A randomized controlled trial evaluating the use of an intelligent, fully automated 2D imaging system to detect lame cows and control lameness.

A fully automated 2-dimensional imaging system that uses machine learning to produce real-time mobility scores has been developed and previously externally validated using human mobility scores and foot lesion records as ground truth. This randomized controlled trial evaluated the effect of integrating this system into an early detection and prompt treatment lameness management protocol on a large dairy farm in the UK A total of 419 multiparous cows ≤30 d-in-milk (DIM) were randomly allocated to either a control (CON) group (n = 208), managed under the farm's standard protocol or an intervention (AUTO) group (n = 211). The CON protocol consisted of routine trims at early (approximately 80 DIM) and mid-lactation (approximately 180 DIM), and examination of cows identified as lame by farm staff. In addition to the CON protocol, weekly automated scores were obtained for AUTO cows. Any AUTO cow exceeding the pre-defined threshold (≥50, on a scale of 0 to 100) or those with a ≥20 points increase in absolute scores during the last 2 weeks were scheduled for examination and treatment. Lameness scores from monthly human mobility scoring sessions were compared between groups using Fisher's exact tests or Chi-squared tests, with relative risks (RR) and odds ratios (OR) calculated. Trimming events, foot lesion prevalence and severity, and number of hoof block applications required were compared between groups using Poisson regressions and Chi-squared tests. The effect on weekly average milk yield was assessed with linear mixed effects models. Culling hazard was assessed using Cox proportional hazards regression (COXPHR). Time to 1st artificial insemination (AI) and time to conception by 150 DIM were assessed with COXPHR, whereas odds for pregnancy to the 1st AI were assessed with binary logistic regression. Cows in the AUTO group had a lower proportion of cows that developed severe lameness (2.0% vs. 7.9%, RR = 0.25; 95% CI: 0.09-0.66; OR = 0.24; 95% CI: 0.08-0.69) and chronic lameness (3.9% vs. 9.8%, RR = 0.40; 95% CI: 0.18-0.91; OR = 0.38; 95% CI: 0.16-0.88) compared with CON cows. Cows in the AUTO group underwent 2.67 trimming events per cow compared with 1.83 in the CON group during the study period (as estimated marginal means). At the 180 DIM routine trim, the AUTO group had a higher proportion of lesion-free cows (22.4% vs. 12.0%) and a lower proportion of cows with moderate lesions (16.0% vs. 25.3%). The small subset of second-parity cows in the AUTO group had higher odds of conception to 1st AI (OR = 7.6; 95% CI: 1.6-36.7) and a greater hazard of conception by 150 DIM (HR = 3.1; 95% CI: 1.3-7.3) compared with their CON counterparts. No differences were detected for weekly average milk yield or culling risk. Our findings indicate that automated mobility monitoring can improve lameness control programs by reducing severe and chronic lameness and improving mid-lactation foot health in cows.