Use of validated objective methods of locomotion characteristics and weight distribution for evaluating the efficacy of ketoprofen for alleviating pain in cows with limb pathologies

Technology applications in bovine gait analysis: A scoping review

Quantitative bovine gait analysis using technology has evolved significantly over the last two decades. However, subjective methods of gait assessment using visual locomotion scoring remain the primary on-farm and experimental approach. The objective of this review is to map research trends in quantitative bovine gait analysis and to explore the technologies that have been utilized to measure biomechanical parameters of gait. A scoping literature review was conducted according to PRISMA guidelines. A search algorithm based on PICO framework generated three components-bovine, gait, and technology-to address our objectives. Three online databases were searched for original work published from January 2000 to June 2020. A two-step screening process was then conducted, starting with the review of article titles and abstracts based on inclusion criteria. A remaining 125 articles then underwent a full-text assessment, resulting in 82 final articles. Thematic analysis of research aims resulted in four major themes among the studies: gait/claw biomechanics, lameness detection, intervention/comparison, and system development. Of the 4 themes, lameness detection (55% of studies) was the most common reason for technology use. Within the literature identified three main technologies were used: force and pressure platforms (FPP), vision-based systems (VB), and accelerometers. FPP were the first and most popular technologies to evaluate bovine gait and were used in 58.5% of studies. They include force platforms, pressure mapping systems, and weight distribution platforms. The second most applied technology was VB (34.1% of studies), which predominately consists of video analysis and image processing systems. Accelerometers, another technological method to measure gait characteristics, were used in 14.6% of studies. In sum, the strong demand for automatic lameness detection influenced the path of development for quantitative gait analysis technologies. Among emergent technologies, deep learning and wearable sensors (e.g., accelerometers) appear to be the most promising options. However, although progress has been made, more research is needed to develop more accurate, practical, and user-friendly technologies.

Case Report: Proximal Phalangeal Fracture Management in a European Bison (Bison bonasus)

Fracture of the digits is a well-known orthopedic condition in adult cattle, and mainly the distal phalanx (P3) is involved. To our knowledge, the treatment of fractures of the middle (P2) and proximal (P1) phalanges with orthopedic claw blocks has not yet been described in cattle and other ruminants. This report describes the first case of the successful management of a P1 fracture in an adult European bison. A 5-year-old female European bison (Bison bonasus) presented with severe weight bearing lameness of the left hind limb and a marked soft tissue swelling accentuated over the plantar and lateral aspects of the proximal and middle phalanges, associated with multifocal light bluish discoloration of the skin (hematoma) and increased local temperature. The cow was examined and managed because of a severely comminuted fracture of the lateral P1 of the left hind limb. Application of an orthopedic block on the healthy medial partner claw allowed to reduce the load of the affected digit. Combined with the administration of non-steroidal anti-inflammatory drugs, it supported immobilization and recovery. Radiographic re-evaluations at weeks 6, 9, and 11 after the injury revealed progressive callus formation and fracture consolidation. From week 9 onwards, until the end of treatment, no lameness was observed. The healing process was good, and both clinical and radiographical improvement were evident after immobilizing the affected digit by applying an orthopedic claw block on the healthy partner claw and administering non-steroidal anti-inflammatory drugs.

Assessment of feeding, ruminating and locomotion behaviors in dairy cows around calving – a retrospective clinical study to early detect spontaneous disease appearance

The study aims to verify the usefulness of new intervals-based algorithms for clinical interpretation of animal behavior in dairy cows around calving period. Thirteen activities associated with feeding-ruminating-locomotion-behaviors of 42 adult Holstein-Friesian cows were continuously monitored for the week (wk) -2, wk -1 and wk +1 relative to calving (overall 30’340 min/animal). Soon after, animals were retrospectively assigned to group-S (at least one spontaneous diseases; n = 24) and group-H (healthy; n = 18). The average activities performed by the groups, recorded by RumiWatch® halter and pedometer, were compared at the different weekly intervals. The average activities on the day of clinical diagnosis (dd0), as well as one (dd-1) and two days before (dd-2) were also assessed. Differences of dd0 vs. dd-1 (ΔD1), dd0 vs. wk -1 (ΔD2), and wk +1 vs. wk -1 (Δweeks) were calculated. Variables showing significant differences between the groups were used for a univariate logistic regression, a receiver operating characteristic analysis, and a multivariate logistic regression model. At wk +1 and dd0, eating- and ruminating-time, eating- and ruminate-chews and ruminating boluses were significantly lower in group-S as compared to group-H, while other activity time was higher. For ΔD2 and Δweeks, the differences of eating- and ruminating-time, as well as of eating-and ruminate-chews were significantly lower in group-S as compared to group-H. Concerning the locomotion behaviors, the lying time was significantly higher in group-S vs. group-H at wk +1 and dd-2. The number of strides was significantly lower in group-S compared to group-H at wk +1. The model including eating-chews, ruminate-chews and other activity time reached the highest accuracy in detecting sick cows in wk +1 (area under the curve: 81%; sensitivity: 73.7%; specificity: 82.4%). Some of the new algorithms for the clinical interpretation of cow behaviour as described in this study may contribute to monitoring animals’ health around calving.

A Review: Development of Computer Vision-Based Lameness Detection for Dairy Cows and Discussion of the Practical Applications

The computer vision technique has been rapidly adopted in cow lameness detection research due to its noncontact characteristic and moderate price. This paper attempted to summarize the research progress of computer vision in the detection of lameness. Computer vision lameness detection systems are not popular on farms, and the accuracy and applicability still need to be improved. This paper discusses the problems and development prospects of this technique from three aspects: detection methods, verification methods and application implementation. The paper aims to provide the reader with a summary of the literature and the latest advances in the field of computer vision detection of lameness in dairy cows.