Medical infrared imaging and orthostatic analysis to determine lameness in the pelvic limbs of dogs
Erika Fernanda V. Garcia, Catherine A. Loughin, Dominic J. Marino and 7 more
Open Veterinary Journal · 2017
doi:10.4314/ovj.v7i4.10 (opens in a new tab)
We email you when a new notice or a question about this paper appears.
Abstract
Subtle lameness makes it difficult to ascertain which is the affected limb. A study was conducted to investigate a change in the thermal pattern and temperature of the thermal image of the paw print in a lame pelvic limb compared to a non-lame pelvic limb of dogs confirmed by orthostatic analysis. Fourteen client owned dogs with a unilateral pelvic limb lameness and 14 healthy employee dogs were examined and the pelvic limbs radiographed. Thermal images of the paw print were taken after each dog was kept in a static position on a foam mat for 30 seconds. Average temperatures and thermographic patterns were analyzed. Analysis was performed in a static position. The asymmetry index for each stance variable and optimal cutoff point for the peak vertical force and thermal image temperatures were calculated. Image pattern analysis revealed 88% success in differentiating the lame group, and 100% in identifying the same thermal pattern in the healthy group. The mean of the peak vertical force revealed a 10.0% difference between the left and right pelvic limb in healthy dogs and a 72.4% between the lame and non-lame limb in the lame dog group. Asymmetry index analysis revealed 5% in the healthy group and 36.2% in the lame group. The optimal cutoff point for the peak vertical force to determine lameness was 41.77% (AUC = 0.93) and for MII 0.943% (AUC = 0.72). The results of this study highlight the change in the thermal pattern of the paw print in the lame pelvic limb compared to a non-lame pelvic limb in the lame group and the healthy group. Medical infrared imaging of the paw prints can be utilized to screen for the lame limb in dogs.
Abstract licence: CC BY-NC
Reads
Read by people on this page
None yet.
Checked or read by AI through naulon
None yet.
A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.
Are you an author?
Is this your paper?
Claim it to add your note and be paid when agents cite it. Free.