Discrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study
The effectiveness of the proposed spatiotemporal discretization approach, validated across diverse datasets and various classification metrics, underscores its potential for objective evaluation, informing targeted interventions and training strategies in radiology. This research advances reliable assessment tools, addressing challenges in perception-related errors to enhance patient care outcomes.
Authors
- Sahra Emamzadehfard (ORCID: https://orcid.org/0000-0002-1053-1441)
- Syed Hasib Akhter Faruqui (ORCID: https://orcid.org/0000-0002-5073-8690)
- Nicholas Czarnek (ORCID: https://orcid.org/0000-0002-7889-0184)
- Kal Clark (ORCID: https://orcid.org/0000-0002-2511-7577)
- Edward J. Golob (ORCID: https://orcid.org/0000-0002-1560-9076)
- Carolina Ramirez-Tamayo (ORCID: https://orcid.org/0000-0002-4413-1366)
- Jeffrey R. Mock (ORCID: https://orcid.org/0000-0003-0446-6687)
- Adel Alaeddini (ORCID: https://orcid.org/0000-0003-4451-3150)
- Stanford Martinez (ORCID: https://orcid.org/0000-0001-7735-6572)
- Aarushi Aggarwal (ORCID: https://orcid.org/0000-0001-6557-680X)
Institutions
- The University of Texas at San Antonio Health Science Center (US)
- Anaheim University (US)
- Southern Methodist University (US)
- Sam Houston State University (US)
- The University of Texas at San Antonio (US)
Publication Details
- Journal
- JMIR Formative Research
- Published
- 2025-01-22
- DOI
- https://doi.org/10.2196/53928
- Primary Topic
- Radiology practices and education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- University of Texas at San Antonio
- University of Texas Health Science Center at San Antonio