Background and Aims: Anxiety-like behavior is widely studied in rodent models, with the
Open Field Test (OFT) being a standard method for evaluation. Typically, reduced center zone exploration and increased thigmotaxis indicate heightened anxiety. Manual scoring is time-consuming and prone to bias, while commercial automated tools are often expensive. This study introduces RASAD, a lightweight, low-cost software developed to automatically assess two anxiety-related parameters from OFT videos: center zone time and total distance traveled. Methods: Eighteen adult male mice (۸–۱۰ weeks, ۲۵–۳۰ g) were divided into three groups (n = ۶): (۱) Control, (۲) Citicoline-treated (۱۰۰ mg/kg, i.p., daily for ۲۱ days), and (۳) Ketamine-treated (۱۰ mg/kg, i.p., single dose, ۳۰ min before test). Each mouse was tested in a ۵۰ × ۵۰ cm open field for ۵ minutes. Behavior was recorded using an overhead camera (۴۸۰×۶۴۰ px, ۱۵ fps), resulting in ۱۸ videos. Using
Python and OpenCV, RASAD tracked mouse position, calculated time spent in the ۲۵ × ۲۵ cm center zone, and measured total distance. Manual scoring by trained observers validated the automated data. Results: Compared to Ethovision, RASAD demonstrated approximately ۷۵% agreement with manual scoring. In the control group, mice traveled an average of ۱۱۰۰ ± ۱۵۰ cm and spent ۵۰ ± ۱۰ seconds in the center zone. Citicoline-treated mice exhibited slightly increased locomotion (۱۱۵۰ ± ۱۳۰ cm) and center zone time (۷۰ ± ۱۲ seconds). In contrast, Ketamine-treated mice spent significantly more time in the center (۱۱۰ ± ۱۸ seconds) with a reduced total distance traveled (۹۸۰ ± ۱۱۰ cm), indicating a strong anxiolytic effect with minimal impact on overall activity. Conclusion: RASAD provides an efficient and affordable solution for OFT analysis, especially for small labs. With further development, incorporating machine learning algorithms could enhance its accuracy and expand its capabilities, making it a powerful, low-cost alternative to expensive commercial tools like Ethovision.
