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  • Author or Editor: Timothy L. Foutz x
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Abstract

OBJECTIVE

To develop a 3-D kinematic model to measure truncal motion in dogs and assess changes in truncal motion in dogs when wearing each of 2 service vests.

ANIMALS

5 adult mixed-breed dogs.

PROCEDURES

27 reflective markers were placed on the pelvis, trunk, and scapula of each dog. Six infrared cameras were placed around a treadmill to track the location of the markers within a calibrated space. Dogs were recorded during walking and trotting on the treadmill. Local and global coordinate systems were established, and a segmental rigid-body model of the trunk was created. Dogs were then recorded while wearing a custom vest and an adjustable vest during walking and trotting on the treadmill. Range of motion of the trunk when dogs were and were not wearing vests was compared by repeated-measures ANOVA.

RESULTS

An anatomic coordinate system was established by use of markers located at T1, T13, and the xiphoid process. Range of motion of the trunk during a gait cycle did not differ significantly regardless of the day of the test for both walking and trotting gaits. Trunk motion of dogs when walking and trotting was significantly reduced when dogs were wearing a vest, compared with trunk motion when not wearing a vest.

CONCLUSIONS AND CLINICAL RELEVANCE

A 3-D kinematic model for measuring truncal rotation was developed. Results indicated measurable differences in the gait of dogs when wearing each of the 2 service vests, compared with the gait when not wearing a vest.

Full access
in American Journal of Veterinary Research

Abstract

Objective—To quantify the 3-D kinematics and collateral ligament strain of stifle joints in cadaveric canine limbs before and after cranial cruciate ligament transection followed by total knee replacement (TKR) involving various tibial plateau angles and spacer thicknesses.

Sample—6 hemi-pelvises collected from clinically normal nonchondrodystrophic dogs (weight range, 25 to 35 kg).

Procedures—Hemi-pelvises were mounted on a modified Oxford knee rig that allowed 6 degrees of freedom of the stifle joint but prevented mechanical movement of the hip and tarsal joints. Kinematics and collateral ligament strain were measured continuously while stifle joints were flexed. Data were again collected after cranial cruciate ligament transection and TKR with combinations of 3 plateau angles (0°, 4°, and 8°) and spacer thicknesses (5, 7, and 9 mm).

Results—Presurgical (ie, normal) stifle joint rotations were comparable to those previously documented for live dogs. After TKR, kinematics recorded for the 8°, 5-mm implant most closely resembled those of unaltered stifle joints. Decreasing the plateau angle and increasing spacer thickness altered stifle joint adduction, internal rotation, and medial translation. Medial collateral ligament strain was minimal in unaltered stifle joints and was unaffected by TKR. Lateral collateral ligament strain decreased with steeper plateau angles but returned to a presurgical level at the flattest plateau angle.

Conclusions and Clinical Relevance—Among the constructs tested, greatest normalization of canine stifle joint kinematics in vitro was achieved with the steepest plateau angle paired with the thinnest spacer. Furthermore, results indicated that strain to the collateral ligaments was not negatively affected by TKR.

Full access
in American Journal of Veterinary Research

Abstract

Objective—To evaluate the accuracy of artificial neural networks (ANNs) for use in predicting subjective diagnostic scores of lameness with variables determined from ground reaction force (GRF) data.

Animals—21 adult mixed-breed dogs.

Procedures—The left cranial cruciate ligament of each dog was transected to induce osteoarthritis of the stifle joint as part of another study. Lameness scores were assigned and GRF data were collected 2 times before and 5 times after ligament transection. Inputs and the output for each ANN were GRF variables and a lameness score, respectively. The ANNs were developed by use of data from 14 dogs and evaluated by use of data for the remaining 7 dogs (ie, dogs not used in model development).

Results—ANN models developed with 2 preferred input variables had an overall accuracy ranging from 96% to 99% for 2 data configurations (data configuration 1 contained patterns or observations for 7 dogs, whereas data configuration 2 contained patterns or observations for 7 other dogs). When additional variables were added to the models, the highest overall accuracy ranged from 97% to 100%.

Conclusions and Clinical Relevance—ANNs provided a method for processing GRF data of dogs to accurately predict subjective diagnostic scores of lameness. Processing of GRF data via ANNs could result in a more precise evaluation of surgical and pharmacological intervention by detecting subtle lameness that could have been missed by visual analysis of GRF curves.

Full access
in American Journal of Veterinary Research