Validation of the DeepLabCut-based Automated Method for the Novel Object Recognition Test in Rats.

BACKGROUND: The novel object recognition (NOR) test is widely used to assess object recognition memory in rodents, but manual scoring is labour-intensive and susceptible to interobserver variability. NEW METHOD: We developed an open-source tool combining DeepLabCut (DLC) with explicit numerical criteria. DLC estimated nose, head, and object coordinates in Sprague-Dawley rats. A Python algorithm classified exploration using grid-searchoptimised distance, angle, and likelihood thresholds. Low-likelihood frames, including those involving object occlusion during climbing, were excluded. RESULTS: On an independent dataset of 18,000 frames, sensitivity and positive predictive value were 97% and 83% for the novel object and 97% and 88% for the familiar object, respectively. Across 24 NOR sessions, automated measurements showed high agreement with the mean scores of two independent blinded observers for novel object exploration time (r = 0.87; ICC(2,1) = 0.86), familiar object exploration time (r = 0.96; ICC(2,1) = 0.95), and the novelty discrimination index (NDI) (r = 0.95; ICC(2,1) = 0.95). Bland-Altman analysis showed no evidence of fixed or proportional bias; the 95% limits of agreement for NDI were -0.09 to 0.09. COMPARISON WITH EXISTING METHODS: The method uses explicitly reported numerical thresholds that can be independently verified and recalibrated. Agreement and systematic bias were evaluated without requiring proprietary analysis software. CONCLUSIONS: The DLC-based method showed strong agreement with manual scoring under the conditions tested and provides a transparent, accessible approach to automated NOR analysis.