Modeling sequential context effects in diagnostic interpretation of screening mammograms

Alamudun, Folami; Paulus, Paige; Yoon, Hong-Jun; Tourassi, Georgia

doi:10.1117/1.JMI.5.3.031408

Title: Modeling sequential context effects in diagnostic interpretation of screening mammograms

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Abstract

Prior research has shown that physicians’ medical decisions can be influenced by sequential context, particularly in cases where successive stimuli exhibit similar characteristics when analyzing medical images. This type of systematic error is known to psychophysicists as sequential context effect as it indicates that judgments are influenced by features of and decisions about the preceding case in the sequence of examined cases, rather than being based solely on the peculiarities unique to the present case. We determine if radiologists experience some form of context bias, using screening mammography as the use case. To this end, we explore correlations between previous perceptual behavior and diagnostic decisions and current decisions. We hypothesize that a radiologist’s visual search pattern and diagnostic decisions in previous cases are predictive of the radiologist’s current diagnostic decisions. To test our hypothesis, we tasked 10 radiologists of varied experience to conduct blind reviews of 100 four-view screening mammograms. Eye-tracking data and diagnostic decisions were collected from each radiologist under conditions mimicking clinical practice. Perceptual behavior was quantified using the fractal dimension of gaze scanpath, which was computed using the Minkowski–Bouligand box-counting method. To test the effect of previous behavior and decisions, we conducted a multifactor fixed-effects ANOVA. Further,more »« less

Authors:

Alamudun, Folami ^[1]; Paulus, Paige ^[2]; Yoon, Hong-Jun ^[1]; Tourassi, Georgia ^[3]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Sciences and Engineering Division. Health Data Sciences Inst.
Univ. of Tennessee, Knoxville, TN (United States). Dept. of Mechanical, Aerospace, and Biomedical Engineering
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Sciences and Engineering Division. Health Data Sciences Inst.; Univ. of Tennessee, Knoxville, TN (United States). Dept. of Mechanical, Aerospace, and Biomedical Engineering

Publication Date:: Mon Mar 19 00:00:00 EDT 2018

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1460180

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Medical Imaging

Additional Journal Information:: Journal Volume: 5; Journal Issue: 3; Journal ID: ISSN 2329-4302

Publisher:: SPIE

Country of Publication:: United States

Language:: English

Subject:: 60 APPLIED LIFE SCIENCES; perceptual behavior; eye tracking; mammography; cognitive bias; context effect

Citation Formats


                    Alamudun, Folami, Paulus, Paige, Yoon, Hong-Jun, and Tourassi, Georgia. Modeling sequential context effects in diagnostic interpretation of screening mammograms.  United States: N. p., 2018. 
Web.  doi:10.1117/1.JMI.5.3.031408.

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                    Alamudun, Folami, Paulus, Paige, Yoon, Hong-Jun, & Tourassi, Georgia. Modeling sequential context effects in diagnostic interpretation of screening mammograms.  United States.  https://doi.org/10.1117/1.JMI.5.3.031408

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                    Alamudun, Folami, Paulus, Paige, Yoon, Hong-Jun, and Tourassi, Georgia. Mon .  
"Modeling sequential context effects in diagnostic interpretation of screening mammograms".  United States.  https://doi.org/10.1117/1.JMI.5.3.031408.  https://www.osti.gov/servlets/purl/1460180.

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@article{osti_1460180,

  title        = {Modeling sequential context effects in diagnostic interpretation of screening mammograms},

  author       = {Alamudun, Folami and Paulus, Paige and Yoon, Hong-Jun and Tourassi, Georgia},

  abstractNote = {Prior research has shown that physicians’ medical decisions can be influenced by sequential context, particularly in cases where successive stimuli exhibit similar characteristics when analyzing medical images. This type of systematic error is known to psychophysicists as sequential context effect as it indicates that judgments are influenced by features of and decisions about the preceding case in the sequence of examined cases, rather than being based solely on the peculiarities unique to the present case. We determine if radiologists experience some form of context bias, using screening mammography as the use case. To this end, we explore correlations between previous perceptual behavior and diagnostic decisions and current decisions. We hypothesize that a radiologist’s visual search pattern and diagnostic decisions in previous cases are predictive of the radiologist’s current diagnostic decisions. To test our hypothesis, we tasked 10 radiologists of varied experience to conduct blind reviews of 100 four-view screening mammograms. Eye-tracking data and diagnostic decisions were collected from each radiologist under conditions mimicking clinical practice. Perceptual behavior was quantified using the fractal dimension of gaze scanpath, which was computed using the Minkowski–Bouligand box-counting method. To test the effect of previous behavior and decisions, we conducted a multifactor fixed-effects ANOVA. Further, to examine the predictive value of previous perceptual behavior and decisions, we trained and evaluated a predictive model for radiologists’ current diagnostic decisions. ANOVA tests showed that previous visual behavior, characterized by fractal analysis, previous diagnostic decisions, and image characteristics of previous cases are significant predictors of current diagnostic decisions. Additionally, predictive modeling of diagnostic decisions showed an overall improvement in prediction error when the model is trained on additional information about previous perceptual behavior and diagnostic decisions.},

  doi          = {10.1117/1.JMI.5.3.031408},

  journal      = {Journal of Medical Imaging},

  number       = 3,

  volume       = 5,

  place        = {United States},

  year         = {Mon Mar 19 00:00:00 EDT 2018},

  month        = {Mon Mar 19 00:00:00 EDT 2018}

}

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