Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study.

Journal: JMIR formative research
PMID: 39842001

Abstract

BACKGROUND: Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists use personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions of these search patterns, which involve the physician verbalizing or annotating the order he or she analyzes the image, can be unreliable due to discrepancies in what is reported versus the actual visual patterns. This discrepancy can interfere with quality improvement interventions and negatively impact patient care.

Authors

  • Stanford Martinez
    Department of Mechanical Engineering, The University of Texas at San Antonio, San Antonio, TX, United States.
  • Carolina Ramirez-Tamayo
    Department of Mechanical Engineering, The University of Texas at San Antonio, San Antonio, TX, United States.
  • Syed Hasib Akhter Faruqui
    Department of Engineering Technology, Sam Houston State University, Huntsville, TX, United States.
  • Kal Clark
    Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States.
  • Adel Alaeddini
    Department of Mechanical Engineering, The University of Texas at San Antonio, San Antonio, Texas, United States.
  • Nicholas Czarnek
  • Aarushi Aggarwal
    Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States.
  • Sahra Emamzadeh
    Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States.
  • Jeffrey R Mock
    Department of Psychology, The University of Texas at San Antonio, San Antonio, TX, United States.
  • Edward J Golob
    Department of Psychology, The University of Texas at San Antonio, San Antonio, TX, United States.

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