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Alexander R, Waite S, Bruno MA, Krupinski EA, Berlin L, Macknik S, Martinez-Conde S. Mandating Limits on Workload, Duty, and Speed in Radiology. Radiology 2022; 304:274-282. [PMID: 35699581 PMCID: PMC9340237 DOI: 10.1148/radiol.212631] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Research has not yet quantified the effects of workload or duty hours on the accuracy of radiologists. With the exception of a brief reduction in imaging studies during the 2020 peak of the COVID-19 pandemic, the workload of radiologists in the United States has seen relentless growth in recent years. One concern is that this increased demand could lead to reduced accuracy. Behavioral studies in species ranging from insects to humans have shown that decision speed is inversely correlated to decision accuracy. A potential solution is to institute workload and duty limits to optimize radiologist performance and patient safety. The concern, however, is that any prescribed mandated limits would be arbitrary and thus no more advantageous than allowing radiologists to self-regulate. Specific studies have been proposed to determine whether limits reduce error, and if so, to provide a principled basis for such limits. This could determine the precise susceptibility of individual radiologists to medical error as a function of speed during image viewing, the maximum number of studies that could be read during a work shift, and the appropriate shift duration as a function of time of day. Before principled recommendations for restrictions are made, however, it is important to understand how radiologists function both optimally and at the margins of adequate performance. This study examines the relationship between interpretation speed and error rates in radiology, the potential influence of artificial intelligence on reading speed and error rates, and the possible outcomes of imposed limits on both caseload and duty hours. This review concludes that the scientific evidence needed to make meaningful rules is lacking and notes that regulating workloads without scientific principles can be more harmful than not regulating at all.
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Affiliation(s)
- Robert Alexander
- From the Departments of Ophthalmology (R.A., S.M., S.M.C.), Radiology (S.W.), Neurology (S.M., S.M.C.), and Physiology & Pharmacology (S.M., S.M.C.), SUNY Downstate Health Sciences University, 450 Clarkson Ave, Brooklyn, NY 11203; Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pa (M.A.B.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (E.A.K.); and Department of Radiology, Rush University Medical College and University of Illinois, Chicago, Ill (L.B.)
| | - Stephen Waite
- From the Departments of Ophthalmology (R.A., S.M., S.M.C.), Radiology (S.W.), Neurology (S.M., S.M.C.), and Physiology & Pharmacology (S.M., S.M.C.), SUNY Downstate Health Sciences University, 450 Clarkson Ave, Brooklyn, NY 11203; Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pa (M.A.B.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (E.A.K.); and Department of Radiology, Rush University Medical College and University of Illinois, Chicago, Ill (L.B.)
| | - Michael A Bruno
- From the Departments of Ophthalmology (R.A., S.M., S.M.C.), Radiology (S.W.), Neurology (S.M., S.M.C.), and Physiology & Pharmacology (S.M., S.M.C.), SUNY Downstate Health Sciences University, 450 Clarkson Ave, Brooklyn, NY 11203; Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pa (M.A.B.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (E.A.K.); and Department of Radiology, Rush University Medical College and University of Illinois, Chicago, Ill (L.B.)
| | - Elizabeth A Krupinski
- From the Departments of Ophthalmology (R.A., S.M., S.M.C.), Radiology (S.W.), Neurology (S.M., S.M.C.), and Physiology & Pharmacology (S.M., S.M.C.), SUNY Downstate Health Sciences University, 450 Clarkson Ave, Brooklyn, NY 11203; Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pa (M.A.B.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (E.A.K.); and Department of Radiology, Rush University Medical College and University of Illinois, Chicago, Ill (L.B.)
| | - Leonard Berlin
- From the Departments of Ophthalmology (R.A., S.M., S.M.C.), Radiology (S.W.), Neurology (S.M., S.M.C.), and Physiology & Pharmacology (S.M., S.M.C.), SUNY Downstate Health Sciences University, 450 Clarkson Ave, Brooklyn, NY 11203; Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pa (M.A.B.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (E.A.K.); and Department of Radiology, Rush University Medical College and University of Illinois, Chicago, Ill (L.B.)
| | - Stephen Macknik
- From the Departments of Ophthalmology (R.A., S.M., S.M.C.), Radiology (S.W.), Neurology (S.M., S.M.C.), and Physiology & Pharmacology (S.M., S.M.C.), SUNY Downstate Health Sciences University, 450 Clarkson Ave, Brooklyn, NY 11203; Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pa (M.A.B.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (E.A.K.); and Department of Radiology, Rush University Medical College and University of Illinois, Chicago, Ill (L.B.)
| | - Susana Martinez-Conde
- From the Departments of Ophthalmology (R.A., S.M., S.M.C.), Radiology (S.W.), Neurology (S.M., S.M.C.), and Physiology & Pharmacology (S.M., S.M.C.), SUNY Downstate Health Sciences University, 450 Clarkson Ave, Brooklyn, NY 11203; Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pa (M.A.B.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (E.A.K.); and Department of Radiology, Rush University Medical College and University of Illinois, Chicago, Ill (L.B.)
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Wu CC, Wolfe JM. The Functional Visual Field(s) in simple visual search. Vision Res 2022; 190:107965. [PMID: 34775158 PMCID: PMC8976560 DOI: 10.1016/j.visres.2021.107965] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 10/07/2021] [Accepted: 10/12/2021] [Indexed: 01/03/2023]
Abstract
During a visual search for a target among distractors, observers do not fixate every location in the search array. Rather processing is thought to occur within a Functional Visual Field (FVF) surrounding each fixation. We argue that there are three questions that can be asked at each fixation and that these imply three different senses of the FVF. 1) Can I identify what is at location XY? This defines a resolution FVF. 2) To what shall I attend during this fixation? This defines an Attentional FVF. 3) Where should I fixate next? This defines an Exploratory FVF. We examine FVFs 2&3 using eye movements in visual search. In three Experiments, we collected eye movements during visual search for the target letter T among distractor letter Ls (Exps 1 and 3) or for a color X orientation conjunction (Exp 2). Saccades that do not go to the target can be used to define the Exploratory FVF. The saccade that goes to the target can be used to define the Attentional FVF since the target was probably covertly detected during the prior fixation. The Exploratory FVF is larger than the Attentional FVF for all three experiments. Interestingly, the probability that the next saccade would go to the target was always well below 1.0, even when the current fixation was close to the target and well within any reasonable estimate of the FVF. Measuring search-based Exploratory and Attentional FVFs sheds light on how we can miss clearly visible targets.
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Affiliation(s)
- Chia-Chien Wu
- Harvard Medical School, Boston, MA, USA; Brigham & Women's Hospital, Boston, MA, USA.
| | - Jeremy M Wolfe
- Harvard Medical School, Boston, MA, USA; Brigham & Women's Hospital, Boston, MA, USA
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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: 6] [Impact Index Per Article: 2.0] [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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Abstract
Breast cancer is the most common cancer among females worldwide and large volumes of breast images are produced and interpreted annually. As long as radiologists interpret these images, the diagnostic accuracy will be limited by human factors and both false-positive and false-negative errors might occur. By understanding visual search in breast images, we may be able to identify causes of diagnostic errors, find ways to reduce them, and also provide a better education to radiology residents. Many visual search studies in breast radiology have been devoted to mammography. These studies showed that 70% of missed lesions on mammograms attract radiologists' visual attention and that a plethora of different reasons, such as satisfaction of search, incorrect background sampling, and incorrect first impression can cause diagnostic errors in the interpretation of mammograms. Recently, highly accurate tools, which rely on both eye-tracking data and the content of the mammogram, have been proposed to provide feedback to the radiologists. Improving these tools and determining the optimal pathway to integrate them in the radiology workflow could be a possible line of future research. Moreover, in the past few years deep learning has led to improving diagnostic accuracy of computerized diagnostic tools and visual search studies will be required to understand how radiologists interact with the prompts from these tools, and to identify the best way to utilize them. Visual search in other breast imaging modalities, such as breast ultrasound and digital breast tomosynthesis, have so far received less attention, probably due to associated complexities of eye-tracking monitoring and analysing the data. For example, in digital breast tomosynthesis, scrolling through the image results in longer trials, adds a new factor to the study's complexity and makes calculation of gaze parameters more difficult. However, considering the wide utilization of three-dimensional imaging modalities, more visual search studies involving reading stack-view examinations are required in the future. To conclude, in the past few decades visual search studies provided extensive understanding about underlying reasons for diagnostic errors in breast radiology and characterized differences between experts' and novices' visual search patterns. Further visual search studies are required to investigate radiologists' interaction with relatively newer imaging modalities and artificial intelligence tools.
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Affiliation(s)
- Ziba Gandomkar
- BreastScreen Reader Assessment Strategy (BREAST), Discipline of Medical Imaging Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Claudia Mello-Thoms
- Department of Radiology, Carver College of Medicine, University of Iowa, Iowa City, IA, US
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Lee S, Hwang Y, Jin Y, Ahn S, Park J. Effects of Individuality, Education, and Image on Visual Attention: Analyzing Eye-tracking Data using Machine Learning. J Eye Mov Res 2019; 12. [PMID: 33828727 PMCID: PMC7881890 DOI: 10.16910/jemr.12.2.4] [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] [Indexed: 11/26/2022] Open
Abstract
Machine learning, particularly classification algorithms, constructs mathematical models from labeled data that can predict labels for new data. Using its capability to identify distinguishing patterns among multi-dimensional data, we investigated the impact of three factors on the observation of architectural scenes: individuality, education, and image stimuli. An analysis of the eye-tracking data revealed that (1) a velocity histogram was unique to individuals, (2) students of architecture and other disciplines could be distinguished via endogenous parameters, but (3) they were more distinct in terms of seeking structural versus symbolic elements. Because of the reverse nature of the classification algorithms that automatically learn from data, we could identify relevant parameters and distinguishing eye-tracking patterns that have not been reported in previous studies.
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Affiliation(s)
| | - Yongha Hwang
- University of Michigan, Ann Arbor, United States
| | - Yan Jin
- Qingdao University of Technology, Qingdao, China
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Williams LH, Drew T. What do we know about volumetric medical image interpretation?: a review of the basic science and medical image perception literatures. Cogn Res Princ Implic 2019; 4:21. [PMID: 31286283 PMCID: PMC6614227 DOI: 10.1186/s41235-019-0171-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Accepted: 05/19/2019] [Indexed: 11/26/2022] Open
Abstract
Interpretation of volumetric medical images represents a rapidly growing proportion of the workload in radiology. However, relatively little is known about the strategies that best guide search behavior when looking for abnormalities in volumetric images. Although there is extensive literature on two-dimensional medical image perception, it is an open question whether the conclusions drawn from these images can be generalized to volumetric images. Importantly, volumetric images have distinct characteristics (e.g., scrolling through depth, smooth-pursuit eye-movements, motion onset cues, etc.) that should be considered in future research. In this manuscript, we will review the literature on medical image perception and discuss relevant findings from basic science that can be used to generate predictions about expertise in volumetric image interpretation. By better understanding search through volumetric images, we may be able to identify common sources of error, characterize the optimal strategies for searching through depth, or develop new training and assessment techniques for radiology residents.
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Waite S, Grigorian A, Alexander RG, Macknik SL, Carrasco M, Heeger DJ, Martinez-Conde S. Analysis of Perceptual Expertise in Radiology - Current Knowledge and a New Perspective. Front Hum Neurosci 2019; 13:213. [PMID: 31293407 PMCID: PMC6603246 DOI: 10.3389/fnhum.2019.00213] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Accepted: 06/07/2019] [Indexed: 12/14/2022] Open
Abstract
Radiologists rely principally on visual inspection to detect, describe, and classify findings in medical images. As most interpretive errors in radiology are perceptual in nature, understanding the path to radiologic expertise during image analysis is essential to educate future generations of radiologists. We review the perceptual tasks and challenges in radiologic diagnosis, discuss models of radiologic image perception, consider the application of perceptual learning methods in medical training, and suggest a new approach to understanding perceptional expertise. Specific principled enhancements to educational practices in radiology promise to deepen perceptual expertise among radiologists with the goal of improving training and reducing medical error.
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Affiliation(s)
- Stephen Waite
- Department of Radiology, SUNY Downstate Medical Center, Brooklyn, NY, United States
| | - Arkadij Grigorian
- Department of Radiology, SUNY Downstate Medical Center, Brooklyn, NY, United States
| | - Robert G. Alexander
- Department of Ophthalmology, SUNY Downstate Medical Center, Brooklyn, NY, United States
- Department of Neurology, SUNY Downstate Medical Center, Brooklyn, NY, United States
- Department of Physiology/Pharmacology, SUNY Downstate Medical Center, Brooklyn, NY, United States
| | - Stephen L. Macknik
- Department of Ophthalmology, SUNY Downstate Medical Center, Brooklyn, NY, United States
- Department of Neurology, SUNY Downstate Medical Center, Brooklyn, NY, United States
- Department of Physiology/Pharmacology, SUNY Downstate Medical Center, Brooklyn, NY, United States
| | - Marisa Carrasco
- Department of Psychology and Center for Neural Science, New York University, New York, NY, United States
| | - David J. Heeger
- Department of Psychology and Center for Neural Science, New York University, New York, NY, United States
| | - Susana Martinez-Conde
- Department of Ophthalmology, SUNY Downstate Medical Center, Brooklyn, NY, United States
- Department of Neurology, SUNY Downstate Medical Center, Brooklyn, NY, United States
- Department of Physiology/Pharmacology, SUNY Downstate Medical Center, Brooklyn, NY, United States
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Wu CC, Wolfe JM. Eye Movements in Medical Image Perception: A Selective Review of Past, Present and Future. Vision (Basel) 2019; 3:E32. [PMID: 31735833 PMCID: PMC6802791 DOI: 10.3390/vision3020032] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 06/09/2019] [Accepted: 06/18/2019] [Indexed: 12/21/2022] Open
Abstract
The eye movements of experts, reading medical images, have been studied for many years. Unlike topics such as face perception, medical image perception research needs to cope with substantial, qualitative changes in the stimuli under study due to dramatic advances in medical imaging technology. For example, little is known about how radiologists search through 3D volumes of image data because they simply did not exist when earlier eye tracking studies were performed. Moreover, improvements in the affordability and portability of modern eye trackers make other, new studies practical. Here, we review some uses of eye movements in the study of medical image perception with an emphasis on newer work. We ask how basic research on scene perception relates to studies of medical 'scenes' and we discuss how tracking experts' eyes may provide useful insights for medical education and screening efficiency.
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Affiliation(s)
- Chia-Chien Wu
- Visual Attention Lab, Department of Surgery, Brigham & Women’s Hospital, 65 Landsdowne St, Cambridge, MA 02139, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Jeremy M. Wolfe
- Visual Attention Lab, Department of Surgery, Brigham & Women’s Hospital, 65 Landsdowne St, Cambridge, MA 02139, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA 02115, USA
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Donnelly N, Muhl-Richardson A, Godwin HJ, Cave KR. Using Eye Movements to Understand how Security Screeners Search for Threats in X-Ray Baggage. Vision (Basel) 2019; 3:vision3020024. [PMID: 31735825 PMCID: PMC6802782 DOI: 10.3390/vision3020024] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 05/17/2019] [Accepted: 06/01/2019] [Indexed: 12/01/2022] Open
Abstract
There has been an increasing drive to understand failures in searches for weapons and explosives in X-ray baggage screening. Tracking eye movements during the search has produced new insights into the guidance of attention during the search, and the identification of targets once they are fixated. Here, we review the eye-movement literature that has emerged on this front over the last fifteen years, including a discussion of the problems that real-world searchers face when trying to detect targets that could do serious harm to people and infrastructure.
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Affiliation(s)
- Nick Donnelly
- Department of Psychology, Liverpool Hope University, Liverpool L16 9JD, UK
| | | | - Hayward J. Godwin
- Psychology, University of Southampton, Southampton SO17 1BJ, UK
- Correspondence:
| | - Kyle R. Cave
- Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, MA 01003, USA
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Brunyé TT, Nallamothu BK, Elmore JG. Eye-tracking for assessing medical image interpretation: A pilot feasibility study comparing novice vs expert cardiologists. PERSPECTIVES ON MEDICAL EDUCATION 2019; 8:65-73. [PMID: 30977060 PMCID: PMC6468026 DOI: 10.1007/s40037-019-0505-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
INTRODUCTION As specialized medical professionals such as radiologists, pathologists, and cardiologists gain education and experience, their diagnostic efficiency and accuracy change, and they show altered eye movement patterns during medical image interpretation. Existing research in this area is limited to interpretation of static medical images, such as digitized whole slide biopsies, making it difficult to understand how expertise development might manifest during dynamic image interpretation, such as with angiograms or volumetric scans. METHODS A two-group (novice, expert) comparative pilot study examined the feasibility and utility of tracking and interpreting eye movement patterns while cardiologists viewed video-based coronary angiograms. A non-invasive eye tracking system recorded cardiologists' (n = 8) visual behaviour while they viewed and diagnosed a series of eight angiogram videos. Analyses assessed frame-by-frame video navigation behaviour, eye fixation behaviour, and resulting diagnostic decision making. RESULTS Relative to novices, expert cardiologists demonstrated shorter and less variable video review times, fewer eye fixations and saccadic eye movements, and less time spent paused on individual video frames. Novices showed repeated eye fixations on critical image frames and regions, though these were not predictive of accurate diagnostic decisions. DISCUSSION These preliminary results demonstrate interpretive decision errors among novices, suggesting they identify and process critical diagnostic features, but sometimes fail to accurately interpret those features. Results also showcase the feasibility of tracking and understanding eye movements during video-based coronary angiogram interpretation and suggest that eye tracking may be valuable for informing assessments of competency progression during medical education and training.
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Affiliation(s)
- Tad T. Brunyé
- Center for Applied Brain & Cognitive Sciences, Tufts University, Medford, MA USA
| | | | - Joann G. Elmore
- Department of Medicine, University of Washington, Seattle, WA USA
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Khosravan N, Celik H, Turkbey B, Jones EC, Wood B, Bagci U. A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning. Med Image Anal 2019; 51:101-115. [PMID: 30399507 PMCID: PMC6407631 DOI: 10.1016/j.media.2018.10.010] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Revised: 07/27/2018] [Accepted: 10/26/2018] [Indexed: 12/19/2022]
Abstract
Computer aided diagnosis (CAD) tools help radiologists to reduce diagnostic errors such as missing tumors and misdiagnosis. Vision researchers have been analyzing behaviors of radiologists during screening to understand how and why they miss tumors or misdiagnose. In this regard, eye-trackers have been instrumental in understanding visual search processes of radiologists. However, most relevant studies in this aspect are not compatible with realistic radiology reading rooms. In this study, we aim to develop a paradigm shifting CAD system, called collaborative CAD (C-CAD), that unifies CAD and eye-tracking systems in realistic radiology room settings. We first developed an eye-tracking interface providing radiologists with a real radiology reading room experience. Second, we propose a novel algorithm that unifies eye-tracking data and a CAD system. Specifically, we present a new graph based clustering and sparsification algorithm to transform eye-tracking data (gaze) into a graph model to interpret gaze patterns quantitatively and qualitatively. The proposed C-CAD collaborates with radiologists via eye-tracking technology and helps them to improve their diagnostic decisions. The C-CAD uses radiologists' search efficiency by processing their gaze patterns. Furthermore, the C-CAD incorporates a deep learning algorithm in a newly designed multi-task learning platform to segment and diagnose suspicious areas simultaneously. The proposed C-CAD system has been tested in a lung cancer screening experiment with multiple radiologists, reading low dose chest CTs. Promising results support the efficiency, accuracy and applicability of the proposed C-CAD system in a real radiology room setting. We have also shown that our framework is generalizable to more complex applications such as prostate cancer screening with multi-parametric magnetic resonance imaging (mp-MRI).
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Affiliation(s)
- Naji Khosravan
- Center for Research in Computer Vision, University of Central Florida, FL, United States
| | - Haydar Celik
- Clinical Center, National Institutes of Health, Bethesda, MD, United States
| | - Baris Turkbey
- Clinical Center, National Institutes of Health, Bethesda, MD, United States
| | - Elizabeth C Jones
- Clinical Center, National Institutes of Health, Bethesda, MD, United States
| | - Bradford Wood
- Clinical Center, National Institutes of Health, Bethesda, MD, United States
| | - Ulas Bagci
- Center for Research in Computer Vision, University of Central Florida, FL, United States.
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den Boer L, van der Schaaf MF, Vincken KL, Mol CP, Stuijfzand BG, van der Gijp A. Volumetric image interpretation in radiology: scroll behavior and cognitive processes. ADVANCES IN HEALTH SCIENCES EDUCATION : THEORY AND PRACTICE 2018; 23:783-802. [PMID: 29767400 PMCID: PMC6132416 DOI: 10.1007/s10459-018-9828-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 05/07/2018] [Indexed: 05/12/2023]
Abstract
The interpretation of medical images is a primary task for radiologists. Besides two-dimensional (2D) images, current imaging technologies allow for volumetric display of medical images. Whereas current radiology practice increasingly uses volumetric images, the majority of studies on medical image interpretation is conducted on 2D images. The current study aimed to gain deeper insight into the volumetric image interpretation process by examining this process in twenty radiology trainees who all completed four volumetric image cases. Two types of data were obtained concerning scroll behaviors and think-aloud data. Types of scroll behavior concerned oscillations, half runs, full runs, image manipulations, and interruptions. Think-aloud data were coded by a framework of knowledge and skills in radiology including three cognitive processes: perception, analysis, and synthesis. Relating scroll behavior to cognitive processes showed that oscillations and half runs coincided more often with analysis and synthesis than full runs, whereas full runs coincided more often with perception than oscillations and half runs. Interruptions were characterized by synthesis and image manipulations by perception. In addition, we investigated relations between cognitive processes and found an overall bottom-up way of reasoning with dynamic interactions between cognitive processes, especially between perception and analysis. In sum, our results highlight the dynamic interactions between these processes and the grounding of cognitive processes in scroll behavior. It suggests, that the types of scroll behavior are relevant to describe how radiologists interact with and manipulate volumetric images.
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Affiliation(s)
- Larissa den Boer
- Utrecht University, Heidelberglaan 1, 3584 CS, Utrecht, The Netherlands.
| | | | - Koen L Vincken
- University Medical Center Utrecht, Utrecht, The Netherlands
| | - Chris P Mol
- University Medical Center Utrecht, Utrecht, The Netherlands
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Aizenman A, Drew T, Ehinger KA, Georgian-Smith D, Wolfe JM. Comparing search patterns in digital breast tomosynthesis and full-field digital mammography: an eye tracking study. J Med Imaging (Bellingham) 2017; 4:045501. [PMID: 29098168 DOI: 10.1117/1.jmi.4.4.045501] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 10/02/2017] [Indexed: 11/14/2022] Open
Abstract
As a promising imaging modality, digital breast tomosynthesis (DBT) leads to better diagnostic performance than traditional full-field digital mammograms (FFDM) alone. DBT allows different planes of the breast to be visualized, reducing occlusion from overlapping tissue. Although DBT is gaining popularity, best practices for search strategies in this medium are unclear. Eye tracking allowed us to describe search patterns adopted by radiologists searching DBT and FFDM images. Eleven radiologists examined eight DBT and FFDM cases. Observers marked suspicious masses with mouse clicks. Eye position was recorded at 1000 Hz and was coregistered with slice/depth plane as the radiologist scrolled through the DBT images, allowing a 3-D representation of eye position. Hit rate for masses was higher for tomography cases than 2-D cases and DBT led to lower false positive rates. However, search duration was much longer for DBT cases than FFDM. DBT was associated with longer fixations but similar saccadic amplitude compared with FFDM. When comparing radiologists' eye movements to a previous study, which tracked eye movements as radiologists read chest CT, we found DBT viewers did not align with previously identified "driller" or "scanner" strategies, although their search strategy most closely aligns with a type of vigorous drilling strategy.
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Affiliation(s)
- Avi Aizenman
- University of California, Vision Science Department, Berkeley, California, United States
| | - Trafton Drew
- University of Utah, Psychology Department, Salt Lake City, Utah, United States
| | - Krista A Ehinger
- York University, Centre for Vision Research, Toronto, Ontario, Canada
| | - Dianne Georgian-Smith
- Brigham and Women's Hospital, Surgery Department, Boston, Massachusetts, United States
| | - Jeremy M Wolfe
- Brigham and Women's Hospital, Surgery Department, Boston, Massachusetts, United States.,Harvard Medical School, Ophthalmology and Radiology Department, Boston, Massachusetts, United States
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14
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Sheridan H, Reingold EM. The Holistic Processing Account of Visual Expertise in Medical Image Perception: A Review. Front Psychol 2017; 8:1620. [PMID: 29033865 PMCID: PMC5627012 DOI: 10.3389/fpsyg.2017.01620] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 09/04/2017] [Indexed: 12/11/2022] Open
Abstract
In the field of medical image perception, the holistic processing perspective contends that experts can rapidly extract global information about the image, which can be used to guide their subsequent search of the image (Swensson, 1980; Nodine and Kundel, 1987; Kundel et al., 2007). In this review, we discuss the empirical evidence supporting three different predictions that can be derived from the holistic processing perspective: Expertise in medical image perception is domain-specific, experts use parafoveal and/or peripheral vision to process large regions of the image in parallel, and experts benefit from a rapid initial glimpse of an image. In addition, we discuss a pivotal recent study (Litchfield and Donovan, 2016) that seems to contradict the assumption that experts benefit from a rapid initial glimpse of the image. To reconcile this finding with the existing literature, we suggest that global processing may serve multiple functions that extend beyond the initial glimpse of the image. Finally, we discuss future research directions, and we highlight the connections between the holistic processing account and similar theoretical perspectives and findings from other domains of visual expertise.
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Affiliation(s)
- Heather Sheridan
- Department of Psychology, University at Albany, State University of New York, Albany, NY, United States
| | - Eyal M. Reingold
- Department of Psychology, University of Toronto, Mississauga, ON, Canada
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15
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van der Gijp A, Ravesloot CJ, Jarodzka H, van der Schaaf MF, van der Schaaf IC, van Schaik JPJ, Ten Cate TJ. How visual search relates to visual diagnostic performance: a narrative systematic review of eye-tracking research in radiology. ADVANCES IN HEALTH SCIENCES EDUCATION : THEORY AND PRACTICE 2017; 22:765-787. [PMID: 27436353 PMCID: PMC5498587 DOI: 10.1007/s10459-016-9698-1] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 07/09/2016] [Indexed: 05/26/2023]
Abstract
Eye tracking research has been conducted for decades to gain understanding of visual diagnosis such as in radiology. For educational purposes, it is important to identify visual search patterns that are related to high perceptual performance and to identify effective teaching strategies. This review of eye-tracking literature in the radiology domain aims to identify visual search patterns associated with high perceptual performance. Databases PubMed, EMBASE, ERIC, PsycINFO, Scopus and Web of Science were searched using 'visual perception' OR 'eye tracking' AND 'radiology' and synonyms. Two authors independently screened search results and included eye tracking studies concerning visual skills in radiology published between January 1, 1994 and July 31, 2015. Two authors independently assessed study quality with the Medical Education Research Study Quality Instrument, and extracted study data with respect to design, participant and task characteristics, and variables. A thematic analysis was conducted to extract and arrange study results, and a textual narrative synthesis was applied for data integration and interpretation. The search resulted in 22 relevant full-text articles. Thematic analysis resulted in six themes that informed the relation between visual search and level of expertise: (1) time on task, (2) eye movement characteristics of experts, (3) differences in visual attention, (4) visual search patterns, (5) search patterns in cross sectional stack imaging, and (6) teaching visual search strategies. Expert search was found to be characterized by a global-focal search pattern, which represents an initial global impression, followed by a detailed, focal search-to-find mode. Specific task-related search patterns, like drilling through CT scans and systematic search in chest X-rays, were found to be related to high expert levels. One study investigated teaching of visual search strategies, and did not find a significant effect on perceptual performance. Eye tracking literature in radiology indicates several search patterns are related to high levels of expertise, but teaching novices to search as an expert may not be effective. Experimental research is needed to find out which search strategies can improve image perception in learners.
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Affiliation(s)
- A van der Gijp
- Radiology Department, University Medical Center Utrecht, E01.132, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
| | - C J Ravesloot
- Radiology Department, University Medical Center Utrecht, E01.132, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - H Jarodzka
- Center for Learning Science and Technologies, Open University of the Netherlands, Heerlen, The Netherlands
| | | | - I C van der Schaaf
- Radiology Department, University Medical Center Utrecht, E01.132, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - J P J van Schaik
- Radiology Department, University Medical Center Utrecht, E01.132, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Th J Ten Cate
- Center for Research and Development of Education, University Medical Center Utrecht, Utrecht, The Netherlands
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16
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Jiang Z, Das M, Gifford HC. Analyzing visual-search observers using eye-tracking data for digital breast tomosynthesis images. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2017; 34:838-845. [PMID: 29036067 DOI: 10.1364/josaa.34.000838] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Visual-search (VS) model observers have the potential to provide reliable predictions of human-observer performance in detection-localization tasks. The purpose of this work was to examine some characteristics of human gaze on breast images with the goal of informing the design of our VS observers. Using a helmet-mounted eye-tracking system, we recorded the movement of gaze from human observers as they searched for masses in sets of 2D digital breast tomosynthesis (DBT) images. The masses in this study were of a single profile. The DBT images were extracted from image volumes reconstructed with the filtered backprojection method. Fixation times associated with observer points of interest were computed from the observer data. We used the k-mean clustering algorithm to get dwell times of gaze data. The dwell times were then compared to sets of morphological feature values extracted from the images. These features, extracted as cross correlations involving the mass profile and the test image, included the matched filter (MF), gradient MF, Laplacian MF, and adaptive MF. The adaptive MF combining four feature maps was computed using a hotelling discriminant generated from training data. For this investigation, we computed correlation coefficients between the fixation times and the feature values. We also conducted a significance test by computing p-values of correlation coefficients for five features. Of all these features, the adaptive MF provided the highest correlation coefficients for DBT images with different densities.
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