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Characterizing Visual Neurosurgical Expertise In Brain Mri Visualization Using Eye-tracking And 3d Fractal Dimension Analysis.

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Characterizing Visual Neurosurgical Expertise In Brain Mri Visualization Using Eye-tracking And 3d Fractal Dimension Analysis.

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Characterizing Visual Neurosurgical Expertise in Brain MRI Visualization Using Eye-Tracking and 3D Fractal Dimension

Poonam Kumari¹, Ghasem Azemi¹, Carlo Russo¹

¹Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, 75 Talavera Road, Sydney, NSW 2109, Australia.

Journal of Eye Movement Research

|June 25, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Neurosurgeons

Keywords:

MRI eye gaze eye-tracking fixation duration fractal dimension neurosurgery scanpath visual attention visual expertise visual stimuli

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Area of Science:

Neuroimaging and neuroscience research.
Medical image analysis and interpretation.
Human visual perception and expertise.

Background:

Eye-tracking studies visual behavior in medical imaging but lacks neurosurgeon representation.
Understanding neurosurgeon visual expertise is crucial for training and computational model development.
Expert visual search strategies in neurosurgery remain undercharacterized.

Purpose of the Study:

To characterize neurosurgery-specific visual expertise using eye-tracking.
To investigate how fixation duration and image complexity relate to expertise.
To develop computational models for neurosurgical image interpretation.

Main Methods:

Compared gaze behavior of naïve observers, registrars, and consultant neurosurgeons.
Analyzed fixation duration and 3D fractal dimension (3DFD) of brain MR images.
Utilized linear mixed-effects modeling and Random Forest classification.

Main Results:

Experts showed longer fixation durations on pathological images.
Expert visual search strategies were dependent on pathology type.
Combined fixation duration and 3DFD improved classification accuracy (>93%) for pathology identification.

Conclusions:

Identified neurosurgery-specific markers of visual expertise.
Combined behavioral and image features can model expert strategies.
Findings support the development of advanced training and computational tools for neurosurgical image interpretation.