Advertisement

Japanese Journal of Radiology

, Volume 37, Issue 6, pp 437–448 | Cite as

Monitoring of fatigue in radiologists during prolonged image interpretation using fNIRS

  • Takashi NihashiEmail author
  • Takeo Ishigaki
  • Hiroko Satake
  • Shinji Ito
  • Osamu Kaii
  • Yoshine Mori
  • Kazuhiro Shimamoto
  • Hiromichi Fukushima
  • Kojiro Suzuki
  • Hiroyasu Umakoshi
  • Mitsuo Ohashi
  • Fumio Kawaguchi
  • Shinji Naganawa
Original Article
  • 88 Downloads

Abstract

Purpose

To determine whether functional near-infrared spectroscopy (fNIRS) allows monitoring fatigue in radiologists during prolonged image interpretation.

Materials and methods

Nine radiologists participated as subjects in the present study and continuously interpreted medical images and generated reports for cases for more than 4 h under real clinical work conditions. We measured changes in oxygenated hemoglobin concentrations [oxy-Hb] in the prefrontal cortex using 16-channel fNIRS (OEG16ME, Spectratech) every hour during the Stroop task to evaluate fatigue of radiologists and recorded fatigue scale (FS) as a behavior data.

Results

Two subjects showed a subjective feeling of fatigue and an apparent decrease in brain activity after 4 h, so the experiment was completed in 4 h. The remaining seven subjects continued the experiment up to 5 h. FS decreased with time, and a significant reduction was observed between before and the end of image interpretation. Seven out of nine subjects showed a minimum [oxy-Hb] change at the end of prolonged image interpretation. The mean change of [oxy-Hb] at the end of all nine subjects was significantly less than the maximum during image interpretation.

Conclusion

fNIRS using the change of [oxy-Hb] may be useful for monitoring fatigue in radiologists during image interpretation.

Keywords

Radiologists Workload Fatigue Spectroscopy Near-infrared 

Notes

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical statement

This study was approved by the Institutional Review Board of Komaki City Hospital and Nagoya Jhohoku Radiology Clinic.

Informed consent

A comprehensive explanation of the experiment was given to subjects before this study and written informed consent was obtained. Authors from multi-institute participant in this study, and each author individually and significantly contributed to the research and the manuscript.

References

  1. 1.
    Bhargavan M, Kaye AH, Forman HP, Sunshine JH. Workload of radiologists in United States in 2006–2007 and trends since 1991–1992. Radiology. 2009.  https://doi.org/10.1148/radiol.2522081895.Google Scholar
  2. 2.
    Nakajima Y, Yamada K, Imamura K, Kobayashi K. Radiologist supply and workload: international comparison—Working Group of Japanese College of Radiology. Radiat Med. 2008.  https://doi.org/10.1007/s11604-008-0259-2.Google Scholar
  3. 3.
    Krupinski EA, Berbaum KS, Caldwell RT, Schartz KM, Kim J. Long radiology workdays reduce detection and accommodation accuracy. J Am College Radiol. 2010.  https://doi.org/10.1016/j.jacr.2010.03.004.Google Scholar
  4. 4.
    Waite S, Scott J, Gale B, Fuchs T, Kolla S, Reede D. Interpretive error in radiology. AJR Am J Roentgenol. 2017.  https://doi.org/10.2214/ajr.16.16963.Google Scholar
  5. 5.
    Stec N, Arje D, Moody AR, Krupinski EA, Tyrrell PN. A systematic review of fatigue in radiology: is it a problem? AJR Am J Roentgenol. 2018.  https://doi.org/10.2214/ajr.17.18613.Google Scholar
  6. 6.
    Pattyn N, Neyt X, Henderickx D, Soetens E. Psychophysiological investigation of vigilance decrement: boredom or cognitive fatigue? Physiol Behav. 2008.  https://doi.org/10.1016/j.physbeh.2007.09.016.Google Scholar
  7. 7.
    Langner R, Eickhoff SB. Sustaining attention to simple tasks: a meta-analytic review of the neural mechanisms of vigilant attention. Psychol Bull. 2013.  https://doi.org/10.1037/a0030694.Google Scholar
  8. 8.
    Gui D, Xu S, Zhu S, Fang Z, Spaeth AM, Xin Y, et al. Resting spontaneous activity in the default mode network predicts performance decline during prolonged attention workload. Neuroimage. 2015.  https://doi.org/10.1016/j.neuroimage.2015.07.030.Google Scholar
  9. 9.
    Taylor-Phillips S, Elze MC, Krupinski EA, Dennick K, Gale AG, Clarke A, et al. Retrospective review of the drop in observer detection performance over time in lesion-enriched experimental studies. J Digit Imaging. 2015.  https://doi.org/10.1007/s10278-014-9717-9.Google Scholar
  10. 10.
    Suda M, Fukuda M, Sato T, Iwata S, Song M, Kameyama M, et al. Subjective feeling of psychological fatigue is related to decreased reactivity in ventrolateral prefrontal cortex. Brain Res. 2009.  https://doi.org/10.1016/j.brainres.2008.11.077.Google Scholar
  11. 11.
    Ishii A, Tanaka M, Shigihara Y, Kanai E, Funakura M, Watanabe Y. Neural effects of prolonged mental fatigue: a magnetoencephalography study. Brain Res. 2013.  https://doi.org/10.1016/j.brainres.2013.07.022.Google Scholar
  12. 12.
    Sun Y, Lim J, Kwok K, Bezerianos A. Functional cortical connectivity analysis of mental fatigue unmasks hemispheric asymmetry and changes in small-world networks. Brain Cogn. 2014.  https://doi.org/10.1016/j.bandc.2013.12.011.Google Scholar
  13. 13.
    Tanaka M, Ishii A, Watanabe Y. Neural effects of mental fatigue caused by continuous attention load: a magnetoencephalography study. Brain Res. 2014.  https://doi.org/10.1016/j.brainres.2014.03.009.Google Scholar
  14. 14.
    Ishii A, Tanaka M, Watanabe Y. Neural mechanisms to predict subjective level of fatigue in the future: a magnetoencephalography study. Sci Rep. 2016.  https://doi.org/10.1038/srep25097.Google Scholar
  15. 15.
    Lim J, Wu WC, Wang J, Detre JA, Dinges DF, Rao H. Imaging brain fatigue from sustained mental workload: an ASL perfusion study of the time-on-task effect. Neuroimage. 2010.  https://doi.org/10.1016/j.neuroimage.2009.11.020.Google Scholar
  16. 16.
    Villringer A, Dirnagl U. Coupling of brain activity and cerebral blood flow: basis of functional neuroimaging. Cerebrovasc Brain Metab Rev. 1995;7(3):240–76.Google Scholar
  17. 17.
    Gratton G, Goodman-Wood MR, Fabiani M. Comparison of neuronal and hemodynamic measures of the brain response to visual stimulation: an optical imaging study. Hum Brain Mapp. 2001;13(1):13–25.CrossRefGoogle Scholar
  18. 18.
    Schroeter ML, Zysset S, Kupka T, Kruggel F, Yves von Cramon D. Near-infrared spectroscopy can detect brain activity during a color-word matching Stroop task in an event-related design. Hum Brain Mapp. 2002. https://doi.org/10.1002/hbm.10052.
  19. 19.
    Rooks CR, Thom NJ, McCully KK, Dishman RK. Effects of incremental exercise on cerebral oxygenation measured by near-infrared spectroscopy: a systematic review. Prog Neurobiol. 2010.  https://doi.org/10.1016/j.pneurobio.2010.06.002.Google Scholar
  20. 20.
    Matsubara T, Matsuo K, Nakashima M, Nakano M, Harada K, Watanuki T, et al. Prefrontal activation in response to emotional words in patients with bipolar disorder and major depressive disorder. Neuroimage. 2014.  https://doi.org/10.1016/j.neuroimage.2013.04.098.Google Scholar
  21. 21.
    Kopton IM, Kenning P. Near-infrared spectroscopy (NIRS) as a new tool for neuroeconomic research. Front Hum Neurosci. 2014.  https://doi.org/10.3389/fnhum.2014.00549.Google Scholar
  22. 22.
    Kasai K, Fukuda M, Yahata N, Morita K, Fujii N. The future of real-world neuroscience: imaging techniques to assess active brains in social environments. Neurosci Res. 2015.  https://doi.org/10.1016/j.neures.2014.11.007.Google Scholar
  23. 23.
    Yasumura A, Kokubo N, Yamamoto H, Yasumura Y, Nakagawa E, Kaga M, et al. Neurobehavioral and hemodynamic evaluation of Stroop and reverse Stroop interference in children with attention-deficit/hyperactivity disorder. Brain Develop. 2014.  https://doi.org/10.1016/j.braindev.2013.01.005.Google Scholar
  24. 24.
    Hoshi Y, Kobayashi N, Tamura M. Interpretation of near-infrared spectroscopy signals: a study with a newly developed perfused rat brain model. J Appl Physiol (Bethesda, Md: 1985). 2001. https://doi.org/10.1152/jappl.2001.90.5.1657.
  25. 25.
    Ozawa S, Matsuda G, Hiraki K. Negative emotion modulates prefrontal cortex activity during a working memory task: a NIRS study. Front Hum Neurosci. 2014.  https://doi.org/10.3389/fnhum.2014.00046.Google Scholar
  26. 26.
    Ochi G, Yamada Y, Hyodo K, Suwabe K, Fukuie T, Byun K, et al. Neural basis for reduced executive performance with hypoxic exercise. Neuroimage. 2018.  https://doi.org/10.1016/j.neuroimage.2017.12.091.Google Scholar
  27. 27.
    Watanabe E, Maki A, Kawaguchi F, Yamashita Y, Koizumi H, Mayanagi Y. Noninvasive cerebral blood volume measurement during seizures using multichannel near infrared spectroscopic topography. J Biomed Opt. 2000.  https://doi.org/10.1117/1.429998.Google Scholar
  28. 28.
    Takizawa R, Fukuda M, Kawasaki S, Kasai K, Mimura M, Pu S, et al. Neuroimaging-aided differential diagnosis of the depressive state. Neuroimage. 2014.  https://doi.org/10.1016/j.neuroimage.2013.05.126.Google Scholar
  29. 29.
    Yanagisawa H, Dan I, Tsuzuki D, Kato M, Okamoto M, Kyutoku Y, et al. Acute moderate exercise elicits increased dorsolateral prefrontal activation and improves cognitive performance with Stroop test. Neuroimage. 2010.  https://doi.org/10.1016/j.neuroimage.2009.12.023.Google Scholar
  30. 30.
    Kujach S, Byun K, Hyodo K, Suwabe K, Fukuie T, Laskowski R, et al. A transferable high-intensity intermittent exercise improves executive performance in association with dorsolateral prefrontal activation in young adults. Neuroimage. 2018.  https://doi.org/10.1016/j.neuroimage.2017.12.003.Google Scholar
  31. 31.
    Parasuraman R, de Visser E, Clarke E, McGarry WR, Hussey E, Shaw T, et al. Detecting threat-related intentional actions of others: effects of image quality, response mode, and target cuing on vigilance. J Exp Psychol Appl. 2009.  https://doi.org/10.1037/a0017132.Google Scholar
  32. 32.
    Jackson C. The Chalder fatigue scale (CFQ 11). Occup Med (Oxford, England). 2015.  https://doi.org/10.1093/occmed/kqu168.Google Scholar
  33. 33.
    Pestilli F, Carrasco M, Heeger DJ, Gardner JL. Attentional enhancement via selection and pooling of early sensory responses in human visual cortex. Neuron. 2011.  https://doi.org/10.1016/j.neuron.2011.09.025.Google Scholar
  34. 34.
    Poghosyan V, Ioannides AA. Attention modulates earliest responses in the primary auditory and visual cortices. Neuron. 2008.  https://doi.org/10.1016/j.neuron.2008.04.013.Google Scholar

Copyright information

© Japan Radiological Society 2019

Authors and Affiliations

  • Takashi Nihashi
    • 1
    Email author
  • Takeo Ishigaki
    • 2
  • Hiroko Satake
    • 3
  • Shinji Ito
    • 3
  • Osamu Kaii
    • 2
  • Yoshine Mori
    • 4
  • Kazuhiro Shimamoto
    • 5
  • Hiromichi Fukushima
    • 2
  • Kojiro Suzuki
    • 6
  • Hiroyasu Umakoshi
    • 1
  • Mitsuo Ohashi
    • 7
  • Fumio Kawaguchi
    • 7
  • Shinji Naganawa
    • 3
  1. 1.Department of RadiologyKomaki City HospitalKomakiJapan
  2. 2.Nagoya Jhohoku Radiology ClinicNagoyaJapan
  3. 3.Department of RadiologyNagoya University Graduate School of MedicineNagoyaJapan
  4. 4.Department of RadiologyAnjo Kosei HospitalAnjoJapan
  5. 5.Department of Radiological and Medical Laboratory SciencesNagoya University Graduate School of MedicineNagoyaJapan
  6. 6.Department of RadiologyAichi Medical UniversityNagakuteJapan
  7. 7.Spectratech, Inc.TokyoJapan

Personalised recommendations