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

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

In addition, this method transforms raw fixation data into structured features that can be effectively used by machine learning models. Overall algorithm: the steps required to generate proposed features from the raw dataset and build the proposed machine learning model. We collected the gaze fixation data from radiologists while they were reading the x-rays. These data were then segmented into fixed temporal groups before discretizing them to convert them into final encoded vectors.

Stanford Martinez, Carolina Ramirez-Tamayo, Syed Hasib Akhter Faruqui, Kal Clark, Adel Alaeddini, Nicholas Czarnek, Aarushi Aggarwal, Sahra Emamzadeh, Jeffrey R Mock, Edward J Golob

JMIR Form Res 2025;9:e53928