Pain is a complex neuro-psychosocial experience that is internal and private making it difficult to assess in both humans and animals. In research approximately 95% of animal models use rodents, with rats being among the most common for pain studies [3]. However, traditional assessments of the pain response struggle to demonstrate that the behaviors are a direct measurement of pain. The rat grimace scale (RGS) was developed based on facial action coding systems (FACS) which have known utility in non-verbal humans [6, 9]. The RGS measures facial action units of orbital tightening, ear changes, nose flattening, and whisker changes in an attempt to quantify the pain behaviors of the rat. These action units are measured on frontal images of rats with their face in clear view on a scale of 0-2, then summed together. The total score is then averaged to find a final value for RGS between 0-2. Currently, the software program Rodent Face Finder® can extract frontal face images. However, the RGS scores are still manually recorded which is a labor-intensive process, requiring hours of training. Furthermore, the scoring can be subjective, with differences existing between researchers and lab groups. The primary aim of this study is to develop an automated system that can detect action unit regions and generate a RGS score for each image. To accomplish this objective, a YOLOv5 object detector and Vision Transformers (ViT) for classification were trained on a dataset of frontal-facing images extracted using Rodent Face Finder®. Subsequently, the model was then validated using a RGS test for blast traumatic brain injury (bTBI). The validation dataset consisted of 40 control images of uninjured rats, 40 images from the bTBI study on the day of injury, and 40 images 1-month post-injury. All 120 images in the validation set were then manually graded for RGS and tested using the automated RGS system. The results indicated that the automated RGS system accurately and efficiently graded the images with minimal variation in results compared to human graders in just 1/14th of the time. This system provides a fast and reliable method to extract meaningful information of rats' internal pain state. Furthermore, the study presents an avenue for future research into real-time pain monitoring. / Master of Science / Pain is a difficult experience to measure, both in humans and animals. It can be a subjective experience that is largely based on individual perception and interpretation. Furthermore, in animals, pain is even more challenging to assess because they cannot communicate their experience through language. Nonetheless, animal research plays an important role in understanding and treating the underlying mechanisms of pain. In animal research, rats are commonly used to study pain. However, traditional methods of assessing pain behaviors are not meant to observe the pain experience, but instead analyze a response to an external stimulus. The rat grimace scale (RGS) was developed as a direct measurement of the pain experience by analyzing the facial features. Currently, RGS scores are manually recorded by trained researchers which is time-consuming and can be subjective. This study aimed to develop an automated system to identify pain related facial expressions and generate a RGS score for frontal-images of rats. The system was trained using a dataset of frontal-facing rat images with varying levels of RGS scores and validated using images of rats from a traumatic brain injury study. The results showed that the automated RGS system accurately identified RGS pain level differences between recently injured rats, uninjured rats, and rats which were allowed to recover for 1-month. Furthermore, the system provided a fast and reliable method for measuring rat pain behavior when compared to manual grading. With this system, researchers will be able to efficiently perform RGS test. Additionally, this study presents an opportunity for future automation of other grimace scales as well as research into real-time pain monitoring.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/115478 |
| Date | 21 June 2023 |
| Creators | Arnold, Brendan Elliot |
| Contributors | Department of Biomedical Engineering and Mechanics, VandeVord, Pamela J., Vijayan, Sujith, Thomas, Christopher Lee |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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