMID: 37301462 DOI: 10.1016/j.jdent.2023.104585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 05/15/2023] [Accepted: 06/07/2023] [Indexed: 06/12/2023] Open
Abstract
OBJECTIVES Understanding dentists' gaze patterns on radiographs may allow to unravel sources of their limited accuracy and develop strategies to mitigate them. We conducted an eye tracking experiment to characterize dentists' scanpaths and thus their gaze patterns when assessing bitewing radiographs to detect primary proximal carious lesions. METHODS 22 dentists assessed a median of nine bitewing images each, resulting in 170 datasets after excluding data with poor quality of gaze recording. Fixation was defined as an area of attentional focus related to visual stimuli. We calculated time to first fixation, fixation count, average fixation duration, and fixation frequency. Analyses were performed for the entire image and stratified by (1) presence of carious lesions and/or restorations and (2) lesion depth (E1/2: outer/inner enamel; D1-3: outer-inner third of dentin). We also examined the transitional nature of the dentists' gaze. RESULTS Dentists had more fixations on teeth with lesions and/or restorations (median=138 [interquartile range=87, 204]) than teeth without them (32 [15, 66]), p<0.001. Notably, teeth with lesions had longer fixation durations (407 milliseconds [242, 591]) than those with restorations (289 milliseconds [216, 337]), p<0.001. Time to first fixation was longer for teeth with E1 lesions (17,128 milliseconds [8813, 21,540]) than lesions of other depths (p = 0.049). The highest number of fixations were on teeth with D2 lesions (43 [20, 51]) and lowest on teeth with E1 lesions (5 [1, 37]), p<0.001. Generally, a systematic tooth-by-tooth gaze pattern was observed. CONCLUSIONS As hypothesized, while visually inspecting bitewing radiographic images, dentists employed a heightened focus on certain image features/areas, relevant to the assigned task. Also, they generally examined the entire image in a systematic tooth-by-tooth pattern.
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Affiliation(s)
- Lubaina T Arsiwala-Scheppach
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany; ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, Switzerland.
| | - Nora Castner
- Department of Computer Science, University of Tuebingen, Tuebingen, Germany
| | - Csaba Rohrer
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany
| | - Sarah Mertens
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany
| | - Enkelejda Kasneci
- Department of Computer Science, Technical University of Munich, Germany
| | - Jose Eduardo Cejudo Grano de Oro
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany; ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, Switzerland
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany; ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, Switzerland
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15
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Borchers C, Eder TF, Richter J, Keutel C, Huettig F, Scheiter K. A time slice analysis of dentistry students' visual search strategies and pupil dilation during diagnosing radiographs. PLoS One 2023; 18:e0283376. [PMID: 37289785 PMCID: PMC10249848 DOI: 10.1371/journal.pone.0283376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 03/08/2023] [Indexed: 06/10/2023] Open
Abstract
Diagnosing orthopantomograms (OPTs: panoramic radiographs) is an essential skill dentistry students acquire during university training. While prior research described experts' visual search behavior in radiology as global-to-focal for chest radiographs and mammography, generalizability to a hybrid search task in OPTs (i.e., searching for multiple, diverse anomalies) remains unclear. Addressing this gap, this study investigated visual search of N = 107 dentistry students while they were diagnosing anomalies in OPTs. Following a global-to-focal expert model, we hypothesized that students would use many, short fixations representing global search in earlier stages, and few, long fixations representing focal search in later stages. Furthermore, pupil dilation and mean fixation duration served as cognitive load measures. We hypothesized that later stages would be characterized by elaboration and a reflective search strategy, leading to higher cognitive load being associated with higher diagnostic performance in late compared to earlier stages. In line with the first hypothesis, students' visual search comprised of a three-stage process that grew increasingly focal in terms of the number of fixations and anomalies fixated. Contrary to the second hypothesis, mean fixation duration during anomaly fixations was positively associated with diagnostic performance across all stages. As OPTs greatly varied in how difficult it was to identify the anomalies contained therein, OPTs with above-average difficulty were sampled for exploratory analysis. Pupil dilation predicted diagnostic performance for difficult OPTs, possibly capturing elaborative cognitive processes and cognitive load compared to mean fixation duration. A visual analysis of fine-grained time slices indicated large cognitive load differences towards the end of trials, showcasing a richness-resolution-trade-off in data sampling crucial for future studies using time-slicing of eye tracking data.
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Affiliation(s)
| | | | | | - Constanze Keutel
- Department of Oral- and Maxillofacial Radiology, Centre for Dentistry, Oral Medicine, and Maxillofacial Surgery at the University Hospital Tübingen, University of Tübingen, Tübingen, Germany
| | - Fabian Huettig
- Department of Prosthodontics, Centre for Dentistry, Oral Medicine, and Maxillofacial Surgery at the University Hospital Tübingen, University of Tübingen, Tübingen, Germany
| | - Katharina Scheiter
- Eberhard Karls University of Tübingen, Tübingen, Germany
- Leibniz-Institut für Wissensmedien, Tübingen, Germany
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16
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Pershin I, Mustafaev T, Ibragimova D, Ibragimov B. Changes in Radiologists' Gaze Patterns Against Lung X-rays with Different Abnormalities: a Randomized Experiment. J Digit Imaging 2023; 36:767-775. [PMID: 36622464 PMCID: PMC9838425 DOI: 10.1007/s10278-022-00760-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/23/2022] [Accepted: 12/15/2022] [Indexed: 01/10/2023] Open
Abstract
The workload of some radiologists increased dramatically in the last several, which resulted in a potentially reduced quality of diagnosis. It was demonstrated that diagnostic accuracy of radiologists significantly reduces at the end of work shifts. The study aims to investigate how radiologists cover chest X-rays with their gaze in the presence of different chest abnormalities and high workload. We designed a randomized experiment to quantitatively assess how radiologists' image reading patterns change with the radiological workload. Four radiologists read chest X-rays on a radiological workstation equipped with an eye-tracker. The lung fields on the X-rays were automatically segmented with U-Net neural network allowing to measure the lung coverage with radiologists' gaze. The images were randomly split so that each image was shown at a different time to a different radiologist. Regression models were fit to the gaze data to calculate the treads in lung coverage for individual radiologists and chest abnormalities. For the study, a database of 400 chest X-rays with reference diagnoses was assembled. The average lung coverage with gaze ranged from 55 to 65% per radiologist. For every 100 X-rays read, the lung coverage reduced from 1.3 to 7.6% for the different radiologists. The coverage reduction trends were consistent for all abnormalities ranging from 3.4% per 100 X-rays for cardiomegaly to 4.1% per 100 X-rays for atelectasis. The more image radiologists read, the smaller part of the lung fields they cover with the gaze. This pattern is very stable for all abnormality types and is not affected by the exact order the abnormalities are viewed by radiologists. The proposed randomized experiment captured and quantified consistent changes in X-ray reading for different lung abnormalities that occur due to high workload.
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Affiliation(s)
- Ilya Pershin
- Innopolis University, Republic of Tatarstan, Innopolis, Russia
| | - Tamerlan Mustafaev
- Innopolis University, Republic of Tatarstan, Innopolis, Russia
- Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
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17
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Shafiei SB, Shadpour S, Mohler JL, Attwood K, Liu Q, Gutierrez C, Toussi MS. Developing surgical skill level classification model using visual metrics and a gradient boosting algorithm. ANNALS OF SURGERY OPEN 2023; 4:e292. [PMID: 37305561 PMCID: PMC10249659 DOI: 10.1097/as9.0000000000000292] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 04/24/2023] [Indexed: 06/13/2023] Open
Abstract
Objective Assessment of surgical skills is crucial for improving training standards and ensuring the quality of primary care. This study aimed to develop a gradient boosting classification model (GBM) to classify surgical expertise into inexperienced, competent, and experienced levels in robot-assisted surgery (RAS) using visual metrics. Methods Eye gaze data were recorded from 11 participants performing four subtasks; blunt dissection, retraction, cold dissection, and hot dissection using live pigs and the da Vinci robot. Eye gaze data were used to extract the visual metrics. One expert RAS surgeon evaluated each participant's performance and expertise level using the modified Global Evaluative Assessment of Robotic Skills (GEARS) assessment tool. The extracted visual metrics were used to classify surgical skill levels and to evaluate individual GEARS metrics. Analysis of Variance (ANOVA) was used to test the differences for each feature across skill levels. Results Classification accuracies for blunt dissection, retraction, cold dissection, and burn dissection were 95%, 96%, 96%, and 96%, respectively. The time to complete only the retraction was significantly different among the 3 skill levels (p-value = 0.04). Performance was significantly different for 3 categories of surgical skill level for all subtasks (p-values<0.01). The extracted visual metrics were strongly associated with GEARS metrics (R2>0.7 for GEARS metrics evaluation models). Conclusions Machine learning (ML) algorithms trained by visual metrics of RAS surgeons can classify surgical skill levels and evaluate GEARS measures. The time to complete a surgical subtask may not be considered a stand-alone factor for skill level assessment.
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Affiliation(s)
- Somayeh B. Shafiei
- From the Department of Urology, Roswell Park Comprehensive Cancer Center in Buffalo, NY
| | - Saeed Shadpour
- Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
| | - James L. Mohler
- From the Department of Urology, Roswell Park Comprehensive Cancer Center in Buffalo, NY
| | - Kristopher Attwood
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY
| | - Qian Liu
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY
| | - Camille Gutierrez
- Obstetrics and Gynecology Residency Program, Sisters of Charity Health System, Buffalo, NY
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18
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Byrne SA, Reynolds APF, Biliotti C, Bargagli-Stoffi FJ, Polonio L, Riccaboni M. Predicting choice behaviour in economic games using gaze data encoded as scanpath images. Sci Rep 2023; 13:4722. [PMID: 36959330 PMCID: PMC10036613 DOI: 10.1038/s41598-023-31536-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 03/14/2023] [Indexed: 03/25/2023] Open
Abstract
Eye movement data has been extensively utilized by researchers interested in studying decision-making within the strategic setting of economic games. In this paper, we demonstrate that both deep learning and support vector machine classification methods are able to accurately identify participants' decision strategies before they commit to action while playing games. Our approach focuses on creating scanpath images that best capture the dynamics of a participant's gaze behaviour in a way that is meaningful for predictions to the machine learning models. Our results demonstrate a higher classification accuracy by 18% points compared to a baseline logistic regression model, which is traditionally used to analyse gaze data recorded during economic games. In a broader context, we aim to illustrate the potential for eye-tracking data to create information asymmetries in strategic environments in favour of those who collect and process the data. These information asymmetries could become especially relevant as eye-tracking is expected to become more widespread in user applications, with the seemingly imminent mass adoption of virtual reality systems and the development of devices with the ability to record eye movement outside of a laboratory setting.
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Affiliation(s)
- Sean Anthony Byrne
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy
| | | | - Carolina Biliotti
- AXES Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy
| | | | - Luca Polonio
- Department of Economics, Management and Statistics, University of Milano - Bicocca, Milan, Italy.
| | - Massimo Riccaboni
- AXES Research Unit, IMT School for Advanced Studies Lucca, Lucca, Italy
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19
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Tjon JK, Jarodzka H, Linskens IH, Van der Knoop BJ, De Vries JIP. Eye-tracking visual patterns of sonographers with and without fetal motor assessment expertise. Early Hum Dev 2023; 177-178:105722. [PMID: 36774729 DOI: 10.1016/j.earlhumdev.2023.105722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/23/2023] [Accepted: 01/31/2023] [Indexed: 02/08/2023]
Abstract
OBJECTIVES Fetal motor assessment (FMA) in addition to structural anomaly scan enhances prenatal detection of arthrogryposis multiplex congenita (AMC). In the Amsterdam UMC, sonographers are trained to perform FMA. We examined the effect of motor assessment training by comparing sonographers with (SMA) and without this training (S) on their qualitative motor assessment in fetuses with normal (FNM) and abnormal motility (FAM) and their visual processing by eye-tracking. METHODS The study was performed from 2019 to 2020. Five SMA and five S observed five FNM and five FAM videos. Qualitative FMA consisted of six aspects of the general movement and the overall conclusion normal or abnormal. The visual processing aspects examined through eye-tracking were fixation duration, number of revisits per region of interest (ROI) and scanpaths of saccades between fixation points. RESULTS Quality assessment by SMA revealed more correct aspects in FNM than in FAM but overall conclusions were equally correct (92-96 %). S scored aspects of FNM better than in FAM, but overall conclusion correct only in half of FNM and three quarters of FAM. Eye-tracking of SMA and S showed fixation duration and revisits with similar distributions per ROIs for FNM and FAM, but SMA perform more trunk revisits in FNM. Scanpaths had smaller circumference, less outliers and more consistency in SMA than S. CONCLUSION This modest population of qualified sonographers showed that additional FMA training improved qualitative motor assessment. Eye-tracking revealed differences in visual processing and stimulates continuous education for professionals active in the detection of these rare diseases.
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Affiliation(s)
- J K Tjon
- Department of Obstetrics and Gynaecology, Amsterdam Movement Sciences, Amsterdam University Medical Centre, Location VUmc, the Netherlands.
| | - H Jarodzka
- Department of Online Learning and Instruction, Faculty of Educational Sciences, Open Universiteit, the Netherlands
| | - I H Linskens
- Department of Obstetrics and Gynaecology, Amsterdam Movement Sciences, Amsterdam University Medical Centre, Location VUmc, the Netherlands
| | - B J Van der Knoop
- Department of Obstetrics and Gynaecology, Amsterdam Movement Sciences, Amsterdam University Medical Centre, Location VUmc, the Netherlands
| | - J I P De Vries
- Department of Obstetrics and Gynaecology, Amsterdam Movement Sciences, Amsterdam University Medical Centre, Location VUmc, the Netherlands
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20
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Vach K, Schlueter N, Ganss C, Vach W. Understanding the Influence of Patient Factors on Accuracy and Decision-Making in a Diagnostic Accuracy Study with Multiple Raters-A Case Study from Dentistry. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:1781. [PMID: 36767148 PMCID: PMC9914814 DOI: 10.3390/ijerph20031781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/13/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
In diagnostic accuracy studies, the test of interest is typically applied only once in each patient. This paper illustrates some possibilities that arise when diagnoses are carried out by a sufficiently large number of multiple raters. In a dental study, sixty-one examiners were asked to diagnose 49 tooth areas with different grades of tissue loss (minor, moderate, and advanced) to decide whether dentine was exposed (positive status) or not (negative status). The true status was determined by histology (reference). For each tooth, the rate of correct decisions reflecting the difficulty to diagnose this tooth and the positive rate reflecting the perception of the tooth by the raters was computed. Meta-analytical techniques were used to assess the inter-tooth variation and the influence of tooth-specific factors on difficulty or perception, respectively. A huge variation in diagnostic difficulty and perception could be observed. Advanced tissue loss made diagnoses more difficult. The background colour and tissue loss were associated with perception and may hint to cues used by the raters. The use of multiple raters in a diagnostic accuracy study allows detailed investigations which make it possible to obtain further insights into the decision-making process of the raters.
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Affiliation(s)
- Kirstin Vach
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Stefan-Meier-Str. 26, D-79104 Freiburg, Germany
- Center for Dental Medicine, Department of Operative Dentistry and Periodontology, Faculty of Medicine and Medical Center, University of Freiburg, Hugstetter Straße 55, D-79106 Freiburg, Germany
| | - Nadine Schlueter
- Department of Conservative Dentistry, Periodontology and Preventive Dentistry, Hannover Medical School, Carl-Neuberg-Str. 1, D-30625 Hannover, Germany
| | - Carolina Ganss
- Department for Operative Dentistry, Endodontics, and Pediatric Dentistry, Section Cariology of Ageing, Philipps-University Marburg, Georg-Voigt-Str. 3, D-35039 Marburg, Germany
| | - Werner Vach
- Basel Academy for Quality and Research in Medicine, Steinenring 6, CH-4051 Basel, Switzerland
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21
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Active visual search in naturalistic environments reflects individual differences in classic visual search performance. Sci Rep 2023; 13:631. [PMID: 36635491 PMCID: PMC9837148 DOI: 10.1038/s41598-023-27896-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/10/2023] [Indexed: 01/13/2023] Open
Abstract
Visual search is a ubiquitous activity in real-world environments. Yet, traditionally, visual search is investigated in tightly controlled paradigms, where head-restricted participants locate a minimalistic target in a cluttered array that is presented on a computer screen. Do traditional visual search tasks predict performance in naturalistic settings, where participants actively explore complex, real-world scenes? Here, we leverage advances in virtual reality technology to test the degree to which classic and naturalistic search are limited by a common factor, set size, and the degree to which individual differences in classic search behavior predict naturalistic search behavior in a large sample of individuals (N = 75). In a naturalistic search task, participants looked for an object within their environment via a combination of head-turns and eye-movements using a head-mounted display. Then, in a classic search task, participants searched for a target within a simple array of colored letters using only eye-movements. In each task, we found that participants' search performance was impacted by increases in set size-the number of items in the visual display. Critically, we observed that participants' efficiency in classic search tasks-the degree to which set size slowed performance-indeed predicted efficiency in real-world scenes. These results demonstrate that classic, computer-based visual search tasks are excellent models of active, real-world search behavior.
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22
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Liu CH, Hung J, Chang CW, Lin JJH, Huang ES, Wang SL, Lee LA, Hsiao CT, Sung PS, Chao YP, Chang YJ. Oral presentation assessment and image reading behaviour on brain computed tomography reading in novice clinical learners: an eye-tracking study. BMC MEDICAL EDUCATION 2022; 22:738. [PMID: 36284299 PMCID: PMC9597969 DOI: 10.1186/s12909-022-03795-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 10/06/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND To study whether oral presentation (OP) assessment could reflect the novice learners' interpretation skills and reading behaviour on brain computed tomography (CT) reading. METHODS Eighty fifth-year medical students were recruited, received a 2-hour interactive workshop on how to read brain CT, and were assigned to read two brain CT images before and after instruction. We evaluated their image reading behaviour in terms of overall OP post-test rating, the lesion identification, and competency in systematic image reading after instruction. Students' reading behaviour in searching for the target lesions were recorded by the eye-tracking technique and were used to validate the accuracy of lesion reports. Statistical analyses, including lag sequential analysis (LSA), linear mixed models, and transition entropy (TE) were conducted to reveal temporal relations and spatial complexity of systematic image reading from the eye movement perspective. RESULTS The overall OP ratings [pre-test vs. post-test: 0 vs. 1 in case 1, 0 vs. 1 in case 2, p < 0.001] improved after instruction. Both the scores of systematic OP ratings [0 vs.1 in both cases, p < 0.001] and eye-tracking studies (Case 1: 3.42 ± 0.62 and 3.67 ± 0.37 in TE, p = 0.001; Case 2: 3.42 ± 0.76 and 3.75 ± 0.37 in TE, p = 0.002) showed that the image reading behaviour changed before and after instruction. The results of linear mixed models suggested a significant interaction between instruction and area of interests for case 1 (p < 0.001) and case 2 (p = 0.004). Visual attention to the target lesions in the case 1 assessed by dwell time were 506.50 ± 509.06 and 374.38 ± 464.68 milliseconds before and after instruction (p = 0.02). However, the dwell times in the case 2, the fixation counts and the frequencies of accurate lesion diagnoses in both cases did not change after instruction. CONCLUSION Our results showed OP performance may change concurrently with the medical students' reading behaviour on brain CT after a structured instruction.
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Affiliation(s)
- Chi-Hung Liu
- Department of Neurology, Linkou Medical Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Division of Medical Education, Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - June Hung
- Department of Neurology, Linkou Medical Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chun-Wei Chang
- Department of Neurology, Linkou Medical Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - John J H Lin
- Graduate Institute of Science Education, National Taiwan Normal University, No. 88, Ting-Jou Rd., Sec. 4, Taipei City, Taiwan.
| | - Elaine Shinwei Huang
- Department of Neurology, Linkou Medical Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Shu-Ling Wang
- Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Li-Ang Lee
- School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Otorhinolaryngology-Head and Neck Surgery, Linkou Main Branch, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Cheng-Ting Hsiao
- School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Emergency Medicine, Chang Gung Memorial Hospital, Chiayi, Taiwan
- Chang Gung Medical Education Research Centre, Taoyuan, Taiwan
| | - Pi-Shan Sung
- Department of Neurology, College of Medicine, National Cheng Kung University Hospital, National Cheng Kung University, Tainan, Taiwan
| | - Yi-Ping Chao
- Department of Neurology, Linkou Medical Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan
- Department of Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan
| | - Yeu-Jhy Chang
- Department of Neurology, Linkou Medical Center, Chang Gung Memorial Hospital, Taoyuan, Taiwan
- School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Division of Medical Education, Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Chang Gung Medical Education Research Centre, Taoyuan, Taiwan
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23
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Homfray B, Attwood A, Channon SB. Anatomy in Practice: How Do Equine and Production Animal Veterinarians Apply Anatomy in Primary Care Settings? JOURNAL OF VETERINARY MEDICAL EDUCATION 2022; 50:e20220074. [PMID: 36198110 DOI: 10.3138/jvme-2022-0074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
To successfully prepare veterinary undergraduates for the workplace, it is critical that anatomy educators consider the context in which developing knowledge and skills will be applied. This study aimed to establish how farm animal and equine general practitioners use anatomy and related skills within their daily work. Qualitative ethnographic data in the form of observations and semi-structured interviews were collected from 12 veterinarians working in equine or farm animal first-opinion practice. Data underwent thematic analysis using a grounded theory approach. The five themes identified were relevant to both equine and farm animal veterinarians and represented the breadth and complexity of anatomy, its importance for professional and practical competence, as well as the requirement for continuous learning. The centrality and broad and multifaceted nature of anatomy was found to challenge equine and farm animal veterinarians, highlighting that essential anatomy knowledge and related skills are vital for their professional and practical competence. This aligns with the previously described experiences of companion animal clinicians. In equine practice, the complexity of anatomical knowledge required was particularly high, especially in relation to diagnostic imaging and assessing normal variation. This resulted in greater importance being placed on formal and informal professional development opportunities. For farm animal clinicians, anatomy application in the context of necropsy and euthanasia was particularly noted. Our findings allow anatomy educators to design appropriate and effective learning opportunities to ensure that veterinary graduates are equipped with the skills, knowledge, and resources required to succeed in first-opinion veterinary practice.
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Li S, Jiang Y, Sun C, Guo K, Wang X. An Investigation on the Influence of Operation Experience on Virtual Hazard Perception Using Wearable Eye Tracking Technology. SENSORS (BASEL, SWITZERLAND) 2022; 22:5115. [PMID: 35890794 PMCID: PMC9324423 DOI: 10.3390/s22145115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 07/05/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
Poor electrical hazard recognition is a widespread issue in the production industry. Hazard perception has impacts workers' hazard recognition, causing them to experience unanticipated hazard exposure and suffer catastrophic injuries. To improve the factors of affecting hazard perception, the current study examined hazard recognition as an everyday visual search task. A comparative test was carried out combining the advantages and disadvantages of the two test methods. It was confirmed that the virtual image test data can replace the real image test data and demonstrate superior flexible settings performance, so the virtual image test method is used. A hazard perception test method based on wearable eye tracking technology was proposed to analyze the eye-tracking data (i.e., fixation, count, search duration, mean fixation duration, eye tracking, and hazard recognition performance feedback) were compared between experts in the field of electrical safety: skilled workers with at least five years of work experience and workers who had been on the job for less than a year. It was found that experts had a better hazard recognition accuracy and missed detection rate than other workers. Experts' hazards research track was more concised and paid less attention time. This advantage is most obvious in complex risk environments. The findings also suggest that workers who have different working years was not obvious visual search patterns other than the search duration. As can be seen the work experience is not an absolute factor in improving hazard perception. The present research will be useful to understand the influence of working years on hazard perception and provide a theoretical basis for corresponding training.
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Affiliation(s)
| | | | - Chao Sun
- Correspondence: (C.S.); (K.G.); Tel.: +86-138-3604-7482 (C.S.); +86-185-4519-3196 (K.G.)
| | - Kangkang Guo
- Correspondence: (C.S.); (K.G.); Tel.: +86-138-3604-7482 (C.S.); +86-185-4519-3196 (K.G.)
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Grady JN, Cox PH, Nag S, Mitroff SR. Conscientiousness protects visual search performance from the impact of fatigue. Cogn Res Princ Implic 2022; 7:56. [PMID: 35763131 PMCID: PMC9240146 DOI: 10.1186/s41235-022-00410-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 06/15/2022] [Indexed: 12/01/2022] Open
Abstract
Visual search—looking for targets among distractors—underlies many critical professions (e.g., radiology, aviation security) that demand optimal performance. As such, it is important to identify, understand, and ameliorate negative factors such as fatigue—mental and/or physical tiredness that leads to diminished function. One way to reduce the detrimental effects is to minimize fatigue itself (e.g., scheduled breaks, adjusting pre-shift behaviors), but this is not always possible or sufficient. The current study explored whether some individuals are less susceptible to the impact of fatigue than others; specifically, if conscientiousness, the ability to control impulses and plan, moderates fatigue’s impact. Participants (N = 374) self-reported their energy (i.e., the inverse of fatigue) and conscientiousness levels and completed a search task. Self-report measures were gathered prior to completing the search task as part of a large set of surveys so that participants could not anticipate any particular research question. Preregistered linear mixed-effect analyses revealed main effects of energy level (lower state energy related to lower accuracy) and conscientiousness (more trait conscientiousness related to higher accuracy), and, critically, a significant interaction between energy level and conscientiousness. A follow-up analysis, that was designed to illustrate the nature of the primary result, divided participants into above- vs. below-median conscientiousness groups and revealed a significant negative relationship between energy level and accuracy for the below median, but not above-median, group. The results raise intriguing operational possibilities for visual search professions, with the most direct implication being the incorporation of conscientiousness measures to personnel selection processes.
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Affiliation(s)
- Justin N Grady
- Department of Psychological and Brain Sciences, The George Washington University, 2125 G St NW, Washington, DC, 20052, USA
| | - Patrick H Cox
- Department of Psychological and Brain Sciences, The George Washington University, 2125 G St NW, Washington, DC, 20052, USA.,Intelligence Community Postdoctoral Research Fellowship Program, Department of Psychological and Brain Sciences, The George Washington University, Washington, DC, 20052, USA
| | - Samoni Nag
- Department of Psychological and Brain Sciences, The George Washington University, 2125 G St NW, Washington, DC, 20052, USA
| | - Stephen R Mitroff
- Department of Psychological and Brain Sciences, The George Washington University, 2125 G St NW, Washington, DC, 20052, USA.
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Pershin I, Kholiavchenko M, Maksudov B, Mustafaev T, Ibragimova D, Ibragimov B. Artificial Intelligence for the Analysis of Workload-Related Changes in Radiologists' Gaze Patterns. IEEE J Biomed Health Inform 2022; 26:4541-4550. [PMID: 35704540 DOI: 10.1109/jbhi.2022.3183299] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Around 60-80% of radiological errors are attributed to overlooked abnormalities, the rate of which increases at the end of work shifts. In this study, we run an experiment to investigate if artificial intelligence (AI) can assist in detecting radiologists' gaze patterns that correlate with fatigue. A retrospective database of lung X-ray images with the reference diagnoses was used. The X-ray images were acquired from 400 subjects with a mean age of 49 ± 17, and 61% men. Four practicing radiologists read these images while their eye movements were recorded. The radiologists passed a series of concentration tests at prearranged breaks of the experiment. A U-Net neural network was adapted to annotate lung anatomy on X-rays and calculate coverage and information gain features from the radiologists' eye movements over lung fields. The lung coverage, information gain, and eye tracker-based features were compared with the cumulative work done (CDW) label for each radiologist. The gaze-traveled distance, X-ray coverage, and lung coverage statistically significantly (p < 0.01) deteriorated with cumulative work done (CWD) for three out of four radiologists. The reading time and information gain over lungs statistically significantly deteriorated for all four radiologists. We discovered a novel AI-based metric blending reading time, speed, and organ coverage, which can be used to predict changes in the fatigue-related image reading patterns.
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Teng C, Lee LH, Lander J, Drukker L, Papageorghiou AT, Noble AJ. Skill Characterisation of Sonographer Gaze Patterns during Second Trimester Clinical Fetal Ultrasounds using Time Curves. PROCEEDINGS. EYE TRACKING RESEARCH & APPLICATIONS SYMPOSIUM 2022; 2022:30. [PMID: 36812105 PMCID: PMC7614191 DOI: 10.1145/3517031.3529637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
We present a method for skill characterisation of sonographer gaze patterns while performing routine second trimester fetal anatomy ultrasound scans. The position and scale of fetal anatomical planes during each scan differ because of fetal position, movements and sonographer skill. A standardised reference is required to compare recorded eye-tracking data for skill characterisation. We propose using an affine transformer network to localise the anatomy circumference in video frames, for normalisation of eye-tracking data. We use an event-based data visualisation, time curves, to characterise sonographer scanning patterns. We chose brain and heart anatomical planes because they vary in levels of gaze complexity. Our results show that when sonographers search for the same anatomical plane, even though the landmarks visited are similar, their time curves display different visual patterns. Brain planes also, on average, have more events or landmarks occurring than the heart, which highlights anatomy-specific differences in searching approaches.
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Affiliation(s)
- Clare Teng
- Institute of Biomedical Engineering, University of Oxford Oxford, United Kingdom
| | - Lok Hin Lee
- Institute of Biomedical Engineering, University of Oxford Oxford, United Kingdom
| | - Jayne Lander
- Nuffield Department of Women’s and Reproductive Health, University of Oxford Oxford, United Kingdom
| | - Lior Drukker
- Nuffield Department of Women’s and Reproductive Health, University of Oxford Oxford, United Kingdom Women’s Ultrasound, Department of Obstetrics and Gynecology, Beilinson Medical Center, Sackler Faculty of Medicine, Tel-Aviv University Tel Aviv, Israel
| | - Aris T. Papageorghiou
- Nuffield Department of Women’s and Reproductive Health, University of Oxford Oxford, United Kingdom
| | - Alison J. Noble
- Institute of Biomedical Engineering, University of Oxford Oxford, United Kingdom
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Sahraian S, Yousem D, Beheshtian E, Jalilianhasanpour R, Morales RE, Krupinski EA, Zhan H. Improving Radiology Trainees' Perception Using Where's Waldo? Acad Radiol 2022; 29 Suppl 5:S11-S17. [PMID: 33172815 DOI: 10.1016/j.acra.2020.10.023] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 10/23/2020] [Accepted: 10/27/2020] [Indexed: 11/26/2022]
Abstract
RATIONALE AND OBJECTIVES Perception is an essential skill leading to expertise in diagnostic radiology. We determined if practicing "Where's Waldo?" images improves accuracy and speed with which first and second year radiology residents detect abnormalities on chest radiographs (CXRs). MATERIALS AND METHODS Residents at three institutions were pretested using 50 CXRs, identifying locations of potential abnormalities. They were then split into trained (examining 7 "Where's Waldo?" images over three weeks) and control groups (no "Where's Waldo?"). They were then re-tested on the 50 CXRs. At one site, visual search parameters were acquired. Data were analyzed with repeated measures ANOVAs. RESULTS There was no significant difference in performance for trained vs control (F = 0.622, p = 0.436), with both improving significantly on post-test (F = 4.72, p = 0.037). Session time decreased significantly for both groups from pre to post-test (F = 81.47, p < 0.0001) and the decrease was significantly more (F = 31.59, p < 0.0001) for the trained group than the control group as well as for PGY with PGY3 having a larger average decrease in session time than PGY2. Eye-tracking data also showed significant increases in per image search efficiency with training. CONCLUSION Practicing "Where's Waldo?" or similar nonradiology search tasks may facilitate the acquisition of radiology image search but not detection skills, impacting reading efficiency more than detection accuracy.
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Ernst D, Wolfe JM. How fixation durations are affected by search difficulty manipulations. VISUAL COGNITION 2022. [DOI: 10.1080/13506285.2022.2063465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Daniel Ernst
- Brigham & Women’s Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Bielefeld University, Bielefeld, Germany
| | - Jeremy M. Wolfe
- Brigham & Women’s Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
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Gong H, Hsieh SS, Holmes D, Cook D, Inoue A, Bartlett D, Baffour F, Takahashi H, Leng S, Yu L, McCollough CH, Fletcher JG. An interactive eye-tracking system for measuring radiologists' visual fixations in volumetric CT images: Implementation and initial eye-tracking accuracy validation. Med Phys 2021; 48:6710-6723. [PMID: 34534365 PMCID: PMC8595866 DOI: 10.1002/mp.15219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 08/28/2021] [Accepted: 08/30/2021] [Indexed: 01/17/2023] Open
Abstract
PURPOSE Eye-tracking approaches have been used to understand the visual search process in radiology. However, previous eye-tracking work in computer tomography (CT) has been limited largely to single cross-sectional images or video playback of the reconstructed volume, which do not accurately reflect radiologists' visual search activities and their interactivity with three-dimensional image data at a computer workstation (e.g., scroll, pan, and zoom) for visual evaluation of diagnostic imaging targets. We have developed a platform that integrates eye-tracking hardware with in-house-developed reader workstation software to allow monitoring of the visual search process and reader-image interactions in clinically relevant reader tasks. The purpose of this work is to validate the spatial accuracy of eye-tracking data using this platform for different eye-tracking data acquisition modes. METHODS An eye-tracker was integrated with a previously developed workstation designed for reader performance studies. The integrated system captured real-time eye movement and workstation events at 1000 Hz sampling frequency. The eye-tracker was operated either in head-stabilized mode or in free-movement mode. In head-stabilized mode, the reader positioned their head on a manufacturer-provided chinrest. In free-movement mode, a biofeedback tool emitted an audio cue when the head position was outside the data collection range (general biofeedback) or outside a narrower range of positions near the calibration position (strict biofeedback). Four radiologists and one resident were invited to participate in three studies to determine eye-tracking spatial accuracy under three constraint conditions: head-stabilized mode (i.e., with use of a chin rest), free movement with general biofeedback, and free movement with strict biofeedback. Study 1 evaluated the impact of head stabilization versus general or strict biofeedback using a cross-hair target prior to the integration of the eye-tracker with the image viewing workstation. In Study 2, after integration of the eye-tracker and reader workstation, readers were asked to fixate on targets that were randomly distributed within a volumetric digital phantom. In Study 3, readers used the integrated system to scroll through volumetric patient CT angiographic images while fixating on the centerline of designated blood vessels (from the left coronary artery to dorsalis pedis artery). Spatial accuracy was quantified as the offset between the center of the intended target and the detected fixation using units of image pixels and the degree of visual angle. RESULTS The three head position constraint conditions yielded comparable accuracy in the studies using digital phantoms. For Study 1 involving the digital crosshairs, the median ± the standard deviation of offset values among readers were 15.2 ± 7.0 image pixels with the chinrest, 14.2 ± 3.6 image pixels with strict biofeedback, and 19.1 ± 6.5 image pixels with general biofeedback. For Study 2 using the random dot phantom, the median ± standard deviation offset values were 16.7 ± 28.8 pixels with use of a chinrest, 16.5 ± 24.6 pixels using strict biofeedback, and 18.0 ± 22.4 pixels using general biofeedback, which translated to a visual angle of about 0.8° for all three conditions. We found no obvious association between eye-tracking accuracy and target size or view time. In Study 3 viewing patient images, use of the chinrest and strict biofeedback demonstrated comparable accuracy, while the use of general biofeedback demonstrated a slightly worse accuracy. The median ± standard deviation of offset values were 14.8 ± 11.4 pixels with use of a chinrest, 21.0 ± 16.2 pixels using strict biofeedback, and 29.7 ± 20.9 image pixels using general biofeedback. These corresponded to visual angles ranging from 0.7° to 1.3°. CONCLUSIONS An integrated eye-tracker system to assess reader eye movement and interactive viewing in relation to imaging targets demonstrated reasonable spatial accuracy for assessment of visual fixation. The head-free movement condition with audio biofeedback performed similarly to head-stabilized mode.
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Affiliation(s)
- Hao Gong
- Department of Radiology, Mayo Clinic, Rochester, MN 55901
| | - Scott S. Hsieh
- Department of Radiology, Mayo Clinic, Rochester, MN 55901
| | - David Holmes
- Department of Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN 55901
| | - David Cook
- Department of Internal Medicine, Mayo Clinic, Rochester, MN 55901
| | - Akitoshi Inoue
- Department of Radiology, Mayo Clinic, Rochester, MN 55901
| | - David Bartlett
- Department of Radiology, Mayo Clinic, Rochester, MN 55901
| | | | | | - Shuai Leng
- Department of Radiology, Mayo Clinic, Rochester, MN 55901
| | - Lifeng Yu
- Department of Radiology, Mayo Clinic, Rochester, MN 55901
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Smith AD, De Lillo C. Sources of variation in search and foraging: A theoretical perspective. Q J Exp Psychol (Hove) 2021; 75:197-231. [PMID: 34609229 DOI: 10.1177/17470218211050314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Search-the problem of exploring a space of alternatives to identify target goals-is a fundamental behaviour for many species. Although its foundation lies in foraging, most studies of human search behaviour have been directed towards understanding the attentional mechanisms that underlie the efficient visual exploration of two-dimensional (2D) scenes. With this review, we aim to characterise how search behaviour can be explained across a wide range of contexts, environments, spatial scales, and populations, both typical and atypical. We first consider the generality of search processes across psychological domains. We then review studies of interspecies differences in search. Finally, we explore in detail the individual and contextual variables that affect visual search and related behaviours in established experimental psychology paradigms. Despite the heterogeneity of the findings discussed, we identify that variations in control processes, along with the ability to regulate behaviour as a function of the structure of search space and the sampling processes adopted, to be central to explanations of variations in search behaviour. We propose a tentative theoretical model aimed at integrating these notions and close by exploring questions that remain unaddressed.
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Affiliation(s)
| | - Carlo De Lillo
- Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
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Aust J, Mitrovic A, Pons D. Assessment of the Effect of Cleanliness on the Visual Inspection of Aircraft Engine Blades: An Eye Tracking Study. SENSORS (BASEL, SWITZERLAND) 2021; 21:6135. [PMID: 34577343 PMCID: PMC8473167 DOI: 10.3390/s21186135] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/03/2021] [Accepted: 09/07/2021] [Indexed: 01/20/2023]
Abstract
Background-The visual inspection of aircraft parts such as engine blades is crucial to ensure safe aircraft operation. There is a need to understand the reliability of such inspections and the factors that affect the results. In this study, the factor 'cleanliness' was analysed among other factors. Method-Fifty industry practitioners of three expertise levels inspected 24 images of parts with a variety of defects in clean and dirty conditions, resulting in a total of N = 1200 observations. The data were analysed statistically to evaluate the relationships between cleanliness and inspection performance. Eye tracking was applied to understand the search strategies of different levels of expertise for various part conditions. Results-The results show an inspection accuracy of 86.8% and 66.8% for clean and dirty blades, respectively. The statistical analysis showed that cleanliness and defect type influenced the inspection accuracy, while expertise was surprisingly not a significant factor. In contrast, inspection time was affected by expertise along with other factors, including cleanliness, defect type and visual acuity. Eye tracking revealed that inspectors (experts) apply a more structured and systematic search with less fixations and revisits compared to other groups. Conclusions-Cleaning prior to inspection leads to better results. Eye tracking revealed that inspectors used an underlying search strategy characterised by edge detection and differentiation between surface deposits and other types of damage, which contributed to better performance.
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Affiliation(s)
- Jonas Aust
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand;
| | - Antonija Mitrovic
- Department of Computer Science and Software Engineering, University of Canterbury, Christchurch 8041, New Zealand;
| | - Dirk Pons
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand;
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Kliewer MA, Bagley AR. How to Read an Abdominal CT: Insights from the Visual and Cognitive Sciences Translated for Clinical Practice. Curr Probl Diagn Radiol 2021; 51:639-647. [PMID: 34583872 DOI: 10.1067/j.cpradiol.2021.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 07/01/2021] [Accepted: 07/18/2021] [Indexed: 11/22/2022]
Abstract
When first learning abdominal CT studies, residents are often given little concrete, practical direction. There is, however, a large literature from the visual and cognitive sciences that can provide guidance towards search strategies that maximize efficiency and comprehensiveness. This literature has not penetrated radiology teaching to any great extent. In this article, we will examine the current pedagogy (and why that falls short), why untutored search fails, where misses occur in abdomen/pelvis CT, why these misses occur where they do, how expert radiologists search 3d image stacks, and how novices might expedite the acquisition of expertise.
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Affiliation(s)
- Mark A Kliewer
- Department of Radiology, University of Wisconsin - Madison, Madison, Wisconsin
| | - Anjuli R Bagley
- Radiology, The University of Colorado - Denver, Department of Radiology, Aurora, CO, USA, University of Colorado Hospital (UCH), Aurora, Colorado
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LoGiudice AB, Sherbino J, Norman G, Monteiro S, Sibbald M. Intuitive and deliberative approaches for diagnosing 'well' versus 'unwell': evidence from eye tracking, and potential implications for training. ADVANCES IN HEALTH SCIENCES EDUCATION : THEORY AND PRACTICE 2021; 26:811-825. [PMID: 33423154 DOI: 10.1007/s10459-020-10023-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 12/21/2020] [Indexed: 06/12/2023]
Abstract
Rapidly assessing how ill a patient is based on their immediate presentation-colloquially termed 'eyeballing' in practice-serves a vital role in acute care settings. Yet surprisingly little is known about how this diagnostic skill is learned or how it should be taught. Some authors have pointed to a dual-process model, suggesting that assessments of illness severity are driven by two distinct types of processing: an intuitive, fast, pattern recognition-like process (Type 1) that depends on many prior patient encounters and outcomes being stored in memory; and a deliberate, slow, analytic process (Type 2) characterized by additional data gathering, data scrutiny, or recollection of rules. But prior studies have supported a dual-process model for the assessment of illness severity only insofar as experienced clinicians chiefly displayed what was presumed to be Type 1 processing. Here we further explored a dual-process model by examining whether less experienced clinicians displayed both types of processing when assessing illness severity across a series of cases. Consistent with the model, a dissociation between Type 1 and Type 2 processing was observed through resident reports of deliberation, response times, and three eye tracking metrics associated with diagnostic expertise. We conclude by discussing potential implications for the training of this enigmatic diagnostic skill.
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Affiliation(s)
- Andrew B LoGiudice
- MacPherson Institute for Leadership, Innovation, and Excellence in Teaching, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4L8, Canada.
- McMaster Faculty of Health Sciences Program in Education Research, Innovation and Theory (MERIT), Hamilton, Canada.
| | - Jonathan Sherbino
- Department of Medicine, McMaster University, Hamilton, Canada
- McMaster Faculty of Health Sciences Program in Education Research, Innovation and Theory (MERIT), Hamilton, Canada
| | - Geoffrey Norman
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada
- McMaster Faculty of Health Sciences Program in Education Research, Innovation and Theory (MERIT), Hamilton, Canada
| | - Sandra Monteiro
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada
- McMaster Faculty of Health Sciences Program in Education Research, Innovation and Theory (MERIT), Hamilton, Canada
| | - Matthew Sibbald
- Department of Medicine, McMaster University, Hamilton, Canada
- McMaster Faculty of Health Sciences Program in Education Research, Innovation and Theory (MERIT), Hamilton, Canada
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An algorithmic approach to determine expertise development using object-related gaze pattern sequences. Behav Res Methods 2021; 54:493-507. [PMID: 34258709 PMCID: PMC8863757 DOI: 10.3758/s13428-021-01652-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/14/2021] [Indexed: 12/20/2022]
Abstract
Eye tracking (ET) technology is increasingly utilized to quantify visual behavior in the study of the development of domain-specific expertise. However, the identification and measurement of distinct gaze patterns using traditional ET metrics has been challenging, and the insights gained shown to be inconclusive about the nature of expert gaze behavior. In this article, we introduce an algorithmic approach for the extraction of object-related gaze sequences and determine task-related expertise by investigating the development of gaze sequence patterns during a multi-trial study of a simplified airplane assembly task. We demonstrate the algorithm in a study where novice (n = 28) and expert (n = 2) eye movements were recorded in successive trials (n = 8), allowing us to verify whether similar patterns develop with increasing expertise. In the proposed approach, AOI sequences were transformed to string representation and processed using the k-mer method, a well-known method from the field of computational biology. Our results for expertise development suggest that basic tendencies are visible in traditional ET metrics, such as the fixation duration, but are much more evident for k-mers of k > 2. With increased on-task experience, the appearance of expert k-mer patterns in novice gaze sequences was shown to increase significantly (p < 0.001). The results illustrate that the multi-trial k-mer approach is suitable for revealing specific cognitive processes and can quantify learning progress using gaze patterns that include both spatial and temporal information, which could provide a valuable tool for novice training and expert assessment.
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Application of Eye Tracking Technology in Aviation, Maritime, and Construction Industries: A Systematic Review. SENSORS 2021; 21:s21134289. [PMID: 34201734 PMCID: PMC8271947 DOI: 10.3390/s21134289] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/15/2021] [Accepted: 06/17/2021] [Indexed: 11/25/2022]
Abstract
Most accidents in the aviation, maritime, and construction industries are caused by human error, which can be traced back to impaired mental performance and attention failure. In 1596, Du Laurens, a French anatomist and medical scientist, said that the eyes are the windows of the mind. Eye tracking research dates back almost 150 years and it has been widely used in different fields for several purposes. Overall, eye tracking technologies provide the means to capture in real time a variety of eye movements that reflect different human cognitive, emotional, and physiological states, which can be used to gain a wider understanding of the human mind in different scenarios. This systematic literature review explored the different applications of eye tracking research in three high-risk industries, namely aviation, maritime, and construction. The results of this research uncovered the demographic distribution and applications of eye tracking research, as well as the different technologies that have been integrated to study the visual, cognitive, and attentional aspects of human mental performance. Moreover, different research gaps and potential future research directions were highlighted in relation to the usage of additional technologies to support, validate, and enhance eye tracking research to better understand human mental performance.
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Avoiding potential pitfalls in visual search and eye-movement experiments: A tutorial review. Atten Percept Psychophys 2021; 83:2753-2783. [PMID: 34089167 PMCID: PMC8460493 DOI: 10.3758/s13414-021-02326-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/03/2021] [Indexed: 12/15/2022]
Abstract
Examining eye-movement behavior during visual search is an increasingly popular approach for gaining insights into the moment-to-moment processing that takes place when we look for targets in our environment. In this tutorial review, we describe a set of pitfalls and considerations that are important for researchers – both experienced and new to the field – when engaging in eye-movement and visual search experiments. We walk the reader through the research cycle of a visual search and eye-movement experiment, from choosing the right predictions, through to data collection, reporting of methodology, analytic approaches, the different dependent variables to analyze, and drawing conclusions from patterns of results. Overall, our hope is that this review can serve as a guide, a talking point, a reflection on the practices and potential problems with the current literature on this topic, and ultimately a first step towards standardizing research practices in the field.
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Van De Luecht M, Reed WM. The cognitive and perceptual processes that affect observer performance in lung cancer detection: a scoping review. J Med Radiat Sci 2021; 68:175-185. [PMID: 33556995 PMCID: PMC8168065 DOI: 10.1002/jmrs.456] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 12/11/2020] [Indexed: 12/19/2022] Open
Abstract
INTRODUCTION Early detection of malignant pulmonary nodules through screening has been shown to reduce lung cancer-related mortality by 20%. However, perceptual and cognitive factors that affect nodule detection are poorly understood. This review examines the cognitive and visual processes of various observers, with a particular focus on radiologists, during lung nodule detection. METHODS Four databases (Medline, Embase, Scopus and PubMed) were searched to extract studies on eye-tracking in pulmonary nodule detection. Studies were included if they used eye-tracking to assess the search and detection of lung nodules in computed tomography or 2D radiographic imaging. Data were charted according to identified themes and synthesised using a thematic narrative approach. RESULTS The literature search yielded 25 articles and five themes were discovered: 1 - functional visual field and satisfaction of search, 2 - expert search patterns, 3 - error classification through dwell time, 4 - the impact of the viewing environment and 5 - the effect of prevalence expectation on search. Functional visual field reduced to 2.7° in 3D imaging compared to 5° in 2D radiographs. Although greater visual coverage improved nodule detection, incomplete search was not responsible for missed nodules. Most radiological errors during lung nodule detection were decision-making errors (30%-45%). Dwell times associated with false-positive (FP) decisions informed feedback systems to improve diagnosis. Interruptions did not influence diagnostic performance; however, it increased viewing time by 8% and produced a 23.1% search continuation accuracy. Comparative scanning was found to increase the detection of low contrast nodules. Prevalence expectation did not directly affect diagnostic accuracy; however, decision-making time increased by 2.32 seconds with high prevalence expectations. CONCLUSION Visual and cognitive factors influence pulmonary nodule detection. Insights gained from eye-tracking can inform advancements in lung screening. Further exploration of eye-tracking in lung screening, particularly with low-dose computed tomography (LDCT), will benefit the future of lung cancer screening.
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Affiliation(s)
- Monica‐Rose Van De Luecht
- Discipline of Medical Imaging ScienceFaculty of Medicine and HealthSydney School of Health SciencesThe University of SydneySydneyNSWAustralia
| | - Warren Michael Reed
- Medical Imaging Optimisation and Perception Group (MIOPeG)Discipline of Medical Imaging ScienceSydney School of Health SciencesFaculty of Medicine and HealthThe University of SydneySydneyNSWAustralia
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Wang FS, Wolf J, Farshad M, Meboldt M, Lohmeyer Q. Object-Gaze Distance: Quantifying Near- Peripheral Gaze Behavior in Real-World Applications. J Eye Mov Res 2021; 14. [PMID: 34122747 PMCID: PMC8189527 DOI: 10.16910/jemr.14.1.5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Eye tracking (ET) has shown to reveal the wearer’s cognitive processes using the measurement
of the central point of foveal vision. However, traditional ET evaluation methods have
not been able to take into account the wearers’ use of the peripheral field of vision. We
propose an algorithmic enhancement to a state-of-the-art ET analysis method, the Object-
Gaze Distance (OGD), which additionally allows the quantification of near-peripheral gaze
behavior in complex real-world environments. The algorithm uses machine learning for area
of interest (AOI) detection and computes the minimal 2D Euclidean pixel distance to the
gaze point, creating a continuous gaze-based time-series. Based on an evaluation of two
AOIs in a real surgical procedure, the results show that a considerable increase of interpretable
fixation data from 23.8 % to 78.3 % of AOI screw and from 4.5 % to 67.2 % of AOI
screwdriver was achieved, when incorporating the near-peripheral field of vision. Additionally,
the evaluation of a multi-OGD time series representation has shown the potential to
reveal novel gaze patterns, which may provide a more accurate depiction of human gaze
behavior in multi-object environments.
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van Montfort D, Kok E, Vincken K, van der Schaaf M, van der Gijp A, Ravesloot C, Rutgers D. Expertise development in volumetric image interpretation of radiology residents: what do longitudinal scroll data reveal? ADVANCES IN HEALTH SCIENCES EDUCATION : THEORY AND PRACTICE 2021; 26:437-466. [PMID: 33030627 PMCID: PMC8041671 DOI: 10.1007/s10459-020-09995-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 09/28/2020] [Indexed: 06/11/2023]
Abstract
The current study used theories on expertise development (the holistic model of image perception and the information reduction hypothesis) as a starting point to identify and explore potentially relevant process measures to monitor and evaluate expertise development in radiology residency training. It is the first to examine expertise development in volumetric image interpretation (i.e., CT scans) within radiology residents using scroll data collected longitudinally over five years of residency training. Consistent with the holistic model of image perception, the percentage of time spent on full runs, i.e. scrolling through more than 50% of the CT-scan slices (global search), decreased within residents over residency training years. Furthermore, the percentage of time spent on question-relevant areas in the CT scans increased within residents over residency training years, consistent with the information reduction hypothesis. Second, we examined if scroll patterns can predict diagnostic accuracy. The percentage of time spent on full runs and the percentage of time spent on question-relevant areas did not predict diagnostic accuracy. Thus, although scroll patterns over training years are consistent with visual expertise theories, they could not be used as predictors of diagnostic accuracy in the current study. Therefore, the relation between scroll patterns and performance needs to be further examined, before process measures can be used to monitor and evaluate expertise development in radiology residency training.
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Affiliation(s)
- Dorien van Montfort
- Department of Education, Utrecht University, Heidelberglaan 1, 3584CS, Utrecht, The Netherlands
| | - Ellen Kok
- Department of Education, Utrecht University, Heidelberglaan 1, 3584CS, Utrecht, The Netherlands.
| | - Koen Vincken
- Image Sciences Institute, Imaging Dept, University Medical Center, Utrecht, The Netherlands
| | - Marieke van der Schaaf
- Department of Education, Utrecht University, Heidelberglaan 1, 3584CS, Utrecht, The Netherlands
- Center for Research and Development of Education, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Anouk van der Gijp
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cécile Ravesloot
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Dirk Rutgers
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
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Williams L, Carrigan A, Auffermann W, Mills M, Rich A, Elmore J, Drew T. The invisible breast cancer: Experience does not protect against inattentional blindness to clinically relevant findings in radiology. Psychon Bull Rev 2021; 28:503-511. [PMID: 33140228 PMCID: PMC8068567 DOI: 10.3758/s13423-020-01826-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/04/2020] [Indexed: 12/11/2022]
Abstract
Retrospectively obvious events are frequently missed when attention is engaged in another task-a phenomenon known as inattentional blindness. Although the task characteristics that predict inattentional blindness rates are relatively well understood, the observer characteristics that predict inattentional blindness rates are largely unknown. Previously, expert radiologists showed a surprising rate of inattentional blindness to a gorilla photoshopped into a CT scan during lung-cancer screening. However, inattentional blindness rates were higher for a group of naïve observers performing the same task, suggesting that perceptual expertise may provide protection against inattentional blindness. Here, we tested whether expertise in radiology predicts inattentional blindness rates for unexpected abnormalities that were clinically relevant. Fifty radiologists evaluated CT scans for lung cancer. The final case contained a large (9.1 cm) breast mass and lymphadenopathy. When their attention was focused on searching for lung nodules, 66% of radiologists did not detect breast cancer and 30% did not detect lymphadenopathy. In contrast, only 3% and 10% of radiologists (N = 30), respectively, missed these abnormalities in a follow-up study when searching for a broader range of abnormalities. Neither experience, primary task performance, nor search behavior predicted which radiologists missed the unexpected abnormalities. These findings suggest perceptual expertise does not protect against inattentional blindness, even for unexpected stimuli that are within the domain of expertise.
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Affiliation(s)
| | - Ann Carrigan
- Psychology, Macquarie University, Macquarie Park, Australia
| | - William Auffermann
- School of Medicine, Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA
| | - Megan Mills
- School of Medicine, Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA
| | - Anina Rich
- Cognitive Science, Macquarie University, Macquarie Park, Australia
| | - Joann Elmore
- David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Trafton Drew
- Psychology, University of Utah, Salt Lake City, UT, USA
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Karargyris A, Kashyap S, Lourentzou I, Wu JT, Sharma A, Tong M, Abedin S, Beymer D, Mukherjee V, Krupinski EA, Moradi M. Creation and validation of a chest X-ray dataset with eye-tracking and report dictation for AI development. Sci Data 2021; 8:92. [PMID: 33767191 PMCID: PMC7994908 DOI: 10.1038/s41597-021-00863-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 02/09/2021] [Indexed: 12/15/2022] Open
Abstract
We developed a rich dataset of Chest X-Ray (CXR) images to assist investigators in artificial intelligence. The data were collected using an eye-tracking system while a radiologist reviewed and reported on 1,083 CXR images. The dataset contains the following aligned data: CXR image, transcribed radiology report text, radiologist's dictation audio and eye gaze coordinates data. We hope this dataset can contribute to various areas of research particularly towards explainable and multimodal deep learning/machine learning methods. Furthermore, investigators in disease classification and localization, automated radiology report generation, and human-machine interaction can benefit from these data. We report deep learning experiments that utilize the attention maps produced by the eye gaze dataset to show the potential utility of this dataset.
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Affiliation(s)
| | | | - Ismini Lourentzou
- IBM Research, Almaden Research Center, San Jose, CA, 95120, USA
- Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Joy T Wu
- IBM Research, Almaden Research Center, San Jose, CA, 95120, USA
| | - Arjun Sharma
- IBM Research, Almaden Research Center, San Jose, CA, 95120, USA
| | - Matthew Tong
- IBM Research, Almaden Research Center, San Jose, CA, 95120, USA
| | - Shafiq Abedin
- IBM Research, Almaden Research Center, San Jose, CA, 95120, USA
| | - David Beymer
- IBM Research, Almaden Research Center, San Jose, CA, 95120, USA
| | | | - Elizabeth A Krupinski
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, 30322, USA
| | - Mehdi Moradi
- IBM Research, Almaden Research Center, San Jose, CA, 95120, USA.
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Eder TF, Richter J, Scheiter K, Huettig F, Keutel C. Comparing radiographs with signaling improves anomaly detection of dental students: An eye‐tracking study. APPLIED COGNITIVE PSYCHOLOGY 2021. [DOI: 10.1002/acp.3819] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Thésése F. Eder
- non‐university research institute Leibniz‐Institut für Wissensmedien Tübingen Germany
| | - Juliane Richter
- non‐university research institute Leibniz‐Institut für Wissensmedien Tübingen Germany
| | - Katharina Scheiter
- non‐university research institute Leibniz‐Institut für Wissensmedien Tübingen Germany
- University of Tübingen Tübingen Germany
| | - Fabian Huettig
- Department of Prosthodontics, Centre for Dentistry, Oral Medicine, and Maxillofacial Surgery, University Hospital Tübingen University of Tübingen Tübingen Germany
| | - Constanze Keutel
- Department of Oral‐ and Maxillofacial Radiology, Centre for Dentistry, Oral Medicine, and Maxillofacial Surgery, University Hospital Tübingen University of Tübingen Tübingen Germany
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Robson SG, Tangen JM, Searston RA. The effect of expertise, target usefulness and image structure on visual search. Cogn Res Princ Implic 2021; 6:16. [PMID: 33709197 PMCID: PMC7977019 DOI: 10.1186/s41235-021-00282-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 02/19/2021] [Indexed: 11/18/2022] Open
Abstract
Experts outperform novices on many cognitive and perceptual tasks. Extensive training has tuned experts to the most relevant information in their specific domain, allowing them to make decisions quickly and accurately. We compared a group of fingerprint examiners to a group of novices on their ability to search for information in fingerprints across two experiments-one where participants searched for target features within a single fingerprint and another where they searched for points of difference between two fingerprints. In both experiments, we also varied how useful the target feature was and whether participants searched for these targets in a typical fingerprint or one that had been scrambled. Experts more efficiently located targets when searching for them in intact but not scrambled fingerprints. In Experiment 1, we also found that experts more efficiently located target features classified as more useful compared to novices, but this expert-novice difference was not present when the target feature was classified as less useful. The usefulness of the target may therefore have influenced the search strategies that participants used, and the visual search advantages that experts display appear to depend on their vast experience with visual regularity in fingerprints. These results align with a domain-specific account of expertise and suggest that perceptual training ought to involve learning to attend to task-critical features.
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Affiliation(s)
- Samuel G Robson
- School of Psychology, The University of Queensland, St Lucia, 4072, QLD, Australia.
| | - Jason M Tangen
- School of Psychology, The University of Queensland, St Lucia, 4072, QLD, Australia
| | - Rachel A Searston
- School of Psychology, The University of Adelaide, Adelaide, 5005, SA, Australia
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Fernández-Miranda PM, Bellón PS, Del Barrio AP, Iglesias LL, García PS, Aguilar-Gómez F, González DR, Vega JA. Developing a Training Web Application for Improving the COVID-19 Diagnostic Accuracy on Chest X-ray. J Digit Imaging 2021; 34:242-256. [PMID: 33686526 PMCID: PMC7939450 DOI: 10.1007/s10278-021-00424-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 11/06/2020] [Accepted: 01/11/2021] [Indexed: 12/24/2022] Open
Abstract
In December 2019, a new coronavirus known as 2019-nCoV emerged in Wuhan, China. The virus has spread globally and the infection was declared pandemic in March 2020. Although most cases of coronavirus disease 2019 (COVID-19) are mild, some of them rapidly develop acute respiratory distress syndrome. In the clinical management, chest X-rays (CXR) are essential, but the evaluation of COVID-19 CXR could be a challenge. In this context, we developed COVID-19 TRAINING, a free Web application for training on the evaluation of COVID-19 CXR. The application included 196 CXR belonging to three categories: non-pathological, pathological compatible with COVID-19, and pathological non-compatible with COVID-19. On the training screen, images were shown to the users and they chose a diagnosis among those three possibilities. At any time, users could finish the training session and be evaluated through the estimation of their diagnostic accuracy values: sensitivity, specificity, predictive values, and global accuracy. Images were hand-labeled by four thoracic radiologists. Average values for sensitivity, specificity, and global accuracy were .72, .64, and .68. Users who achieved better sensitivity registered less specificity (p < .0001) and those with higher specificity decreased their sensitivity (p < .0001). Users who sent more answers achieved better accuracy (p = .0002). The application COVID-19 TRAINING provides a revolutionary tool to learn the necessary skills to evaluate COVID-19 on CXR. Diagnosis training applications could provide a new original manner of evaluation for medical professionals based on their diagnostic accuracy values, and an efficient method to collect valuable data for research purposes.
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Affiliation(s)
- P Menéndez Fernández-Miranda
- Departamento de Radiología, Hospital Universitario "Marqués de Valdecilla", Santander, Spain. .,Departamento Morfología y Biología Celular, Universidad de Oviedo, Oviedo, Spain.
| | - P Sanz Bellón
- Departamento de Radiología, Hospital Universitario "Marqués de Valdecilla", Santander, Spain.,Departamento Morfología y Biología Celular, Universidad de Oviedo, Oviedo, Spain
| | - A Pérez Del Barrio
- Departamento de Radiología, Hospital Universitario "Marqués de Valdecilla", Santander, Spain.,Departamento Morfología y Biología Celular, Universidad de Oviedo, Oviedo, Spain
| | - L Lloret Iglesias
- Grupo de Computación Avanzada y e-Ciencia, Instituto de Física de Cantabria, (IFCA), Consejo Superior de Investigaciones Científicas (CSIC), Santander, Spain
| | | | - F Aguilar-Gómez
- Grupo de Computación Avanzada y e-Ciencia, Instituto de Física de Cantabria, (IFCA), Consejo Superior de Investigaciones Científicas (CSIC), Santander, Spain
| | - D Rodríguez González
- Grupo de Computación Avanzada y e-Ciencia, Instituto de Física de Cantabria, (IFCA), Consejo Superior de Investigaciones Científicas (CSIC), Santander, Spain
| | - J A Vega
- Departamento de Morfología y Biología Celular, Universidad de Oviedo, Oviedo, Spain. .,Facultad de Ciencias de La Salud, Universidad Autónoma de Chile, Santiago, Chile.
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Comparing the Visual Perception According to the Performance Using the Eye-Tracking Technology in High-Fidelity Simulation Settings. Behav Sci (Basel) 2021; 11:bs11030031. [PMID: 33807673 PMCID: PMC7998119 DOI: 10.3390/bs11030031] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 02/26/2021] [Accepted: 03/01/2021] [Indexed: 12/22/2022] Open
Abstract
Introduction: We used eye-tracking technology to explore the visual perception of clinicians during a high-fidelity simulation scenario. We hypothesized that physicians who were able to successfully manage a critical situation would have a different visual focus compared to those who failed. Methods: A convenience sample of 18 first-year emergency medicine residents were enrolled voluntarily to participate in a high-fidelity scenario involving a patient in shock with a 3rd degree atrioventricular block. Their performance was rated as pass or fail and depended on the proper use of the pacing unit. Participants were wearing pre-calibrated eye-tracking glasses throughout the 9-min scenario and infrared (IR) markers installed in the simulator were used to define various Areas of Interest (AOI). Total View Duration (TVD) and Time to First Fixation (TFF) by the participants were recorded for each AOI and the results were used to produce heat maps. Results: Twelve residents succeeded while six failed the scenario. The TVD for the AOI containing the pacing unit was significantly shorter (median [quartile]) for those who succeeded compared to the ones who failed (42 [31–52] sec vs. 70 [61–90] sec, p = 0.0097). The TFF for the AOI containing the ECG and vital signs monitor was also shorter for the participants who succeeded than for those who failed (22 [6–28] sec vs. 30 [27–77] sec, p = 0.0182). Discussion: There seemed to be a connection between the gaze pattern of residents in a high-fidelity bradycardia simulation and their performance. The participants who succeeded looked at the monitor earlier (diagnosis). They also spent less time fixating the pacing unit, using it promptly to address the bradycardia. This study suggests that eye-tracking technology could be used to explore how visual perception, a key information-gathering element, is tied to decision-making and clinical performance.
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Eder TF, Richter J, Scheiter K, Keutel C, Castner N, Kasneci E, Huettig F. How to support dental students in reading radiographs: effects of a gaze-based compare-and-contrast intervention. ADVANCES IN HEALTH SCIENCES EDUCATION : THEORY AND PRACTICE 2021; 26:159-181. [PMID: 32488458 PMCID: PMC8238744 DOI: 10.1007/s10459-020-09975-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 05/20/2020] [Indexed: 06/10/2023]
Abstract
In dental medicine, interpreting radiographs (i.e., orthopantomograms, OPTs) is an error-prone process, even in experts. Effective intervention methods are therefore needed to support students in improving their image reading skills for OPTs. To this end, we developed a compare-and-contrast intervention, which aimed at supporting students in achieving full coverage when visually inspecting OPTs and, consequently, obtaining a better diagnostic performance. The comparison entailed a static eye movement visualization (heat map) on an OPT showing full gaze coverage from a peer-model (other student) and another heat map showing a student's own gaze behavior. The intervention group (N = 38) compared five such heat map combinations, whereas the control group (N = 23) diagnosed five OPTs. Prior to the experimental variation (pre-test) and after it (post-test), students in both conditions searched for anomalies in OPTs while their gaze was recorded. Results showed that students in the intervention group covered more areas of the OPTs and looked less often and for a shorter amount of time at anomalies after the intervention. Furthermore, they fixated on low-prevalence anomalies earlier and high-prevalence anomalies later during the inspection. However, the students in the intervention group did not show any meaningful improvement in detection rate and made more false positive errors compared to the control group. Thus, the intervention guided visual attention but did not improve diagnostic performance substantially. Exploratory analyses indicated that further interventions should teach knowledge about anomalies rather than focusing on full coverage of radiographs.
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Affiliation(s)
- Thérése F Eder
- Leibniz-Institut für Wissensmedien, Schleichstraße 6, 72076, Tübingen, Germany.
| | - Juliane Richter
- Leibniz-Institut für Wissensmedien, Schleichstraße 6, 72076, Tübingen, Germany
| | - Katharina Scheiter
- Leibniz-Institut für Wissensmedien, Schleichstraße 6, 72076, Tübingen, Germany
- University of Tübingen, Tübingen, Germany
| | - Constanze Keutel
- Department of Oral- and Maxillofacial Radiology, Centre for Dentistry, Oral Medicine, and Maxillofacial Surgery, University Hospital Tübingen, University of Tübingen, Tübingen, Germany
| | - Nora Castner
- Perception Engineering, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Enkelejda Kasneci
- Perception Engineering, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Fabian Huettig
- Department of Prosthodontics, Centre for Dentistry, Oral Medicine, and Maxillofacial Surgery, University Hospital Tübingen, University of Tübingen, Tübingen, Germany
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Durugbo CM. Eye tracking for work-related visual search: a cognitive task analysis. ERGONOMICS 2021; 64:225-240. [PMID: 32914697 DOI: 10.1080/00140139.2020.1822547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 09/04/2020] [Indexed: 06/11/2023]
Abstract
Cognitive Task Analysis (CTA) is an important methodology in ergonomics for studying workplaces and work patterns. Using eye tracking as a CTA methodology, this article explores visual search patterns in complex work environments and situations. It presents a simulated crime scene case study that applies eye tracking-based experiments in foraging and sense-making loops to elicit and represent knowledge on expert versus novice search patterns for complex work. The case probes the visual search task of preliminarily evaluating and documenting potential crime scene evidence. The experimental protocol relies on the ASL Mobile Eye and the analyses of experimental data include preliminary inspections of live-viewing data on eye-movements, precedence matrices detailing scan paths, and gaze charts that illustrate participants' attention based on fixation counts and durations. In line with the CTA methodology, the article uses concept maps to represent knowledge derived from different phases of the study. The article also discusses the research implications and methodologically reflects on the case study. Practitioner summary: This study offers valuable insights for work design. The use of eye tracking as a CTA methodology offers potentials for translating visual search tasks into defined visual search concepts for complex work environments and situations. The ability to model visual attention is valuable for work designs that improve complex work performance, reduce work stress, and promote work satisfaction.
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Affiliation(s)
- Christopher M Durugbo
- Department of Innovation and Technology Management, Arabian Gulf University, Manama, Bahrain
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Tallon M, Greenlee MW, Wagner E, Rakoczy K, Frick U. How Do Art Skills Influence Visual Search? - Eye Movements Analyzed With Hidden Markov Models. Front Psychol 2021; 12:594248. [PMID: 33584470 PMCID: PMC7875865 DOI: 10.3389/fpsyg.2021.594248] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 01/07/2021] [Indexed: 11/13/2022] Open
Abstract
The results of two experiments are analyzed to find out how artistic expertise influences visual search. Experiment I comprised survey data of 1,065 students on self-reported visual memory skills and their ability to find three targets in four images of artwork. Experiment II comprised eye movement data of 50 Visual Literacy (VL) experts and non-experts whose eye movements during visual search were analyzed for nine images of artwork as an external validation of the assessment tasks performed in Sample I. No time constraint was set for completion of the visual search task. A latent profile analysis revealed four typical solution patterns for the students in Sample I, including a mainstream group, a group that completes easy images fast and difficult images slowly, a fast and erroneous group, and a slow working student group, depending on task completion time and on the probability of finding all three targets. Eidetic memory, performance in art education and visual imagination as self-reported visual skills have significant impact on latent class membership probability. We present a hidden Markov model (HMM) approach to uncover underlying regions of attraction that result from visual search eye-movement behavior in Experiment II. VL experts and non-experts did not significantly differ in task time and number of targets found but they did differ in their visual search process: compared to non-experts, experts showed greater precision in fixating specific prime and target regions, assessed through hidden state fixation overlap. Exploratory analysis of HMMs revealed differences between experts and non-experts in image locations of attraction (HMM states). Experts seem to focus their attention on smaller image parts whereas non-experts used wider parts of the image during their search. Differences between experts and non-experts depend on the relative saliency of targets embedded in images. HMMs can determine the effect of expertise on exploratory eye movements executed during visual search tasks. Further research on HMMs and art expertise is required to confirm exploratory results.
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Affiliation(s)
- Miles Tallon
- Department of Experimental Psychology, University of Regensburg, Regensburg, Germany
- HSD Research Centre Cologne, HSD University of Applied Sciences, Cologne, Germany
| | - Mark W. Greenlee
- Department of Experimental Psychology, University of Regensburg, Regensburg, Germany
| | | | - Katrin Rakoczy
- HSD Research Centre Cologne, HSD University of Applied Sciences, Cologne, Germany
- DIPF Leibniz Institute for Research and Information in Education, Frankfurt am Main, Germany
| | - Ulrich Frick
- HSD Research Centre Cologne, HSD University of Applied Sciences, Cologne, Germany
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Kliewer MA, Hartung M, Green CS. The Search Patterns of Abdominal Imaging Subspecialists for Abdominal Computed Tomography: Toward a Foundational Pattern for New Radiology Residents. J Clin Imaging Sci 2021; 11:1. [PMID: 33500836 PMCID: PMC7827582 DOI: 10.25259/jcis_195_2020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 12/09/2020] [Indexed: 11/04/2022] Open
Abstract
Objectives: The routine search patterns used by subspecialty abdominal imaging experts to inspect the image volumes of abdominal/pelvic computed tomography (CT) have not been well characterized or rendered in practical or teachable terms. The goal of this study is to describe the search patterns used by experienced subspecialty imagers when reading a normal abdominal CT at a modern picture archiving and communication system workstation, and utilize this information to propose guidelines for residents as they learn to interpret CT during training. Material and Methods: Twenty-two academic subspecialists enacted their routine search pattern on a normal contrast-enhanced abdominal/pelvic CT study under standardized display parameters. Readers were told that the scan was normal and then asked to verbalize where their gaze centered and moved through the axial, coronal, and sagittal image stacks, demonstrating eye position with a cursor as needed. A peer coded the reported eye gaze movements and scrilling behavior. Spearman correlation coefficients were calculated between years of professional experience and the numbers of passes through the lung bases, liver, kidneys, and bowel. Results: All readers followed an initial organ-by-organ approach. Larger organs were examined by drilling, while smaller organs by oscillation or scanning. Search elements were classified as drilling, scanning, oscillation, and scrilling (scan drilling); these categories were parsed as necessary. The greatest variability was found in the examination the body wall and bowel/mesentery. Two modes of scrilling were described, and these classified as roaming and zigzagging. The years of experience of the readers did not correlated to number of passes made through the lung bases, liver, kidneys, or bowel. Conclusion: Subspecialty abdominal radiologists negotiate through the image stacks of an abdominal CT study in broadly similar ways. Collation of the approaches suggests a foundational search pattern for new trainees.
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Affiliation(s)
- Mark A Kliewer
- Department of Radiology and Ultrasound Imaging, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States
| | - Michael Hartung
- Department of Radiology and Ultrasound Imaging, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States
| | - C Shawn Green
- Department of Psychology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States
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