EquiSim: An Open-Source Articulatable Statistical Model of the Equine Distal Limb

Abstract

Most digital models of the equine distal limb that are available in the community are static and/or subject specific; hence, they have limited applications in veterinary research. In this paper, we present an articulatable model of the entire equine distal limb based on statistical shape modeling. The model describes the inter-subject variability in bone geometry while maintaining proper jointspace distances to support model articulation toward different poses. Shape variation modes are explained in terms of common biometrics in order to ease model interpretation from a veterinary point of view. The model is publicly available through a graphical user interface (https://github.com/jvhoutte/equisim) in order to facilitate future digitalization in veterinary research, such as computer-aided designs, three-dimensional printing of bone implants, bone fracture risk assessment through finite element methods, and data registration and segmentation problems for clinical practices.

Inertial Sensor Technologies—Their Role in Equine Gait Analysis, a Review

“…In human musculoskeletal research, a combination of IMUs, OMC, machine learning gait prediction, and finite element analysis has been used for injury monitoring, treatment, and rehabilitation [ 97 , 98 ]. These techniques can also be extrapolated to animal studies, with research in this direction having recently been conducted [ 99 ]. Data collected by IMU systems are valuable for the evaluation of the horse–rider interaction during dressage riding, training of horses, or coaching.…”

Anatomical variations of the equine femur and tibia using statistical shape modeling

“…One common method to characterize complex anatomical geometry is statistical shape modeling (SSM), which has been employed to quantify the anatomical morphological variability in various joints in humans, such as carpometacarpal joint and patellofemoral joint [9,10]. In horses, SSM has been used to evaluate the equine third metacarpal and also an articulating model of the equine distal limb [11,12]. SSM creates a point-to-point-based correspondence within samples and extracts the most prominent modes of shape variation within the population, named principal components or modes, using principal component analysis (PCA), which mathematically reduces the shape dimensionality of the region of interest [13,14].…”

“…Several studies utilized SSM to provide better understanding regarding the correlation between joint morphology and joint function, which enabled prognosis and determination of progression in humans for joint OA [15,16]. With advanced imaging technologies such as computed tomography (CT) becoming more common in veterinary practice, equine SSM studies are now being published and in future could be used to guide therapies and evaluate risks based on morphologies [11,12].…”