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
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.
References
Brooks-Russell A, Brown T, Friedman K, Wrobel J, Schwarz J, Dooley G, et al. (2021). Simulated driving performance among daily and occasional cannabis users. Accident Analysis and Prevention, 160: 106326. https://doi.org/10.1016/j.aap.2021.106326
Brown B, Adams AJ, Haegerstrom-Portnoy G, Jones RT, Flom MC (1977). Pupil size after use of marijuana and alcohol. American Journal of Ophthalmology, 83(3): 350–354. https://doi.org/10.1016/0002-9394(77)90732-2
Brubacher JR, Chan H, Erdelyi S, Macdonald S, Asbridge M, Mann RE, et al. (2019). Cannabis use as a risk factor for causing motor vehicle crashes: A prospective study. Addiction, 114(9): 1616–1626. https://doi.org/10.1111/add.14663
Campobasso CP, De Micco F, Corbi G, Keller T, Hartung B, Daldrup T, et al. (2020). Pupillary effects in habitual cannabis consumers quantified with pupillography. Forensic Science International, 317: 110559. https://doi.org/10.1016/j.forsciint.2020.110559
Colizzi M, Bhattacharyya S (2018). Cannabis use and the development of tolerance: A systematic review of human evidence. Neuroscience and Biobehavioral Reviews, 93: 1–25. https://doi.org/10.1016/j.neubiorev.2018.07.014
Ding L, Goshtasby A (2001). On the canny edge detector. Pattern Recognition, 34(3): 721–725. https://doi.org/10.1016/S0031-3203(00)00023-6
Downey LA, King R, Papafotiou K, Swann P, Ogden E, Boorman M, et al. (2012). Detecting impairment associated with cannabis with and without alcohol on the standardized field sobriety tests. Psychopharmacology, 224(4): 581–589. https://doi.org/10.1007/s00213-012-2787-9
Fant RV, Heishman SJ, Bunker EB, Pickworth WB (1998). Acute and residual effects of marijuana in humans. Pharmacology, Biochemistry and Behavior, 60(4): 777–784. https://doi.org/10.1016/S0091-3057(97)00386-9
Fasiolo M, Wood SN, Zaffran M, Nedellec R, Goude Y (2021a). Fast calibrated additive quantile regression. Journal of the American Statistical Association, 116(535): 1402–1412. https://doi.org/10.1080/01621459.2020.1725521
Fasiolo M, Wood SN, Zaffran M, Nedellec R, Goude Y (2021b). qgam: Bayesian nonparametric quantile regression modeling in R. Journal of Statistical Software, 100(9): 1–31. https://doi.org/10.18637/jss.v100.i09
Halekoh U, Højsgaard S, Yan J (2006). The r package geepack for generalized estimating equations. Journal of Statistical Software, 15(2): 1–11. https://doi.org/10.18637/jss.v015.i02
Haralick RM, Sternberg SR, Zhuang X (1987). Image analysis using mathematical morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence, 4: 532–550. https://doi.org/10.1109/TPAMI.1987.4767941
Hartman RL, Richman JE, Hayes CE, Huestis MA (2016). Drug recognition expert (dre) examination characteristics of cannabis impairment. Accident Analysis and Prevention, 92: 219–229. https://doi.org/10.1016/j.aap.2016.04.012
IACP (2022). The 12-step dre protocol. https://www.theiacp.org/12-step-process. Accessed: 2022-05-30.
Lamprecht MR, Sabatini DM, Carpenter AE (2007). Cellprofiler: Free, versatile software for automated biological image analysis. BioTechniques, 42(1): 71–75. https://doi.org/10.2144/000112257
Larson MD, Behrends M (2015). Portable infrared pupillometry: A review. Anesthesia and Analgesia, 120(6): 1242–1253. https://doi.org/10.1213/ANE.0000000000000314
Lira MC, Heeren TC, Buczek M, Blanchette JG, Smart R, Pacula RL, et al. (2021). Trends in cannabis involvement and risk of alcohol involvement in motor vehicle crash fatalities in the united states, 2000–2018. American Journal of Public Health, 111(11): 1976–1985. https://doi.org/10.2105/AJPH.2021.306466
McKay RE, Neice AE, Larson MD (2018). Pupillary unrest in ambient light and prediction of opioid responsiveness: Case report on its utility in the management of 2 patients with challenging acute pain conditions. A&A Practice, 10(10): 279–282. https://doi.org/10.1213/XAA.0000000000000710
Merzouki A, Mesa JM, Louktibi A, Kadiri M, Urbano G (2008). Assessing changes in pupillary size in rifian smokers of kif (cannabis sativa l.). Journal of forensic and Legal Medicine, 15(5): 335–338. https://doi.org/10.1016/j.jflm.2007.08.001
Newmeyer MN, Swortwood MJ, Taylor ME, Abulseoud OA, Woodward TH, Huestis MA (2017). Evaluation of divided attention psychophysical task performance and effects on pupil sizes following smoked, vaporized and oral cannabis administration. Journal of Applied Toxicology, 37(8): 922–932. https://doi.org/10.1002/jat.3440
Ortiz-Peregrina S, Ortiz C, Castro-Torres JJ, Jiménez JR, Anera RG (2020). Effects of smoking cannabis on visual function and driving performance. a driving-simulator based study. International Journal of Environmental Research and Public Health, 17(23): 9033. https://doi.org/10.3390/ijerph17239033
Otsu N (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics, 9(1): 62–66. https://doi.org/10.1109/TSMC.1979.4310076
Papafotiou K, Carter JD, Stough C (2005). An evaluation of the sensitivity of the standardised field sobriety tests (sfsts) to detect impairment due to marijuana intoxication. Psychopharmacology, 180(1): 107–114. https://doi.org/10.1007/s00213-004-2119-9
Shahidi Zandi A, Comeau FJ, Mann RE, Di Ciano P, Arslan EP, Murphy T, et al. (2021). Preliminary eye-tracking data as a nonintrusive marker for blood δ-9-tetrahydrocannabinol concentration and drugged driving. Cannabis and Cannabinoid Research, 6(6): 537–547. https://doi.org/10.1089/can.2020.0141
Stark M, Englehart K, Sexton B, Tunbridge R, Jackson P (2003). Use of a pupillometer to assess change in pupillary size post-cannabis. Journal of Clinical Forensic Medicine, 10(1): 9–11. https://doi.org/10.1016/S1353-1131(02)00162-1
van der Walt S, Schönberger JL, Nunez-Iglesias J, Boulogne F, Warner JD, Yager N, et al. (2014). scikit-image: Image processing in Python. PeerJ, 2: e453. https://doi.org/10.7717/peerj.453