A Video Segmentation Pipeline for Assessing Changes in Pupil Response to Light After Cannabis Consumption
Volume 22, Issue 1 (2024), pp. 138–151
Pub. online: 20 June 2023
Type: Data Science In Action
Open Access
Open Access
Received
10 February 2023
10 February 2023
Accepted
2 May 2023
2 May 2023
Published
20 June 2023
20 June 2023
Abstract
Due to long-standing federal restrictions on cannabis-related research, the implications of cannabis legalization on traffic and occupational safety are understudied. Accordingly, there is a need for objective and validated measures of acute cannabis impairment that may be applied in public safety and occupational settings. Pupillary response to light may offer an avenue for detection that outperforms typical sobriety tests and tetrahydrocannabinol concentrations. We developed a video processing and analysis pipeline that extracts pupil sizes during a light stimulus test administered with goggles utilizing infrared videography. The analysis compared pupil size trajectories in response to a light for those with occasional, daily, and no cannabis use before and after smoking. Pupils were segmented using a combination of image pre-processing techniques and segmentation algorithms which were validated using manually segmented data and found to achieve 99% precision and 94% F-score. Features extracted from the pupil size trajectories captured pupil constriction and rebound dilation and were analyzed using generalized estimating equations. We find that acute cannabis use results in less pupil constriction and slower pupil rebound dilation in the light stimulus test.
Supplementary material
Supplementary MaterialThe supplementary materials contain a zipped folder with that is also available on GitHub at https://github.com/benjamin643/pupil_segmentation_and_analysis. This folder contains code to reproduce all analyses in this paper, one example video, and data for reproducing all results post pupil segmentation.
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